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FDI in the post-EU accession Baltic Sea Region: A global or a regional concern? 89 FDI in the post-EU accession Baltic Sea Region: A global or a regional concern? H. Richard Nakamura, Mikael Olsson and Mikael Lönnborg1 Abstract This paper investigates the dynamics of FDIs in the Baltic Sea Region (BSR) by applying the Poisson Pseudo-Maximum Likelihood estimation method on a gravity model. In particular, we analyze the influence of macro and spatial factors on investment stock changes and discuss whether the origin of these investments and the 2004 EU enlargement have had any effects on BSR FDIs. Our results suggest that EU enlargement has been significant for FDI activity in the region, and that FDI is basically a regional issue as it tends to be bilateral within the region. However, the same results also suggest that geographic distance is not a significant factor. We conclude that while being traditional in nature, the BSR FDI pattern is undergoing changes towards a lesser degree of geographic bias. Keywords: Baltic Sea Region, FDI, Trade, Gravity Model, Poisson Pseudo-Maximum Likelihood method. JEL classification: C21, C23, F21, F23, R12 1. Introduction Since the EU accession of Estonia, Latvia, Lithuania and Poland, political rhetoric has envisioned a bright future for the overall development and integration of the Baltic Sea Region (BSR). However, the integration process had been under way long before the Baltic states and Poland became full EU members, and the integration of these countries into the European common market has recently been impeded by the global recession, where the Latvian experience embodies the vulnerability of small open economies in times of international financial crises. Earlier research (e.g., Petri, 1994; UNCTAD, 1996) has shown that trade and foreign direct investment (FDI) intensity are closely associated. However, on the global level, investment distribution is uneven and the BSR is no exception. As seen in Figure 1, investment flows in the BSR are up until 2008 primarily directed from the top industrialized countries on the western shores of the Baltic Sea to the eastern BSR countries, which regionally have cost advantages in all or some input resources for industrial or service production, or being emerging consumer markets close to larger EU countries with a population gaining increasing purchasing power. 1 Centre for Baltic and East European Studies (CBEES), Södertörn University, Södertörn University, SE-141 89 Huddinge, Sweden. Tel: +46-8-608 5008, Fax: +46-8-608 4170. Main author, H. Richard Nakamura, e-mail: [email protected]. The present study and article was prepared, analyzed and written by the main author. Baltic Journal of Economics 12(2) (2012) 89-108 90 Figure 1. Annual inward FDI in the BSR between 1992-2008; % of total world net inflows. 20 18 16 14 BSR-W 12 BSR-E 10 17,9 6,9 5,1 1,1 2,1 1,5 1,7 1,5 2007 0,9 2006 0,8 2005 0,8 2003 0,8 2004 1,2 2002 1,2 2001 1,3 1998 0,4 1997 1995 7,3 7,6 5,7 1996 1994 1993 1992 0,8 0,9 1,2 0 0,5 0,9 12,7 11,9 5,8 3,6 3,1 2 9,1 2000 07,8 4 9,3 1999 6 4,4 1,3 2008 8 Note: BSR-W: Germany, Denmark, Sweden and Finland. BSR-E: Estonia, Latvia, Lithuania and Poland. Source: UNCTAD and own calculations. The statistics between 1992 and 2008, as summarized in Figure 1, indicate a development from an initial boom to a more modest flow of inward investment, suggesting that the Baltic states are losing their comparative advantage in production factor costs. However, this is not the only explanation. Concurring with increased loss of production cost advantages, the purchasing power of host country consumers has increased substantially, which means that the investment motives of the multinational corporations (MNCs) in the Baltic states changed over that period. This in turn might lead to market-oriented FDIs, which, by requiring less investment funds in absolute terms, affects total net FDI inflows downwards. At the very end of the period under analysis in this paper, the global financial crisis occurred, which also affected international FDI flows negatively. Figure 2. Cumulative inward FDI per capita between 1992-2008 (USD in current prices). 35 000 32 058 30 000 25 000 20 000 18 138 17 811 11 632 10 689 4 257 3 573 3 297 Lithuania Germany Estonia BSR-8 /avg./ Finland BSR-W /avg./ Denmark Sweden 0 5 454 Poland 7 342 5 000 Latvia 10 000 BSR-E /avg./ 13 707 15 000 Note: The averages reported for the region as such (BSR-8) and the two constituent parts (BSR-W and BSR-E) are unweighted averages. Source: UNCTAD and own calculations. FDI in the post-EU accession Baltic Sea Region: A global or a regional concern? 91 Decomposition of FDI statistics into country level data will reveal that the per capita value of foreign investment in the BSR area (in cumulative terms) is spread unevenly between mature economies and former transition economies. From Figure 2, it is possible to conclude that the largest value of investment stocks tends to be found in the established mature economies of the western BSR (Germany scoring relatively less in the Figure 2 ranking since the population size is substantially larger than the rest of the BSR countries). Another illustration of the size of FDI in the region is our compilation of all cross-border mergers and acquisitions (M&As) over USD 1 million in the BSR between 1998 and 2010 (See Appendix Tables A1 to A6), which gives an impression made by a few “pure” intra-BSR cross-border M&As (for example, 35% of all cross-border M&As taking place in the BSR between 1998-2010 were between