Spatial Patterns in US Foreign Direct Investment International Master in Public Finance – 2019/2020 Alessia DE SANTO, Camilla FIORINA Table of contents 1. Introduction ......................................................................................................................................... 3 2. Theoretical background and sources of spatial interdependence. ...................................................... 4 3. Data and methodology ........................................................................................................................ 5 4. Descriptive statistics ............................................................................................................................ 8 4.1 Standard descriptive statistics ....................................................................................................... 8 4.2 Spatial descriptive statistics ......................................................................................................... 12 5. Choice of the econometric model and results: full sample ............................................................... 15 5.1 The choice of the spatial model ................................................................................................... 16 5.2 Results .......................................................................................................................................... 19 5.3 Robustness check: alternative weight matrix .............................................................................. 21 6. Choice of the econometric model and results: Europe and Central Asia .......................................... 22 7. Conclusions and limitations ............................................................................................................... 24 Appendix A ............................................................................................................................................. 26 Appendix B - Codes ............................................................................................................................ 28 Appendix C - Database: full sample ................................................................................................... 28 Appendix D – Database: ECA countries.............................................................................................. 28 References ............................................................................................................................................. 28 2 1. Introduction Foreign Direct Investments are a very dynamic phenomenon at the lead of the globalization wave which has characterized all markets in the last decades. In this context of international integration, the activity of multinational enterprises (MNEs) has increased relentlessly. As reported by Dharmapala and Riedel (2013), from 1990 to 2004, Foreign Direct Investment by MNEs grew at an average annual rate of 12,4%, which is more than double than the 5% growth rate of economy. As the share of international transactions represented by FDI increases, so does the research interest for its determinants, as all countries, especially developing ones, are eager to take steps to attract foreign funds. While a substantial body of literature exists about factors driving FDI patterns, no consensus has been found, meaning that there is not a universally accepted set of variables which can be considered as the true determinants of FDI. Moreover, most of existing empirical and theoretical findings rely on a bilateral framework which most of the time fails to take into account the inherent complexity of FDI decisions, which are multilateral by their own nature. For example, an investment abroad could be addressed to a geographical position which enables the company to strategically outsource their production in different countries in order to exploit factor cost differences. Or, a host country could serve the parent as a base to operate in neighboring economies. Agglomeration externalities and imperfect capital mobility - limiting the funds that MNE can invest abroad - can also create interdependent FDI decision across host countries. Standard econometric techniques cannot take into account multilateralism of FDI dynamics, since the latter implies that observations across countries are not independent, a violation of the most fundamental assumption of traditional econometrics. Spatial econometrics models help to overcome this problem allowing for spatial autocorrelation between observations, thus capturing potential spillover effects or the impact of market potential on the level of FDI in one country. Last years have seen a development of the literature on spatial determinants of FDI patterns. Coughlin and Segev (2000) were the first to apply a spatial econometric model to study the determinants of US FDI in Chinese provinces, and found that volumes of FDI in one region were correlated with those of neighboring regions. Subsequent studies made on different parent countries - among others Baltagi et al. (2007) Blonigen et al. (2007) for US outward FDI, Garretsen and Peeters (2009) for Dutch outward FDI, Ledyaeva (2009) for Russian inward FDI, and Chou et al. (2011) for Chinese outward FDI - find spatial correlations across FDI levels in neighbor countries. Our work fits into this latest line of research, implementing a spatial econometric analysis on the determinants of US outbound FDI in 2013 on a sample of 85 developed and developing countries. It is structured as follows: section 2 introduces the reader to the main existing models about FDI patterns. It includes a literature review on the most important theoretical and empirical works on the topic. Section 3 provides a description of the database and the weight matrix used for the analysis , and is completed by section 4 which presents the descriptive statistics. Sections 5 and 6 include the spatial econometric analysis performed over the full sample and over a selected subsample of countries, respectively. Section 7 concludes and presents the limitations of the research. 3 2. Theoretical background and sources of spatial interdependence. The first formal development of MNE theory was provided in two important theoretical works from 1984. Markusen (1984) presents a general equilibrium model in which parent firms establish affiliates to produce and sell in the host country, in order to gain on trade costs, which is known as “horizontal FDI”. Since this translates in higher fixed costs, this kind of MNEs arise in countries with large markets (which allow for economies of scale and offer many investment opportunities) and where set-up cost of factories are low and trade costs are high. In contrast, Helpman’s (1984) general equilibrium model assumes that affiliates are created by parent companies to carry out some stages of the production process, in order to access cheaper input factors, while the home market remains the main destination of the firm. This type of investment is usually referred to by the term “vertical FDI” and is more likely to come into existence when the host country is unskilled-labor abundant and trade costs, as well as plant set-up costs, are low. This gravity-type framework laid the foundation for the majority of empirical studies that followed, which generally report as the main determinants of FDI in a host country: market size, distance from the parent country, human capital endowment, availability of infrastructure, quality of governance and security of property rights. However, this kind of framework fails somehow to take into account the inherent complexity of FDI decisions, as it considers MNEs’ investment decisions as bilateral. More recent models have tried to relax this limitation, mainly through three extensions. First, Carr et al. (2001) and Markusen (2002) have merged the gravity-type environment into a model – known as “knowledge-capital” model - in which the equilibrium between vertical and horizontal FDI in MNEs is endogenously determined, depending on factor availability and barriers to trade and investments. “Export-platform” models (Ekholm et al. (2003), Yeaple (2003), and Bergstrand and Egger (2004)) consider the possibility that the output of foreign affiliates is rather exported than sold in the home market. In other words, MNE invest in a particular country to use it has a “base” to reach neighboring markets. In an empirical study from 2005, Hanson et al. consider the share of US affiliates export to home and foreign markets and find evidence of the importance of export-platform FDI in US MNEs. Alternatively, MNEs can fragment the vertical chain of production across multiple countries in order to exploit the comparative advantages in costs and productivity of different markets, before the product reaches its final destination. Baltagi et al. (2007) named this last practice “complex-vertical” FDI and formalize the importance of “third-country effect” in FDI patterns. They consider a model with two sectors (one homogeneous and one differentiated), three factors of production (capital and skilled and unskilled labor) and three countries. When more than two countries are included, the decision to invest in a host country is not bilateral anymore, but depend also on the characteristics of the third market. Traditional econometric techniques are not suitable for capturing a third-market effect, since the latter implies that FDI decisions across host countries (hence FDI volumes) are not independent. Spatial 4 econometric techniques allow to overcome this problem and each of the described FDI motivations has a different implication in term of spatial relations1. Following Blonigen et al. (2007) in this section we fill focus on the correlation between the level of FDI in a country and in its neighbors, and how the surrounding market potential influences the level of FDI in a country. However, our estimation will reveal the importance of other sources of externalities. Horizontal FDI arise with the objective to avoid high trade costs for the parent companies, which are usually due to import barrier imposed by host countries. In pure horizontal models the only trade off faced by companies when deciding to establish foreign affiliates is whether the trade cost avoided from operating directly in the host market is greater than the set-up costs. In this framework, surrounding market potential or FDI level in neighbor countries will not play any role in the final decision outcome. For this theoretical prediction to be realistic, however, one needs to assume that all destination markets have sufficiently high trade protection against imports from other destination markets. When this is not the case, and the commercial protection among destination countries is lower compared to trade costs incurred by the parent to export in those markets, it is likely that MNE will opt for a “export-platform” type of FDI. Establishing one affiliate in a strategic position will bring the same benefits coming from horizontal FDI in every destination country, while saving the high set-up costs of multiple plants, since it allows the company to rich simultaneously all the neighbor markets without incurring in important trade costs. For this reason, it is plausible to assume that the surrounding market potential will have a positive effect on the level of FDI in the host country of interest. At the same moment, since fixed costs coming from setting-up multiple plants are much higher than trade costs between neighbor countries, the decision of a parent company to establish an affiliate in a country will likely come at the cost of not establishing in the neighbors, which translates in a negative sign of the spatial lag on the FDI. Vertical FDI model implies that companies choose to invest in the host country which offers the lowest production costs for the activity which needs to be relocated. Analogously to what happens for exportplatform, the decision of a pure vertical MNE to invest in a country comes at the expense of neighboring countries. On the other hand, since in this kind of MNE the final destination of products remains the home market, the surrounding market potential is supposed to have an insignificant effect on the volume of FDI in the host country. In complex-vertical MNEs the production process is “fragmented” across different countries. Consequently, a dense supplier network in a region can decrease the costs of separating productive activities. Moreover, there might be other agglomeration forces (e.g. location of immobile resources as mines, IT districts) which, as long they concern the production process of parent companies, can attract FDI. In this sense, it is plausible to expect that both spatial lag on FDI and on market potential can have a positive impact on the FDI inflow into a host country. 3. Data and methodology The analysis to understand the determinants and pattern of US investment abroad is performed over 85 host countries for the year 2013. The database includes 14 Asian and Pacific (AP) countries, 29 It exists a multitude of spatial relations which will be presented in the following sections. Details on the econometric model and techniques will be provided in section 5. 1 5 countries located in Europe and Central Asia (ECA), 16 Latin American and Caribbean (LAC) countries, 8 countries located in Middle East and North Africa (MENA) and 18 Sub Saharan Africa countries2. The complete list of countries can be found in appendix A (table A1). Although Canada is the country attracting the highest level of US investments, it has been excluded from the sample due to the fact that it is isolated from the rest of countries, thus it might create some issues when performing the spatial analysis. Following Beugelsdijk et al. (2010), the level of US Foreign Direct Investment in each country is proxied by Sales of US MNE affiliates into third countries. Affiliates sales, contrarily to FDI stocks, represent a reasonably unbiased proxy of the value added generated by US firms abroad. Data for this measure are obtained from mandatory surveys of U.S. multinational enterprises conducted by BEA3. Outbound of FDI from the United States has been chosen because the US are one the largest foreign investors in the world. Host countries have instead been chosen arbitrarily depending on the availability of data. It is important to notice that missing data in BEA surveys do not indicate absence of US FDI in a host country, but they represent a choice of BEA to avoid disclosure of data of individual companies. Having access to those data in further research could strengthen this kind of analysis, since the presence of “white spots” in the map might cause problems of identification of spatial interaction effects. To account for this issue, we tried as well to implement the analysis exclusively on European and Central Asian countries, which form an unbroken study area. The control variables have been chosen based on previous literature (Blonigen et al., 2007; Regelink and Elhorst, 2015). They include traditional determinants of the standard gravity model – as GDP, population, distance and trade costs – as well as variables stemming from the most recent literature, as the level of instruction and institutional quality. Values of these variables for the parent country are not taken into account. Since the parent country is always the United States it does not provide any additional information to include the observation on these variables for the US. Total GDP, measured in current US dollars, accounts for the market size of the economy. The sign of its effects on FDI flow is expected to be positive, since a greater market size offers more investment and operating opportunities. To control for the fact that FDI have the tendency to move between wealthier markets (Blonigen et al., 2007) the total population – measured as the total number of residents – is included among the regressors. Since, holding GDP constant, a higher population decreases GDP per capita – which proxies the wealth of a nation – a negative impact on FDI is expected. For completeness, GDP per capita has been included as an alternative proxy for market size. Data for the aforementioned variables are taken from the World Bank Development Indicators4. Trade cost represents the barriers that might affect the trade between home and parent country and is obtained computing the inverse of the standard openness measure, calculated as the sum of exports Countries are grouped by geographical region according to the World Bank classification. Asian countries include country from South Asia and East Asia and Pacific regions. 