BSR firms [source: Zephyr M&A database]) to the list of major international M&A deals in the region. This diverse picture is indicative of the unclear knowledge we have on contemporary FDI activity in the Baltic Sea Region. The general purpose of this paper is to investigate and analyze the dynamics of FDIs in the BSR. In particular, this paper verifies the origin and target country directions of FDIs in the region. We will do so by an econometric analysis of the influence of gravity factors on contemporary investment flows, proxied by the inward FDI stocks of the 8 BSR countries of the region, and will discuss whether the geographic origin of these investments and the EU accession of the Eastern Baltic countries are of any importance when discussing regional integration and investment promotion in the BSR. Since EU accession is a central event for this analysis, we limit our study to the period between 2000 and 2008, that is, four years prior to and four years after the accession year of Estonia, Latvia, Lithuania and Poland. Thus, the data obtained cover the immediate pre-accession period to the start of the global financial crisis of 2008-2009. 2. Theoretical framework and earlier research Transaction costs in international trade have been a factor traditionally dealt with more or less pragmatically in order to lift them off the basic models of international trade (e.g., Dixit and Norman, 1980; Jovanović, 2006; Eicher et al., 2009). This has led to trade models with strong assumptions, where the purpose has been to theoretically argue for the basic benefits of international trade rather than to let the models describe a factual reality. If transaction costs and other institutionally related costs are introduced into the theoretical models, the geographic bias observed in trade statistics can also be better explained theoretically, provided the assumption holds that the longer the geographical distance, the more expensive the transport costs. If we presume the mainstream argument that trade and investment follow each other closely (see, for example, Petri, 1994; UNCTAD, 1996), a regional bias similar to those found in trade patterns should also be expected for investment flows. Earlier studies (e.g., Petri, 1994; see also Blomström and Kokko, 2003) suggest that trade flows are more volatile than FDI, which is less surprising since trade is more sensitive to short-term institutional changes in the world trade environment such as exchange rates, war, trade conflicts and trade partners’ domestic political factors, as compared to FDI, which tends to be a more medium- or long-term commitment. Then what are the determinants of FDI? This question can be considered a classic area in the FDI literature, and in their early contributions, Penrose (1959) and Hymer (1960 [1976]) argue that opportunity-seeking behavior predominates internationalizing firms’ investment decisions, forwarding the idea that firms 92 Baltic Journal of Economics 12(2) (2012) 89-108 make their first international expansion when the home market has become mature and the competition has driven profits toward zero – in other words, a situation well accommodated within the framework of neo-classic theory. More recent literature on FDI determinants (e.g., Dunning, 1992; Shapiro and Globerman, 2001) focuses on features connected to host country market attraction and production cost advantages, such as the size of the market, strong purchasing power, education level, infrastructure standards, trade policies, exchange rates and political and macroeconomic stability. In general, increased global competition, liberalization of FDI regimes, technological and logistic advances have changed the conditions for firms of any origin in doing FDI (Sauvant et al., 2009). As a natural consequence of this development, the outward investment behavior of emerging market MNCs has increased dramatically, possibly making the validity of the mainstream assumptions of FDI determinants questionable. For example, Buckley et al. (2007) argue that capital market imperfections, diversification and strategic asset-seeking in institutional environments resembling home country conditions are strong influential factors for Chinese firms’ foreign investment decisions. Similar outward FDI determinants have also been found for Indian firms (Pradhan, 2004). Even if we are discussing primarily macro aspects of FDIs in this paper, some words on motives influencing FDI decisions are necessary for our theoretical discussion. Dunning (1992) has made important contributions to this literature, and has spelt out four different motives for outward FDIs: (Natural) Resource-seeking, Market-seeking, Efficiency-seeking and Strategic Asset-seeking. The first FDI group aims to acquire all forms of input – physical as well as intangible – needed for production. The second FDI group aims for sale and supply of goods and services to other markets. This type of FDI motive is typically triggered as a result of strategic decisions by investing firms to deepen the commitment in the markets they have served earlier primarily by exports (see, for example, Johansson and Vahlne, 1977), by, e.g., starting host country production. The third type of FDI motive, that is, Efficiency-seeking, is denoted by Dunning (1992) as a desire among established firms to utilize economies of scale and scope to, for example, concentrate production in locations associated with internationally competitive production costs, factor endowments, institutional arrangements, and the like. Finally, the fourth FDI motive aims to sustain the investing firm’s long-term strategic objectives of being globally competitive (see Barney’s [1991] discussion of “sustained competitive advantage” of firms), through measures such as acquisition of brands and technology (including intellectual property rights). If we assume the beneficial effects from trade firstly in terms of improved terms-of-trade and secondly in terms of improvement of real exchange rates which increases the purchasing power of the importing country, the benefits to the importing country as a market-seeking FDI destination are obvious. The situation for efficiency-seeking FDI might be the opposite. All other things being equal, the initial advantages of locating production in countries with low production costs can over time, as the overall wealth of the host country increases and spillover effects materialize, switch to a situation where production turns disadvantageous (as it has done in, e.g., Estonia). On the other hand, if upgrading of host country production factors occurs, a production location in the host country might still be advantageous from the efficiency viewpoint, due to a shift to skill-intensive production (as observed by, e.g., Görg and Strobl, 2003). Furthermore (and closely connected to market-seeking type FDI), FDI in the post-EU accession Baltic Sea Region: A global or a regional concern? 93 the lifespan of efficiency-seeking FDI is also affected by which end markets the products produced are sold in, i.e., whether these products are sold domestically in the host country or exported (or both). Thus, the sustainability of efficiency-seeking FDI is heavily dependent on how the foreign investor manages reinvestment in existing FDI stocks in the host country. For resource-seeking FDI, the relationship might be similar to efficiency-seeking FDI that aims for exports, as the investor home country typically imports resources extracted in the FDI host country and in this way increases trade between host and home countries. Finally, strategic asset-seeking FDI implies a home country (long-term) claim on the host country, thus contributing to an improved balance-of-payment position of the importing home country vs. the exporting host country. The theoretical literature exemplified by, e.g., Lipsey’s (2006) discussion, argues that facilitated access to a country’s main markets and resulting increase in trade flows should lead to increased growth and wealth, which in turn makes the country an attractive host for FDIs. This discussion should also be contrasted to the discussion by, e.g., Hummels (1999), who argues that geography is still an important factor for today’s trade and investment. Taken together, there are strong theoretical reasons to believe that economic integration of neighboring regions might foster increased FDI activity. As stated above, a gravity model is employed in this paper for the empirical analysis. Originally suggested in the 1960s by, e.g., Tinbergen (1962), the gravity model has since been used in International Trade studies for analyses of influences of various impeding factors on international trade flows (e.g., Bergstrand, 1985; Broadman, 2005; Flam and Nordström, 2010; Paas, 2000; Paas and Tafenau, 2005; Santos Silva and Tenreyro, 2003). Inspired by these developments in the trade analysis field, a number of researchers have also proposed and attempted to transfer the same analysis method to studies on FDI flows and stocks. The geographic focus of these gravity analyses on FDI flows and stocks has, e.g., been studies on FDI and trade flows between Europe and Latin America (e.g., Africano and Magalhães, 2005), post-cold war Eastern European economic integration (e.g., Christie, 2003), or FDI flows in China (e.g., Marchant and Peng, 2004) and Southeast Asia under the ASEAN regime (e.g., Stone and Jeon, 1999). However, to the best of the authors’ knowledge, no similar studies have been made so far for FDIs in the BSR region. By emphasizing the overall importance of distance in bilateral economic relationships between countries, these attempts to emulate use of trade gravity models onto FDI analyses have in general confirmed theoretical discussion by, e.g., Krugman (1991) of the so-called new economic geography school. In other words, the FDI gravity model estimation results of these studies have suggested the significance of “resisting” factors influencing overall investment between two countries vis-à-vis the total FDI sample. In the following sections, we will explain our adaptation of the traditional gravity model to an FDI context. 3. Model Following the traditional application of the gravity model (e.g., Paas and Tafenau, 2005; Santos Silva and Tenreyro, 2003, 2006), an FDI gravity model that included GDP, GDP per capita, trade and distance as independent variables was adopted. Furthermore, in order to address the time trend inherited in the cross-section time series, a time variable is also included in the regression model. 