3Bureau of Economic Analysis, US direct investment abroad: Operations of US parent companies and their foreign affiliates. https://www.bea.gov/international/di1usdop 4 https://databank.worldbank.org/source/world-development-indicators 2 6 plus imports divided by GDP5. According to previous researches effects of trade cost on FDI flows are ambiguous and depend on the prevalent form of FDI. If FDI are undertaken in order to exploit factors cost differences by vertically outsourcing production, higher trade costs will discourage the home country from investing in that host. On the contrary, as previously mentioned, very high trade costs could also create an incentive for the parent firm to establish a new plant in the host country in order to avoid high transportation cost or excise duties. This latter case will translate in a positive impact on FDI outbound toward a country with high barriers to trade. The distance between parent and host countries is also added and it can be considered a proxy for both higher management costs - which increases costs of production thus decreasing benefits coming from investing in a certain host country - and higher trade costs, whose impact is ambiguous. The data on bilateral distances come from the CEPII database6 where they are computed using the geographic coordinates of the capital cities using the great circle formula. Skill endowments and investment risk are also expected to impact the inflow of US FDI in host countries. Skill endowments is proxied by average years of schooling for people aged 25 or older. This measure is taken from the Human Development Reports of the United Nations Development Programme, which is able to provide a wide range of observations, gathering values from Barro and Lee (2018), the UNESCO Institute for Statistics (2019) and ICF Macro Demographic and Health Surveys, UNICEF Multiple Indicator Cluster Surveys and OECD (2018). As higher skills improve productivity of workers, thus lowering production posts, their impact on FDI volumes in a host country is expected to be positive. The degree of investments risk is instead expected to have a negative effect on US FDI inflows toward host countries. This risk can be influenced by several factors as weak property rights, political and economic instability and also incidence of conflicts. All these sources of risk can be grouped under the wider concept of governance. A number of studies has proven that good governance enhances attractiveness of host countries (see, among others, Azemar and Desbordes (2009)). Therefore, the risk is proxied by the Quality of Government index (as in Regelink and Elhorst, 2015). This index is constructed by Quality of Government Institute7 and is calculated as the mean value of the variables corruption, law and order and bureaucratic quality. It ranges between 0 and 1, and increases if the quality of government increases. Hence, it is expected to have a positive coefficient in the econometric estimation. These variables are taken together to form the so-called modified gravity model (Blonigen et al. 2007): This simple specification, which throughout the paper will form our baseline result, is then modified to take into account spatial interaction between the components of the equation. To this purpose, the choice of an adequate spatial weight matrix is fundamental to capture the existing spatial dynamics between countries. Since there are white spots in our map, and world countries are quite geographically dispersed, the most suitable matrix is the one constructed using the closest neighbors. Since the number of neighbors varies widely from country to country, we decided to use the matrix for the 5 closest neighbors as a baseline, and then to test different specifications using a higher number of neighbors. Import and Export measures come, again, from the World Bank Development Indicators. http://www.cepii.fr/CEPII/en/bdd_modele/presentation.asp?id=6 7 https://qog.pol.gu.se/data 5 6 7 The trade off in this case is to be able to take into account the underlying spatial relations without considering very far countries as neighbors, which can invalidate the results. To this regard, the maximum number of neighbors that has been taken into consideration is 9. As suggested in Regelink and Elhorst (2015), when the study is performed only on European and Central Asian countries, an inverse of distance matrix is chosen. Since countries form a more compact group, this kind of matrix can be preferred to the closest neighbors. However, the use of a contiguity matrix is made impossible by the presence of some islands, as Cyprus and British Isles. 4. Descriptive statistics Before proceeding with the econometric analysis of the relationship between the determinants described above and US FDI volumes in host countries, this section includes a descriptive overview of the data to illustrate the database. When performing a spatial econometric evaluation, two kinds of descriptive statistics can be distinguished: a standard descriptive analysis, which is useful to get a general idea of the existing dynamics between variables, and a spatial descriptive statistics, which allow to detect potential spatial relations and patterns among observations which can be included in the econometric model. 4.1 Standard descriptive statistics A potential problem of this analysis can be the high heterogeneity of observations. The database includes countries from all regions of the world, without distinguishing, for example, for the level of economic development. This inevitably translates in a very high standard deviation, especially for those variables as GDP, population and distance which cover very wide range of values. When observations are highly dispersed it can be difficult to detect relationships between variables and the estimation is likely to give high standard errors. A potential solution to this issue is the use of logarithms, which allows to smooth extreme observations. Table 1 (Panel A and B) reports the mean and standard deviation of variables in level and in natural logarithm. As expected, dispersion is much smaller when the logarithm form is used. Moreover, since the database contains countries that are highly heterogeneous, having access to regional summary statistics can be more informative about existing differences across regions. Panel C of Table 1 reports regional averages for all variables and highlights the existence of some regional disparities. In particular, MENA and SSA countries show consistently lower US FDI volumes with respect to other regions. Moreover, countries located in Europe and Central Asia rank first in the majority of indicators. In particular they display striking differences with respect to other countries in investment risk and trade costs, which are significantly lower than elsewhere. This could suggest the presence of some kind of agglomeration effects, and supports the potential existence of spatial correlation. 8 Table 1 - Mean and Standard Deviation Panel A – Mean and standard deviation for the variables in level Mean Standard Deviation 62294.06 120078.08 622056333363.21 1331992566609.53 19389.26 23491.16 Population 63845369.8 201457587.09 Trade cost 1.46 0.74 Skills 8.93 3.14 Investment risk 0.55 0.23 8431.89 3594.65 Sales 2013 GDP GDP pc Distance Panel B – Mean and standard deviation for the variables in level Mean Standard Deviation 8.65 25.64 9.06 16.58 0.26 2.1 -0.69 8.94 3.21 1.9 1.44 1.59 0.49 0.46 0.44 0.49 ln Sales 2013 ln GDP ln GDP pc ln Population ln Trade cost ln Skills ln Investment risk ln Distance Panel C – Regional average for all variables in natural logarithm Sales 2013 GDP GDP pc Population Trade cost Skills Investment risk Distance AP ECA LAC MENA SSA 9.95 26.77 9.09 17.69 0.36 2.17 -0.52 9.52 9.82 26.42 10.25 16.17 -0.01 2.45 -0.36 8.87 9.6 25.44 9.07 16.37 0.5 2.14 -0.86 8.27 8.31 25.66 8.82 16.84 0.3 1.95 -0.81 9.12 5.04 23.66 7.24 16.42 0.4 1.54 -1.14 9.09 9 As regional disparities have been detected in the average volumes of US FDI, it might be useful to check the dispersion of the dependent variable in each region. Figure 2 displays the boxplots for the dispersions of US FDI volumes (in natural logarithm) in each of the five world’s regions outlined in section 3. Dispersion of data is quite different across regions. The dispersion of FDI volumes is particularly high in ECA and SSA countries. Indeed, ECA region includes countries from western Europe, which are very highly developed and attract huge volumes of US FDI, with countries from east Europe and Central Asia, the majority of which are emerging economies. The SSA region also covers a very wide range of value, since in some of the less developed countries, as Niger, Togo or Gambia, FDI volumes are close to 0 while in more dynamic economies, as South Africa, values are much higher, hitting thousands of millions of dollars. The majority of regions displays a right skewed distribution, with the exception of LAC and MENA countries which display a strong left skewedness. In conclusion, it can be noticed that the logarithm form was useful also to correct for the presence of outliers, which instead where numerous when considering US volumes in levels. Figure 2 - Boxplots for the dependent variable (log), by region Before proceeding with the spatial analysis of the database, it might be helpful to have an idea of the type of relationship existing between the dependent variable and each of the explanatory variables, as well as the amount of correlation. In order to obtain this information, each of the predictors was plotted against the dependent variable, and a linear regression model was used to obtain the line of best fit. Figure 3 displays the resulting scatter plots for each of the predictors. The strongest linear relation is the one between the amount of US FDI, as proxied by affiliates sales, and market size, as proxied by GDP. This is in line with the previous literature, which recognizes market potential as one of the main determinants of FDI. 10 The scatter plots also highlight a positive linear relation between FDI and GDP per capita, population, skills (proxied by average years of schooling) and investment risk (which is proxied by the quality of governments), even if observations are more dispersed. The only two variables which seem to be almost uncorrelated with the level of US FDI are the distance - which displays anyway a slightly negative relation with FDI – and trade cost – which on the contrary is slightly positive correlated with FDI levels. Considering the distance, this result is sharply in contrast with previous literature. However, it is possible to hypothesize that the increasing international integration of worldwide economies and the availability of technologies which reduce costs and time of dealing with distance is contributing in decreasing the relevance that it covers in investment decisions of MNEs. The low correlation between trade costs and FDI levels can instead be associated to a presence of both vertical and horizontal FDI motives, which, as previously mentioned, react in opposite ways to the presence of higher barriers to trade. Figure 3 – Scatter Plots Relationships highlighted in Figure 3 can be found also in the correlation matrix, which in addition includes the sign and amount of relationships between all variables. This is very useful to detect for the possible presence of multicollinearity. In general, it does not report very high correlation across the explanatory variables, with the exception of the proxies for skills and investment risk, which are highly correlated with GDP per capita. This could 11 rise some problems when performing the estimation for their coefficients. This potential issue will be further investigated in Section 5. Figure 4 – Correlation Matrix 4.2 Spatial descriptive statistics The spatial descriptive statistics provides information about the existence and the amount of spatial correlation among observations. In this study we are mainly interested in detecting the presence of spatial autocorrelation in the amount of US FDI, which can signal the presence of potential spillovers effects among countries, as well as in the proxies for market size, namely GDP and GDP per capita, to study the surrounding market potential of countries. A first way to check for the presence of agglomeration effects is to plot the variables of interest in a map. This allows to check for the existence of geographical regions with higher concentration of US capitals and of the other variables of interest. Figure 5 plots the amount of US FDI, as proxied by affiliate sales, for all host countries included in the sample. It is possible to detect some regions where US FDI volumes are much higher than elsewhere. In particular, western European countries seem to attract the highest amount of US capital, followed by East Asia and Pacific region and Latin America and the Caribbean. In contrast, African and Middle East seem to attract very low levels of US FDI. 12 Figure 5 A Moran test performed on our observations confirms the presence of spatial autocorrelation in the amount of US FDI worldwide. As specified in Section 3, the weight matrix of the 5 closest neighbors will be used as a baseline. Appendix A reports the Moran test and the Moran map using the closest 9 neighbors, which is the maximum number of neighbors which will be considered. Table 2 reports the result from the Moran Test: the p-value is close to 0, meaning that we refuse the hypothesis of absence of spatial autocorrelation with a good amount of certainty. The Moran I statistics is greater than 0, signaling a positive spatial autocorrelation: this means that neighboring countries tend to display similar volumes of FDI. The result is confirmed when the test is performed using larger weight matrixes. Table 2 - Moran Test Moran I test under randomization data: ([email protected]$sales2013) weights: PPV5.w Moran I statistic standard deviate = 5.7692, p-value = 3.984e-09 alternative hypothesis: greater sample estimates: Moran I statistic Expectation Variance 0.311702157 -0.011904762 0.003146382 Panel A of Figure 6 reports the Moran Diagram and the Moran Map obtained using the closest 5 neighbors. Panel A reveals that the majority of observations can be found in the 3rd quadrant: there are many countries which display below average values of US FDI and are clustered in below average regions. The Moran Map reported in Panel B is an illustration of the Moran diagram and allows to understand which are the countries in the “above average” neighborhoods and in the “below average” neighborhoods. The most clustered regions are western Europe, which confirms to attract the highest 13 volumes of US FDI, and Africa and Middle East, where many countries displaying very low volumes of US FDI can be found in the same neighborhood. The classification of Eastern Asia and Pacific countries as “hot points” (countries with above average values which are clustered in above average regions) varies widely with the choice of the weight matrix. Using a higher number of closest neighbors usually highlights the presence of a second cluster of countries attracting above average volumes of US FDI, which includes China, Japan, India and Thailand – as can be observed in figure A2 in the appendix. Figure 6 - Spatial Autocorrelation Panel A – Moran Diagram Panel B – Moran Map 14 Appendix A includes also the Moran test and the Moran map for the logarithm of observations. The existence of spatial correlation is confirmed and the Moran test reports higher values for the Moran I statistics, signaling an even stronger positive correlation. Although correlation remains positive, the majority of observations is now found in the upper right quadrant, meaning that the majority of units has above average values for US FDI and are clustered in neighbors with high FDI volumes. This