94 Baltic Journal of Economics 12(2) (2012) 89-108 Formalizing the main gravity model used in this study, we obtain lnFDIijt = β0 + β1 lnGDPit + β2 lnGDPjt + β3 lnGDPCit + β4 lnGDPCjt + (1) β5 lnTradeijt + β6 lnDistanceijt + β7 Time + β8 DEU + β9 DBSR + lnεijt where lnFDI = Total bilateral FDI stocks between home country i and host country j transformed into natural logarithms lnGDP = Gross Domestic Product transformed into natural logarithms lnGDPC = GDP per capita transformed into natural logarithms lnTrade = Total bilateral trade between home country i and host country j transformed into natural logarithms lnDistance = Distance between home country i and host country j transformed into natural logarithms Time = Time trend variable (2000 = 1, …, 2008 = 9) DEU = EU accession dummy K transformed into natural logarithms (K = 0 if 20002003 period, K = 1 if 2004-2008 period) DBSR = BSR country dummy K transformed into natural logarithms (K = 0 if non-BSR country, K = 1 if BSR country) ln ε = Error term i = Home country i j = Host country j t = time period t. As seen, the variables chosen for our FDI gravity model (1) follow the standard pattern for most analysis applications in trade and FDI research (e.g., Africano and Magalhães, 2005; Ledyaeva and Linden, 2006; Paas, 2000; Paas and Tafenau, 2005; Petri, 1994; Santos Silva and Tenreyro, 2003; Stone and Jeon, 1999). As the dependent variable, total FDI stocks are chosen as a measure for FDI due to better data availability for all countries and years in the sample. The independent variables are the gross domestic product (GDP) of countries i and j, GDP per capita (GDPC) of countries i and j, geographic distance between countries i and j (Distance) and total value of trade flows between countries i and j (Trade). Since we are using cross-section time series data, there are obvious risks for various factors inherited in the data that might result in spurious estimations, making regression results unreliable. In order to detect the presence of unit roots and non-stationarity in the data, Dickey-Fuller tests were executed on the estimated residuals and all dependent and independent variables (except for Distance). The test results2 allowed us to reject the null hypothesis of the existence of a unit root as observed values exceeded critical DF values at the 5% level. Thus, no tests for variable cointegration were performed due to the indicated absence of unit roots in Dickey-Fuller tests (e.g., Studenmund 2011). In addition to this set of basic regressors, dummy variables were introduced. The purpose of these dummy variables was to estimate 1) the statistical significance of changes in the FDI pattern from the 2004 accession of the four Eastern European countries Estonia, Latvia, Lithuania and Poland to the EU, and 2) the propensity for FDI in the BSR to be a “regional” issue (i.e., that BSR FDI occurs between countries located around the Baltic Sea). Some notes on the interpretation of the dummy parameters have to be made here, since we have transformed the dummy variable K into natural logarithms in order to maintain the consistency 2 DF tests are not reported here due to space constraints. FDI in the post-EU accession Baltic Sea Region: A global or a regional concern? 95 of the estimation model. Obviously, this makes a traditional interpretation of the dummy parameters misleading and the parameter estimation has to be antilogged in order to obtain an estimated magnitude of the influence of the dummy parameters on the dependent variable (Giles, 2011a, 2011b). Therefore, the estimations for the dummy variables were antilogged in the final analysis in order to obtain correct interpretations. Table 1. Summary of the variables of the main gravity model. Variable FDIij Variable type Dependent Description Total bilateral FDI stocks of home country i and host country j transformed into natural logarithms Expected sign Data source OECD WIIW Eurostat Home GDPi Independent GDP of the home country i transformed into natural logarithms + OECD Eurostat Host GDPj Independent GDP of the host country j transformed into natural logarithms + OECD Eurostat Home GDPCi Independent GDP per capita of the home country i transformed into natural logarithms + OECD Eurostat Host GDPCj Independent GDP per capita of the host country j transformed into natural logarithms + OECD Eurostat Tradeij Independent Total bilateral trade between home country i and host country j transformed into natural logarithms + IMF DOTS EcoWin Distanceij Independent - Google Earth Time Independent DEU Dummy DBSR Dummy Total distance (in km) between home country i and host country j transformed into natural logarithms Time trend variable (2000 = 1, …, 2008 =9 EU accession dummy K transformed into natural logarithms (K = 0 if 20002003 period, K = 1 if 2004-2008 period) Home and host BSR country dummy K transformed into natural logarithms (K = 0 if non-BSR country, K = 1 if BSR country) Note: For currency conversion purposes between WIIW, Eurostat, IMF DOTS and OECD data sets, annual average exchange rates for EUR, GBP and USD were obtained from the Furthermore, some important points regarding estimation problems with the gravity model require discussion. In the past, mostly ordinary least square (OLS) estimations have been used to estimate gravity models. Recent contributions (e.g., Santos Silva and Tenreyro, 2006; Flam and Nordström, 2010) point to bias problems in the presence of heteroskedasticity. This makes estimations less reliable or, in the worst case, even misleading due to the different weights given to the observations in a sample. Santos Silva and Tenreyro (2006) point to the fact that “the expected value of the logarithm of a random variable is different from Baltic Journal of Economics 12(2) (2012) 89-108 96 the logarithm of its expected value” (Santos Silva and Tenreyro, 2006: 641), which has been ignored in past gravity analyses in economics. Instead, the authors suggest employing the Poisson Pseudo-Maximum Likelihood (PPML) estimation method, which gives equal weight to the observations in a sample. Through Monte Carlo simulations, Santos Silva and Tenreyro (2006) show that estimations yielded from the PPML method were robust to different patterns of heteroskedasticity. Being aware of the bias risk from heteroskedasticity in OLS estimations of gravity models, we will follow Santos Silva and Tenreyro (2006) and use the PPML method for this study. All estimations and test statistics were obtained with a Stata 11 statistical package. 