is due to the logarithm transformation, which reduces substantially the variability of observations; however, equilibriums across geographic regions remain similar, with the exception that south America is now classified as an above average neighborhood. Before proceeding with the choice of the econometric model, it is useful to check whether similar patterns emerge also in the Moran maps for the explanatory variables, which can be find in Appendix A. In particular, it can be noticed that the Moran Map for the GDP displays very similar clusters to that for US FDI: the highest levels of GDP can be found in western Europe and Asia. This is in line with the high correlation existing between the two variables, and indicates that GDP could be a suitable proxy for market size in this study. On the other hand, GDP per capita highlights a unique above average area, western Europe, while the rest of the world - with the exception of Australia, New Zealand, Japan and Saudi Arabia - is classified as cold point. This supports the idea that GDP might be a better proxy for market size than GDP per capita. Other explanatory variables display quite different dynamics and highlights different clusters. It can be noticed that both patterns of average education and quality of governments display positive sptalia correlation, and always recognize western Europe as an above average cluster. On the other hand, observations for trade costs seem to be more negatively correlated, as it is common to find countries with very low trade costs next to countries with above average trade costs. Two clusters can be recognized: northern Europe, which is a below average area, and south America, whose trade costs are on average higher than the rest of the world. However, those clusters are not the same highlighted by the Moran map for FDI volumes, which is in line with the low correlation between those two variables. 5. Choice of the econometric model and results: full sample In order to build a suitable model for the purpose of our analysis, we start from the simple linear regression model which will be estimated by OLS (Ordinary Least Squares). Even if this is a non-spatial model, it is commonly used as a diagnostic tool for model specification evaluation and as a benchmark for comparisons with spatial models. Then we will proceed to analyze the presence of spatial interactions among the units considered. The simple OLS model is: 𝑦 = 𝛼 + 𝑋𝛽 + 𝜀 Where 𝑦 is the intercept coefficient, X is the matrix of the regressors and 𝛽 containes the corresponding coefficients to be estimated; 𝜀 is the usual error term, that is a white noise term. Including the set of regressors, we obtain the following modified gravity model: 15 The descriptive analysis revealed that a log-log specification is suitable for our data. This is supported by previous findings as Blonigen and Davies (2004) which find that such specification leads to well behaved residuals. Results of this specification are reported in Table 4, where different regressions are presented in order to show several issues that were encountered during the estimation. The most general specification included all the regressors presented above. As it is possible to see in column 1, this specification presents very weak coefficients and almost none of them resulted significant. This is likely due to the presence of collinearity among GDP per capita and both investment risk and skills proxies, as highlighted in section 4.1. Column 2 presents the result of the estimation without the inclusion of GDP per capita. Clearly this specification improves considerably and shows the relevance of the market size in influencing FDI flows, GDP becomes in fact highly significant. Moreover, the level of skills endowment of a host country seems also to matter – the coefficient is significant at 5%. The distance between the parent and the host country results relevant and with a negative impact which could have the straightforward interpretation that a higher distance implies higher transportation or management costs. The measure for investment risk remains insignificant even when removing GDP per capita. Therefore, an additional specification is included. Column 3 shows the results of the regression excluding both GDP and GDP per capita: only in this case investment risk resulted significant. However, since the market size has showed to be a main determinant of FDI flows, it is important to keep at least one variable that would allow us to see its relevance when implementing a spatial model. Therefore only GDP per capita was excluded ant the baseline specification is the one showed in Column 2. 5.1 The choice of the spatial model When performing a spatial analysis, one of the most crucial points is the choice of the weight matrix. The presence of several white spots in our map influenced our choice, and made it impossible to use the contiguity matrix. When the entire sample is taken into account, units are highly geographical dispersed, and the most suitable matrix is those constructed using closest neighbors. Although we performed several estimations using different number of neighbors, this section presents results using the 5 and 9 closest neighbors, which are respectively the minimum and the maximum number of neighbors which will be considered in this paper. We approach the choice of the model using a bottom-up strategy: we started from a non-spatial model and we then performed several statistical tests to detect the presence of different spatial relations. All the following tests are applied on the baseline specification discusses above. 16 Table 3 - OLS regression results Dependent variable: Ln(Sales of Us Affiliates) Complete (1) No GDPpc (2) Ln(GDP) 0.6148 (6.0603) 1.0521*** (0.2862) Ln(GDPpc) 0.4392 (6.0794) Ln(Population) 0.6948 (6.0890) 0.2555 (0.3028) 1.2342*** (0.1554) Ln(Trade cost) -0.6486 (0.4789) -0.6467 (0.4751) -0.3414 (0.5035) Ln(Skills) 1.6024** (0.7369) 1.6070** (0.7295) 3.4721*** (0.5642) Ln(Investment risk) 0.6787 (0.6867) 0.6834 (0.6792) 2.1766*** (0.5860) -0.9532** (0.4015) -0.9510** (0.3978) -1.2440*** (0.4194) -16.8278*** (4.8185) -16.8030*** (4.7755) -6.4097 (4.1425) 85 0.7545 0.7322 1.6613 (df = 77) 85 0.7545 0.7356 1.6506 (df = 78) 39.9571*** (df = 6; 78) 85 0.7120 0.6938 1.7766 (df = 79) Ln(Distance) Constant Observations R2 Adjusted R2 Residual Std. Error F Statistic 33.8129*** (df = 7; 77) Note: No GDP no GDPpc (3) 39.0575*** (df = 5; 79) *p<0.1; **p<0.05; ***p<0.01 First of all, to choose between a spatial and non-spatial model, the Moran I test for the presence of residual spatial autocorrelation is performed. The null hypothesis of this test is the absence of spatial autocorrelation; hence, considering that the p-value is equal to 0.0001124, we will reject H0 and confirm the hypotheses about the presence of a spatial link among our data. However, it must be pointed out that the value of the Moran I indicate only a mild autocorrelation. Table 4 – Moran I Test Global Moran I for regression residuals data: model: lm(formula = modeleln1, data = mylndata) weights: matrice Moran I statistic standard deviate = 3.6894, p-value = 0.0001124 alternative hypothesis: greater sample estimates: Observed Moran I Expectation Variance 0.161251104 -0.040365644 0.002986355 17 Once the presence of spatial autocorrelation is confirmed, we proceed to perform different statistical test in order to choose the spatial model that will fit our data in the most accurate way. There are four possible models to be chosen: SLX, SAR, SEM and SDM. These spatial econometrics models account for the three types of spatial interaction: • • • an endogenous interaction, when the economic decision of an agent or geographical zone will depend on the decision of its neighbors (SAR or Dynamic model); an exogenous interaction, when an agent’s economic decision will depend on the observable characteristics of its neighbors (SLX model); a spatial correlation of the effects due to the same unobserved characteristics (SEM model). This correlation could be explained by the presence of a spatially structured latent variable or by the presence of composition effects which marks a form of spatial heterogeneity. Finally, the SDM model is a combination of the SAR and the SEM and by combining the two models, it also includes the features of the SLX specification. The SDM model is the more complex one since it aim at analyzing the presence of spatial externalities, of a spillover effect and of spatial autocorrelation in the error terms. To choose among these models, the Lagrange multiplier tests are performed. Below the results of four different tests are reported: LMerr, LMlag, the RLMerr and the RLMlag. These tests account for, respectively: error dependence, a missing spatially lagged variable, error dependence but conditional on the presence of a missing spatially lagged variable and lastly for the presence of a missing spatially lagged variable but in presence of error dependence. Table 5 - Lagrange Multiplier p-value results LMErr LMlag RLMerr RLMlag p-value = 0.0107 p-value = 0.9646 p-value = 0.00166 p-value = 0.06598 Since we cannot reject the null hypothesis of the LMErr and the RLMerr, the above results indicate that the preferred model should be the SEM. However, before proceeding with this model, the likelihood ratio test is performed. The latter compares the differences between the Spatial Error Model (SEM) and the Spatial Durbin Model (SDM) and indicate the more appropriate specification. The presence of pvalue smaller than 0,05 bring to the conclusion that the SDM model is the best one. Table 6 - Likelihood ratio test between the SDM and SEM model Likelihood ratio for spatial linear models data: Likelihood ratio = 17.17, df = 6, p-value = 0.00868 sample estimates: Log likelihood of ze.sardm Log likelihood of ze.sem -146.4163 -155.0012 18 Finally, the Akaike Information Criterion might be helpful with the choice, confirming or rejecting the expected result. In this test, the model which reports the lower value should be the preferred one. In our case the lowest value is the one assigned to the SDM model hence confirming our previous conclusion. Table 7: Results of the Akaike information criterion Model OLS SLX SAR SDM SEM AIC 335.1172 325.2747 337.1098 322.8327 328.0023 At this point it is possible to proceed and estimate the Spatial Durbin Model with the baseline specification discussed above. The model has the following form 𝑦 = 𝑊𝑦𝜌 + 𝑋𝛽 + 𝑊𝑋𝜃 + 𝜀 where 𝑊𝑦𝜌 account for an endogenous effect given by the lagged dependent variable; 𝑊𝑋𝜃, the spatial lag of the explanatory, account for spatial externalities. Moreover, as mentioned, this model is also robust to the presence of spatial autocorrelation in the error terms. 5.2 Results Results coming from the implementation of the SDM model are showed in Table 8. The interpretation of coefficients for a spatial Durbin model is complex since it contains both exogenous and endogenous spatial dependencies. Marginal effects cannot be drawn directly from the coefficients of the regression but it is instead necessary to decompose them in 3 effects. First, the direct effect reflects the impact on the dependent variable of a unit change in the explanatory variables. The indirect effect contains both endogenous – spillovers – and exogenous effect, which may arise from the values of explanatory variables of one country’s neighbors. The sum of these two effects compose the total effect. Table 8 displays the estimates of the 3 effects for our model, while the standard regression table is reported in Appendix A (table A6). Starting from the direct effects, the results show that the traditional determinants of FDI remain significant when a spatial regression is performed. Moreover, they show the expected sign. The market size, represented by GDP, has a positive effect on US FDI flows. This comes to no surprise since the fact that a larger market size should attract FDI flows is quite straightforward. First of all, the GDP is an indicator of the degree of development, therefore, since investors will invest in a country where the perceived profitability of their projects is secured, they will take into account the signal transmitted by GDP in this regard. Moreover, a larger market size implies, usually, lower distribution cost (if of course the production and distribution facilities are located where the majority of the customers will be located) and a greater chance of obtaining better and more specialized inputs. 19 Skills endowment measure is significant and has a positive sign: greater skill levels are usually positively correlated with FDI. This might signal a prevalence of horizontal FDI (both pure horizontal and exportplatform) motives, in which the firm is attracted by host countries with a higher human capital which is believed to improve productivity. In contrast, when pure vertical motives drive FDI decisions, skills might show a negative impact on FDI since MNEs prefer host countries which are unskilled-labor abundant in order to benefit from low labor costs. Investment risk is, as expected, positively linked with the level of FDI. This measure is an indication of the quality of the government therefore it is usually expected to attract foreign investor. The ability of governments to maintain a favorable and stable macroeconomic environment and the strength of the law system are in fact proven to play a positive role in influencing FDI flows in both developed and developing countries. The presence of low inflation and stable exchange rates as well as the high protection of the property rights increase the feasibility of economic activity and the scope for market transactions. The sign of the coefficients of trade costs allows to detect the prevailing motivations behind US FDI outbounds and to understand that FDI decisions across host countries are not independent. Being a proxy for the protectionist measures of a country, high trade costs are one of the main determinants of horizontal or “export-platform” FDI. When strong barriers to trade are present, a parent company might decide to directly set up a new plant in a host country or to establish one affiliates in a strategic position and use this as a “platform” to reach the neighbors countries, a practice which is known as tariff jumping. In both cases a positive sign on the direct effect is expected. However, our results are in the opposite situation. This suggest presence of Vertical FDI. The negative sign of trade costs might in fact indicate that, since MNEs are pursuing the objective of delocalize their production in order to reduce their costs, the presence of high trade cost in a certain country will make them opt for a different location, therefore resulting in a negative impact of FDI outbounds in that specific country. This result is strengthened considering the sign of the indirect effect. Trade cost is the only significant variable among the lagged regressors and shows a positive sign. This seems to be in line with the aforementioned reasoning in favor of vertical FDI. In fact, in presence of vertical FDI motives, if neighboring countries have higher trade costs with respect to a certain host country, parent companies have incentives to locate affiliates in the latter one. Therefore, an increase in the lagged value of trade cost has a significant and positive impact on volumes of FDI toward the host country considered. In addition, the fact that the surrounding market potential, as proxied by GDP, shows a non-significant coefficient seems to point to the presence of a pure vertical FDI framework, excluding the possibility of the complex-vertical model designed by Baltagi et al. (2007). In fact, in this kind of MNE the final destination of products is the parent market. Therefore, the surrounding market potential is supposed to have an insignificant effect on the volume of FDI in the host country. Regelink and Elhorst (2013) confirm that similar conditions to ours signals predominance of pure vertical FDI. 20 Altogether, our estimations do not show a clear indication of the main type of FDI in the sample countries considered. However, heterogeneity of regions analyzed could have created some difficulties in this sense. This issue will be further investigated in section 