4. Data sources and descriptive statistics We define the BSR as the EU member countries surrounding the Baltic Sea, i.e., Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Poland and Sweden. Furthermore, in order to control for major FDI countries outside this region, the main world FDI countries that are OECD members (i.e., US, UK, France and Japan) are also included in the sample. In total, this sample makes 12 countries, covering FDI (defined as the FDI stock of each sample country), GDP, GDP per capita and trade between 2000 and 2008 (i.e., 4 years before and 4 years after EU accession by the three Baltic states and Poland). Finally, distance, defined as the geographic distance between the capitals of each bilateral relationship, was used as a proxy for the physical distance between the sample countries. Table 2. Descriptive statistics (after conversion of variables to natural logarithms except for the time trend variable) Variable year country lnFDI_stock lnhome_GDP lnhost_GDP lnhome_GDPC lnhost_GDPC lntrade lndistance EU_acc_dummy BSR_dummy time Obs 1080 1080 1041 1080 1080 1080 1080 1080 1080 1080 1080 1080 Mean 2004 6.1 7.142286 26.409601 26.409601 9.887204 9.879565 7.782435 7.281833 0.7222222 0.5333333 5 Std. Dev. 2.583185 3.351676 2.713552 2.204863 2.204863 0.8487946 0.8550506 1.846522 1.143067 0.4481107 0.4991188 2.583185 Min 2000 1 -2.67 22.45947 22.45947 8.09 8.09 2.56 4.43 0 0 1 Max 2008 12 12.8 30.27672 30.27672 11.04 11.04 12.36 9.1 1 1 9 Since the quality and availability of macro data for the sample countries (especially for the three Baltic states) vary, the data used in this study were obtained and compiled from the following sources: OECD, The Vienna Institute for International Economic Studies (WIIW), IMF Direction of Trade Statistics (IMF DOTS), Eurostat, and EcoWin. For currency conversion purposes when transforming EcoWin and WIIW data, denoted in Euro, into US Dollars (current prices) in order to make all data comparable, annual average exchange rates for Euro, British Pounds and US Dollars were obtained from the ECB. Distances between sample country capitals were obtained through the Google Earth distance measurement tool. FDI in the post-EU accession Baltic Sea Region: A global or a regional concern? 97 Table 2 presents the descriptive statistics for the sample, which contains 1080 observations. Of these, 39 observations for FDI stocks were negative, and deleted during conversion to natural logarithms. This left us a total of 1041 observations. Since the PPML method only allows for zero or positive values of natural logarithms, another 14 observations were omitted, which left us with a total of 1027 observations to be estimated through the PPML method. 5. Results Table 3 reports the cross-section estimation results of the basic version of the main gravity model (i.e., without dummies for EU accession and home-host country location) for each of the years 2000 to 2008. As stated earlier, due to bias problems in using OLS in gravity model estimations (see Santos Silva and Tenreyro, 2006) and detection of error term heteroskedasticity in our OLS estimation3, we used the PPML method for this analysis. The first item to note is the overall significance of the estimated models. Secondly, all variables except for the GDP variable have the theoretically expected signs. For all individual years, trade tends to be the most influential variable when estimating FDI dynamics. This is also confirmed in a correlation analysis, where the partial correlation between the Trade variable and the dependent variable was 0.796 at the 1% level4. This follows the FDI theory outlined and discussed in UNCTAD (1996), Blomström and Kokko (2003) and Lipsey (2006), and the findings of, e.g., Petri (1994). What is seemingly more theoretically puzzling is the negative sign of the GDP variable. Mainstream theory would suggest such a variable would yield a positive sign. We interpret this unexpected sign by the very nature of the BSR FDI pattern; when compared to the largest foreign direct investor countries in the world, the main share of FDI activity in the BSR is made by firms from small open economies investing in other small open economies of the BSR. Therefore, as one of two control variables for size effects (i.e., GDP and GDPC) included in the classic form of gravity models, the negative sign of GDP has to be considered together with GDPC estimations. Turning our attention to the size of the home and host economies measured as GDP per capita also gives some indication of the importance of this factor for the build-up of BSR FDI stocks. This result is rather intuitive, since the increasing wealth of a country, all other things being equal, tends to increase the average income level of the population, which in turn makes host markets interesting for foreign investors. However, we should not forget that FDI is global in scope, directed by business and profit opportunities identified by investing firms no matter where the location, which means that what we have observed here is not unique for the BSR; we will return to this observation later in our continued discussion. Finally, regarding geographic distance as a theoretically impeding factor for trade and FDI location (e.g., Hummels, 1999; Krugman, 1991), this study supports the findings of, e.g., Stone and Jeon (1999) that distance is a statistically insignificant factor when determining the location propensity of FDI. This is also a highly interesting result, suggesting that distance is a secondary factor to FDIs in the BSR. 3 4 Tests are not reported here due to space constraints. Partial correlations are not reported here due to space constraints. Baltic Journal of Economics 12(2) (2012) 89-108 98 Table 3. PPML estimation results of basic FDI gravity equations from 2000 through 2008. 