6. Table 8 - Decomposition of direct and indirect effects, SDM model (5th closest neighbors) Impact Effects: Simulation Results Sales of US affiliates Mean Direct SIgnificance Indirect Mean Significance lngdp 1.1543 yes -1.0808 no lnpopulation 0.3426 no 0.4560 lntradecost lnskills lninvestrisk lndistance -1.2143 2.0120 1.3009 0.9111 yes yes yes no 3.8150 -0.1971 4.0009 -2.1323 Mean Total Significance no no 0.07352 0.79858 yes no no no 2.60075 1.81486 5.30184 -1.22122 no no yes no no * Montecarlo Method, Iterations = 1:1000, Thinning interval = 1, Number of chains = 1Sample size per chain = 1000 5.3 Robustness check: alternative weight matrix As discussed before, the choice of the weight matrix has a crucial role in a spatial analysis. Therefore, in order to test the robustness of our results, we perform the analysis described above using the weight matrix of the 9th closest neighbors. The same baseline specification is considered and the same bottom-up procedure in then followed to choose the best spatial model. The Moran Test confirms that also in this case a spatial model is to be preferred to the standard OLS, even if the value of the Moran I (0.078288898) indicate again a mild autocorrelation. The various statistical test on the kind of spatial correlation indicate that the most suitable model is, also in this case, the Spatial Durbin Model. Table 9 shows the decomposition of direct and indirect effects for the SDM model. Analyzing the average values and the significance of the different coefficients, results are almost identical to the one discussed in the previous section. The market size and the skill endowment of a country stays significant and have positive signs. A larger market size and a more skilled labor force are again proven to bring a positive effect on FDI inflows in a country. The significance and the sign of the trade cost measure is also confirmed. With respect to the other weight matrix, also the indirect effects don’t show any change: trade cost is the only significant variable among the lagged regressors and it shows a positive sign. Therefore, results previously obtained appear to be robust to a change in the weight matrix. 21 Table 9 - Decomposition of direct and indirect effects, SDM model (9th closest neighbor) Impact Effects: Simulation Results Mean Direct SIgnificance Sales of US affiliates Indirect Mean Significance lngdp 1.0466 yes -0.155604 no lnpopulation 0.3878 no -0.001142 lntradecost lnskills lninvestrisk lndistance -1.1262 2.3872 1.4315 0.7642 yes yes yes no 4.415057 -1.637989 2.650763 -2.224384 Mean Total Significance no no 0.8910 0.3867 yes no no no 3.2889 0.7492 4.0823 -1.4601 yes no no no no * Montecarlo Method, Iterations = 1:1000, Thinning interval = 1, Number of chains = 1Sample size per chain = 1000 6. Choice of the econometric model and results: Europe and Central Asia When conducting an empirical study on the location of FDI an important issue is the heterogeneity of the sample of countries considered. Several studies find evidence of substantial differences in FDI determinants when comparing, for example, developed and developing countries. For example, Blonigen et al. (2007) argue that different regions of the world are likely to attract FDI for different reasons: while OECD countries are usually linked to pure horizontal or “export platform” motives they found that, in contrast, in non-OECD countries vertical FDI models prevail. In addition, again having in mind the differences between developed and developing countries, the geographical localization also plays a role in particular, of course, when the spatial dimension is taken into account. Developed countries are in fact primary clustered in Europe (and in general they are geographically located in the north of the equator), while developing countries are more spread in a higher number of continents. Then, when pooling together data from both groups, important information about spatial interaction could be missed, negatively affecting the results of the analysis. Therefore, in this section, we split our sample and decide to focus on a restricted group of 29 countries, all located in Europe or central Asia. Since this group of countries does not present white spots it allows to address potential issues coming from a geographically dispersed dataset. To proceed in the analysis of this group we chose, as the weight matrix, the inverse of distance which is commonly preferred in the FDI literature (Regelink and Elhorst, 2015). Following previous works on similar samples (Blonigen et al., 2007), the threshold is set equal to the distance between Brussels and Amsterdam, that is 173 Km. The model specification is identical to the one we used for the full sample (meaning we exclude gdp per capita) since similar results are founded when implementing the correlation matrix. First, the usual tests are performed to identify the more appropriate model for this sample of countries. After having confirmed the presence of a spatial relation in our model, we implement the Robust 22 Lagrange Multiplier test and the Akaike information criterion. Both tests indicate the SAR model as the best one. Results of the decomposition of direct and indirect effects are reported in Table 10. The sign of direct coefficients is in line with those found in the full sample; however only GDP and trade costs stayed significant whereas in the full sample also skills measures and investment risk indicator proved to have an effect of FDI. Hence, in this case, not all the traditional determinants of FDI inflows seems to be robust to the inclusion of spatial components, indicating that different dynamics might come into play. What is more evident when considering a smaller sample of countries and the results of the analysis implemented on it is that countries cannot be treated as independent entities since stronger third country effects are detected as showed in the table below. In fact, considering indirect effects, interesting results are obtained. First of all, a positive and significant spillover effect from GDP is found: an increase in the GDP of one country will positively impact the dependent variable of its neighbors and this will in turn positively affect the FDI inflows in the initially considered host country. This might suggest that the surrounding market potential plays a role in influencing parent firm’s decisions on where to allocate its investments, thus pointing to the presence of “complex-vertical” and “export-platform” FDI models in which a dense supplier network and the possibility of export in neighbor markets is crucial. When countries close to a possible host are attractive markets and offer several investment opportunities, it could positively affect the decision of the parent company, which has now several incentives to locate its affiliates in that specific host country. Moreover, a negative sign in the coefficient of trade costs seems to strengthen this result: an increase in trade costs will negatively affects the FDI outbound into neighboring countries and therefore indirectly - FDI in the first country considered. More precisely, if trade costs are high in country i, this will negatively impact the level of FDI in its neighbors, which in turn will decrease the volume of US investments directed into i. This effect might be due to a prevalence of “export-platform” FDI inflows: in fact, in this kind of model, multinationals are interested in markets that could be a strategic location base for serving neighbor countries. In this context, the presence of high barriers to trade in one or more countries in a certain region will lower the attractiveness of the latter and will create a negative spillover effect. This hypothesis seems to find support in the fact that export platform models are common in regions where countries enjoy free trade agreements. This is the case, for example, of European Union member countries which are part of the Schengen agreement on free movement of goods and citizens. Moreover, these dynamics apply also when the so called “complex-vertical” FDI prevail. In ECA region, very different markets – from the very rich countries of western and northern Europe to the emerging eastern and central Asian economies – coexist, in a relatively small geographic area. In presence of low barriers to trade, MNEs can exploit differences in productivity and factors costs across these markets, by strategically fragment their production process across those countries. 