2000 2001 2002 0.643 0.952 1.000 (0. 917) (0.908) (0.900) ln(Home GDP) -0.045 -0.061** -0.066** (0.030) (0.031) (0.031) ln(Host GDP) -0.046 -0.062** -0.065** (0.030) (0.030) (0.030) ln(Home GDPC) 0.095*** 0.110*** 0.119*** (0.031) (0.034) (0.034) ln(Host GDPC) 0.098*** 0.113*** 0.119*** (0.032) (0.036) (0.036) ln(Trade) 0.201 0.223*** 0.229 (0.035) (0.033) (0.035) ln(Distance) -0.028 -0.035 -0.0367 (0.041) (0.040) (0.039) Pseudo R2 0.181 0.196 0.187 Log Pseudolikelihood -219.750 -233.501 -230.822 No. of observations (= n) 110 116 114 Coefficients with robust standard errors within parenthesis. *, ** and 10%, 5% and 1% levels respectively. Constant 2003 2004 2005 0.225 -0.560 -1.056 (0.785) (0.602) (0.729) -0.020 -0.006 0.000 (0.027) (0.018) (0.020) -0.019 -0.003 0.002 (0.024) (0.019) (0.021) 0.075*** 0.097*** 0.114*** (0.023) (0.025) (0.032) 0.074*** 0.080*** 0.105*** (0.024) (0.025) (0.031) 0.174*** 0.140*** 0.126*** (0.030) (0.020) (0.020) -0.012 -0.025 -0.035 (0.038) (0.026) (0.030) 0.160 0.144 0.138 -230.087 -228.301 -233.606 114 114 116 *** represents statistical significance at the Table 3. Continued. Constant ln(Home GDP) ln(Host GDP) ln(Home GDPC) ln(Host GDPC) ln(Trade) ln(Distance) 2006 -0.594 (0.552) 0.001 (0.017) 0.003 (0.018) 0.084*** 2007 -0.261 (0.434) -0.009 (0.015) -0.007 (0.017) 0.084*** 2008 -0.098 (0.406) -0.021 (0.013) -0.019 (0.014) 0.092*** (0.028) 0.078*** (0.029) 0.131*** (0.017) -0.031 (0.026) 0.126 -233.182 (0.028) 0.080*** (0.029) 0.138*** (0.014) -0.014 (0.024) 0.115 -234.044 (0.025) 0.081*** (0.024) 0.161*** (0.015) 0.008 (0.021) 0.107 -222.386 Pseudo R2 Log Pseudolikelihood No. of observa116 116 111 tions (= n) Coefficients with standard errors within parenthesis. *, ** and *** represents statistical significance at the 10%, 5% and 1% levels respectively. Turning our attention to the cross-section time series version of the same data set, we can make more interesting observations. In Table 4, we can see that besides the fact that OLS estimators systematically overestimate the coefficients for the independent variables compared FDI in the post-EU accession Baltic Sea Region: A global or a regional concern? 99 to PPML estimators5, the PPML parameters of all independent variables are also statistically significant except for the Distance variable. The signs of the parameters follow the pattern from the cross-section estimations reported in Table 3. Table 4. OLS and PPML estimation results of the FDI gravity model for the entire period, controlled for EU accession and geographic proximity. Model 1: OLS estimation of the main gravity model for the period 2000-2008 Constant -6.674*** (1.423) ln(Home GDP) -0.172*** (0.047) ln(Host GDP) -0.156*** (0.046) ln(Home GDPC) 0.653*** (0.068) ln(Host GDPC) 0.597*** (0.068) ln(Trade) 1.301*** (0.051) ln(Distance) 0.023 (0.075) Time -0.062*** (0.016) DEU DBSR Model 2: PPML estimation of the main gravity model for the period 2000-2008 -0.063 (0.221) -0.023*** (0.007) -0.021*** (0.007) 0.100*** (0.010) 0.094*** (0.010) 0.166*** (0.008) -0.003 (0.010) -0.009*** (0.002) Model 3: PPML Gravity model controlling for 2004 EU accession -0.211 (0.233) -0.021*** (0.007) -0.022*** (0.007) 0.110*** (0.011) 0.101*** (0.010) 0.167*** (0.008) -0.000 (0.010) -0.015*** (0.002) -0.061*** (0.016) Model 4: PPML Gravity model controlling for home and host country being a BSR country -0.235 (0.241) -0.020*** (0.008) -0.018** (0.008) 0.100*** (0.010) 0.094*** (0.010) 0.164*** (0.008) 0.003 (0.010) -0.010*** (0.002) -0.033*** (0.013) Model 5: PPML Gravity model controlling for 2004 EU accession and home and host country being a BSR country -0.317 (0.246) -0.019** (0.008) -0.020*** (0.008) 0.109*** (0.011) 0.100*** (0.010) 0.166*** (0.008) 0.004 (0.010) -0.015*** (0.003) -0.057*** (0.016) -0.022* (0.013) Adj R2 0.828 Pseudo R2 0.156 0.156 0.156 0.156 Log Pseudolike-2070.025 -2068.620 -2069.649 -2068.457 lihood No. of observa1041 1027 1027 1027 1027 tions (= n) Coefficients with standard errors within parenthesis. *, ** and *** represent statistical significance at the 10%, 5% and 1% levels respectively. However, the estimation results of the two dummy variables DEU and DBSR tell us an interesting story. As expected, EU accession per se did have a statistically significant influence on changes in FDI stocks. In the PPML estimation models 3 and 5 (see Table 4), the EU accession dummy yielded statistically significant results suggesting that, holding all other variables constant, post-accession FDI stock levels were on average 6% higher than prior to EU accession. The location dummy for BSR countries’ FDI stocks (models 4 and 5 of Table 4) yield a Probably due to the presence of heteroskedastic errors in the sample; the magnitude of this bias can be easily controlled for by comparing the elasticities (i.e., the coefficients) of the OLS and PPML estimations, respectively. The formula to compute the percentage effects is (eβi – 1) x 100, where βi is the coefficient estimated (Santos Silva & Tenreyro, 2006: 651). 5 100 Baltic Journal of Economics 12(2) (2012) 89-108 statistically significant coefficient, indicating that, holding all other variables constant, intraBSR FDI stocks are on average between 2% and 3% higher than FDI stocks held by firms from the major industrialized countries outside the BSR. All in all, the PPML estimations of the FDI gravity model by and large confirm the “common wisdom” of the theoretical and empirical literature on FDI cited in this paper, with two interesting exceptions: the negative sign of the GDP variable, and the statistically insignificant parameter estimation for the Distance variable in combination with the statistically significant BSR location dummy variable. We will now move on to interpret the PPML estimation results in the next section. 6. Discussion and conclusions In this study, we used a gravity model to analyze recent FDI trends in the BSR measured by changes in FDI stocks in bilateral relationships between countries in and outside the BSR. First of all, we would like to forward the methodological contribution of this paper to the FDI literature, namely the econometric analysis on macro data using the Poisson PseudoMaximum Likelihood estimation method, which has been demonstrated by Santos Silva and Tenreyro (2006) to be unbiased and robust to different patterns of heteroskedasticity. This would imply the appropriateness of using PPML estimations for our sample here, by being the most consistent estimation method for gravity analyses of the type presented in this article. This has, to the best of the authors’ knowledge, not yet been done on BSR FDI data, and leads us to forward our next contribution to the literature on BSR FDI, which is to show that the close connection between trade and FDI discussed in the theoretical literature also has its validity for the BSR setting. Earlier gravity model analyses have stopped at studying the relationship between trade and distance, and done so by using OLS for their estimation (e.g., Paas, 2000; Paas and Tafenau, 2005). Here, we present a variant of the gravity model that also includes FDI. The findings of our study are expected in line with Petri’s (1994) and Hummels’ (1999) discussions and earlier, similar, studies on FDI and trade (e.g., Africano and Magalhães, 2005; Bergstrand, 1985; Broadman, 2005; Christie, 2003; Ledyaeva and Linden, 2006; Santos Silva and Tenreyro, 2003; Santos Silva and Tenreyro, 2006), i.e., the estimation results suggest trade volume, the size of home and host economies and the location of investing countries as important factors for understanding foreign direct investment activity in the Baltic Sea Region. The assumption of investing firms’ general preference for countries and cultures close to their home countries – at least in the early stages of an internationalization process – is something that we recognize from mainstream theory, such as the early theories on FDI and firm internationalization (see, for example, Johanson and Vahlne, 1977; Dunning, 1977, 1979). This “basic paradigm” of early FDI theories has since been challenged by the current development pattern of global FDI, where we can observe firms such as the so-called “born globals” and where physical distance seems to play a lesser role in investment decisions (e.g., UNCTAD, 2011) for the benefit of other investment incentives such as market size, costs of obtaining resources and low production costs that might be decisive for investment decisions. This could be the explanatory factor behind the statistically insignificant results for the Distance parameters in combination with the other, statistically significant, parameters. Large physical distances per se might have become less of an effective deterrent for foreign direct investors. FDI in the post-EU accession Baltic Sea Region: A global or a regional concern? 101 In the midst of all this, our results also suggest that we should not abandon or forget that the “basic paradigm” is still valid, and that the “traditional” FDI pattern lingers on at least in the BSR in the sense that foreign direct investments in the BSR tend to be between countries within the same region. FDIs follow the trade pattern very closely in the BSR, which is associated with the location of the principal trade partners of BSR countries. The answer to the title of this paper is therefore: “For the BSR, FDI is indeed a regional issue rather than a global concern” – with the addition “but this pattern is undergoing changes”. Thus, we can conclude that the FDIs taking place in the BSR are still characterized as being an “internal affair” of the region, which is also correlated to the overall trade pattern of the area. The imports of eastern BSR countries are dominated by products and services produced in western BSR countries or produced by firms owned by western BSR MNCs, and that onethird of all exports of the eastern BSR go to western BSR countries (Olsson et al., 2010). It is also interesting, and highly relevant, to reflect our results with what is said in EU political circles in connection with the EU accession of the eastern BSR countries. The political rhetoric of hope and intentions prior to 2004, and manifested in the so-called EU Baltic Sea Region strategy in 2009 (Baltic Development Forum, 2009), envisioned significant effects and dynamism in economic integration, trade and FDI from the EU enlargement project. This is also what we would expect from the FDI literature (e.g., Lipsey, 2006). We might well use our results to discuss BSR economic integration also from a somewhat different viewpoint than those held by, e.g., policymakers in the BSR, who were hailing the EU accession of the Baltic states and Poland as historic for BSR economic development. We are not