23 Table 10 - Decomposition of direct and indirect effects, SDM model (Inverse of distance matrix) Mean lngdp lnpopulation lntradecost lnskills lninvestrisk lndistance Direct SIgnificance 1.6122 -0.1749 -1.5525 -1.8754 1.0992 -0.1075 yes no yes no no no Impact Effects: Simulation Results US affiliates sales (ECA countries) Indirect Total Mean Significance Mean Significance 0.76468 -0.06980 -0.72860 -0.83505 0.51414 0.04819 yes no yes no no no 2.37685 -0.24473 -2.28105 -2.71043 1.61337 -0.05933 yes no yes no no no * Montecarlo Method, Iterations = 1:1000, Thinning interval = 1, Number of chains = 1Sample size per chain = 7. Conclusions and limitations Using a sample of 85 host countries (observed in the year 2013) this work aims to investigate the multilateral nature of US FDI decisions and the possibility of spatial patterns in US investment abroad. In addition, results obtained in this kind of analysis allow to understand the underlying motives existing behind investments decisions by multinational companies. First, a spatial econometric analysis was performed on the entire sample, which includes countries from five world regions. Results from this first step reveal, in general, a prevalence of vertical FDI motives: US multinational companies mainly invest abroad to exploit costs differences in factors of production. This is coherent with the composition of our sample of countries. The sample used for this study had to exclude for technical reasons two of the world leading economies: United States and Canada. Hence, apart from western Europe and Oceania, the remaining countries can be mainly considered emerging economies, which typically are able to offer low production costs to foreign companies. In this context, externalities or spillover effects play a minor role as US companies are mainly interested in producing abroad and selling in the home market. Then, a spatial analysis was performed exclusively over European and Central Asian countries, which highlighted very different dynamics. Spatial lags gained significance, revealing important spillover effects in the European market. Surrounding market potential and existing trade barriers across ECA countries are revealed to be important determinants of US FDI patterns in this area. This suggest that decisions of US multinational companies to invest in Europe or Central Asia are highly supported by export-platforms motives or can be categorized as complex-vertical FDI. Taking into account this result one may conclude that FDI decision cannot be considered as bilateral. Instead, multilateral relationships and third country effects come into play. However, when drawing conclusions from our results it must be taken into consideration that the difference in the relevance of spatial dynamics in the two samples could be due to the geographical 24 dispersion of countries when the entire sample is studied. As we already mentioned, the presence of white spots might cause problems of identification of spatial interaction effects. However, with existing spatial econometric techniques, this is a limitation one must take into consideration when performing this kind of analysis. In addition, when considering such a heterogeneous group of country, it results very hard to identify a single type of FDI and it is instead more likely that different FDI motivations coexist, and contrasting results should be expected in the estimated coefficients of explanatory variables. A limitation exists also regarding the second step of the analysis since the sample of selected ECA countries is too small and risks to report some bias or not to be representative. Hence, one must be cautious when extending related findings to larger samples or drawing policy implications. Despite its limitations, this work brings some very interesting findings which should be further investigated. In particular, while the majority of literature has focused on the spatial dynamics regarding market potential, the existing barriers to trade across host countries and the related spatial dynamics seem to be highly relevant in MNEs decisions on where to direct their investments. 25 Appendix A Table A1 – Countries included in the sample, by region AP Australia, Brunei, China, Indonesia, India, Japan, Republic of Korea, Sri Lanka, Malaysia, New Zealand, Philippines, Papua New Guinea, Thailand, Vietnam ECA Austria, Azerbaijan, Belgium, Bulgaria, Belarus, Switzerland, Cyprus, Czech Republic, Germany, Denmark, Spain, Estonia, Finland, France, United Kingdom, Croatia, Hungary, Ireland, Italy, Lithuania, Luxembourg, Latvia, Netherlands, Norway, Poland, Portugal, Russia, Sweden, Turkey LAC Argentina, Bahamas, Brazil, Chile, Colombia, Dominican Republic, Ecuador, Guatemala, Honduras, Jamaica, Mexico, Panama, Peru, Paraguay, Trinidad and Tobago, Venezuela MENA Egypt, Iraq, Israel, Jordan, Libya, Morocco, Saudi Arabia, Yemen SSA Botswana, Cote d'Ivoire, Congo (Kinshasa), Congo (Brazzaville), Gabon, Guinea, Gambia, Kenya, Liberia, Mali, Malawi, Niger, Nigeria, Sudan, Senegal, Togo, South Africa, Zimbabwe Table A2 - Moran test, diagram and map using PPV9.w Moran I test under randomization data: ([email protected]$sales2013) weights: PPV9.w Moran I statistic standard deviate = 7.0074, p-value = 1.214e-12 alternative hypothesis: greater sample estimates: Moran I statistic Expectation Variance 0.279431528 -0.011904762 0.00172853 Table A3 - Moran test, diagram and map of logarithm of observations using PPV5.w Moran I test under randomisation data: ([email protected]$lnsales2013) weights: PPV5.w Moran I statistic standard deviate = 7.0178, p-value = 1.127e-12 alternative hypothesis: greater sample estimates: Moran I statistic Expectation Variance 0.410600879 -0.011904762 0.003624615 26 Table A2 - Moran test, diagram and map of logarithm of observations using PPV9.w Moran I test under randomisation data: ([email protected]$lnsales2013) weights: PPV9.w Moran I statistic standard deviate = 9.4803, p-value < 2.2e-16 alternative hypothesis: greater sample estimates: Moran I statistic Expectation Variance 0.411077759 -0.011904762 0.001990687 Table A3 - Moran Maps of the explantory variables Panel A: GDP Panel C: population Panel E: skills Panel B: GDP per capita Panel D: trade costs Panel F: quality of government 27 Table A4 – SDM regression results Dependent variable: lnsales2013 SDM lngdppc lnpopulation lntradecost lnskills lninvestrisk lndistance lag.lngdppc lag.lnpopulation lag.lntradecost lag.lnskills lag.lninvestrisk lag.lndistance Constant 1.0345*** (0.2664) 1.4419*** (0.1336) -1.1065** (0.4402) 2.3974*** (0.7057) 1.4333** (0.6298) 0.7564 (0.7425) -0.1882 (0.9124) -0.1120 (0.5903) 4.4072*** (1.4258) -1.4861 (1.5912) 2.8607 (2.2315) -2.3185** (1.0894) -6.3954 (13.8960) Observations Log Likelihood sigma2 Akaike Inf. Crit. Wald Test LR Test 85 -146.7181 1.8482 323.4362 0.0238 (df = 1) 0.0212 (df = 1) Note: *p<0.1;** p<0.05; *** p<0.01 Appendix B – Codes: full sample Appendix C - Database: full sample Appendix D – Codes: ECA countries Appendix E – Database: ECA countries References Baltagi, B. H. & Egger, P. & Pfaffermayr, M. 2007. "Estimating models of complex FDI: Are there third-country effects?". Journal of Econometrics, Elsevier, vol. 140(1), pages 260-281, September. Beugelsdijk, S. & Hennart, J. F. & Slangen, A. & Smeets, R. 2010. "Why and how FDI stocks are a biased measure of MNE affiliate activity". Journal of International Business Studies, Palgrave Macmillan; Academy of International Business, vol. 41(9), pages 1444-1459, December. Blonigen, B. A. & Davies, R. B. & Waddell, G. R. & Naughton, H. T., 2007. "FDI in space: Spatial autoregressive relationships in foreign direct investment". European Economic Review, Elsevier, vol. 51(5), pages 1303-1325, July. 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