arguing against this fact as such. However, we would like to point to the statistically insignificant result of the Distance variable, which normally yields a negative parameter in gravity models. In combination with the statistically significant dummy parameter result for EU accession, we would suggest that FDI decisions are more dependent on, e.g., FDI regimes, trade regimes and production cost advantages rather than geographical closeness (i.e., the distance) of markets and production locations. Blomström and Kokko (2003) argue in their overview of the FDI literature that the existence of other basic economic foundations concurring with regulatory reforms and political rhetoric are crucial to make firms carry out FDI and host economies benefit from positive FDI spillovers. The eastern BSR countries are still in their “catching-up” transition process, albeit growing at a slower pace now than during the 1990s, and their market potential, defined as the overall purchasing power of the consumers in these markets, continues to attract FDIs into the region. Furthermore, the economic growth and development of the eastern BSR countries also spills over to the flow of outward FDIs of eastern BSR firms, which have increased steadily during the last 15 years. In conclusion, policymakers in countries struggling to attract inward FDIs should be aware of the non-perpetual and “liquid” nature of FDI, especially in the manufacturing sector. As inward FDIs in the BSR are dominated by investments in the manufacturing sector, this particular nature of FDI in the region should encourage governments to formulate realistic FDI policies attractive for foreign investors, aiming for sustained long-term retention of foreign investment in, e.g., non-manufacturing sectors. Contemporary global investor groups are not necessarily of EU origin, and BSR policymakers staying comfortable with inward FDI pro- 102 Baltic Journal of Economics 12(2) (2012) 89-108 motion programs based on freedom of labor and capital within the EU might miss out factors which foreign investors consider more important than geographic home country closeness and free factor movements. Suggestions for Future Research This paper is intended as a study of longitudinal dynamics in the FDI pattern of the BSR, explained by selected macro and spatial factors and estimated by using the Poisson PseudoMaximum Likelihood method. By doing this study, we have opened up a number of possible developments for this research. One is, as suggested by, e.g., Nakamura (2005), that cross-border investment in the form of M&A tends to follow the overall M&A trend in the particular economy, that is, a tendency for behavioral isomorphism in the organizational field that the companies belong to (i.e., that firms are exhibiting a “follow-the-herd” mentality). The question is whether such behavior is also observable in the BSR. Another phenomenon that should be investigated in a BSR setting is institutional reasons for foreign companies to invest in a particular BSR economy that is not geographically close to the home country of the investor. Furthermore, in this study we only observed a small temporal “window” in the history of BSR FDI; to study the influence of firm-specific historical, path dependent, factors is probably still an alternative way to study historical FDI trends in the BSR. 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World Investment Report: Non-Equity Modes of International Production and Development. New York and Geneva: UNCTAD. Zephyr (2011). Zephyr Global M&A database. Brussels: Bureau van Dijk. Source: Orbit/Zephyr M&A database. *Asterisks denote deal value estimations only. aIPOs and JVs not included. bCountry codes: EE=Estonia, FI=Finland, SE=Sweden. Table A2. Cross-border M&A deals in Estonia worth over USD 1 billion 1998-2010a, b. Source: Orbit/Zephyr M&A database. *Asterisks denote deal value estimations only. aInitial Public Offers (IPOs) and Joint Ventures (JVs) not included. bCountry codes: AT=Austria, BH=Bahrain, DK=Denmark, FR=France, GB=Great Britain, IS=Iceland, LU=Luxemburg, NL=Netherlands, NO=Norway, SE=Sweden, US=USA. Table A1. Cross-border M&A deals in Denmark worth over USD 1 billion 1998-2010a, b. 106 Baltic Journal of Economics 12(2) (2012) 89-108 Britain, IT=Italy, PL=Poland. Source: Orbit/Zephyr M&A database. *Asterisks denote deal value estimations only. aIPOs and JVs not included. bCountry codes: AT=Austria, DE=Germany, FR=France, GB=Great Table A5. Cross-border M&A deals in Poland worth over USD 1 billion 1998-2010a, b. Source: Orbit/Zephyr M&A database. *Asterisks denote deal value estimations only. aIPOs and JVs not included. bCountry codes: LT=Lithuania, PL=Poland. Table A4. Cross-border M&A deals in Lithuania worth over USD 1 billion 1998-2010a, b. Source: Orbit/Zephyr M&A database. *Asterisks denote deal value estimations only. aIPOs and JVs not included. bCountry codes: DK=Denmark, FI=Finland, GB=Great Britain, SE=Sweden, US=USA, ZA=South Africa. Table A3. Cross-border M&A deals in Finland worth over USD 1 billion 1998-2010a, b. FDI in the post-EU accession Baltic Sea Region: A global or a regional concern? 107 Source: Orbit/Zephyr M&A database. *Asterisks denote deal value estimations only. aIPOs and JVs not included. bCountry codes: AE=United Arab Emirates, AT=Austria, BH=Bahrain, CN=China, DE=Germany, DK=Denmark, FI=Finland, FR=France, GB=Great Britain, IS=Iceland, IT=Italy, NL=Netherlands, NO=Norway, SE=Sweden, US=USA. Table A6. Cross-border M&A deals in Sweden worth over USD 1 billion 1998-2010a, b. 108 Baltic Journal of Economics 12(2) (2012) 89-108