1 Introduction
In recent years, many observers in both policy and academic circles argue that foreign direct investment (FDI) is of growing importance for the economic performance of countries and regions as it plays a primary role in the global (re)organisation of production (WTO
1996; Dicken
2007; Yeung and Coe
2015; Iammarino
2018). In a firm perspective, Multinational Companies (MNCs) constantly make decisions that are related to the search for new locations, by acquiring or merging with other firms (Guadalupe et al.
2012; Ascani
2018) or by setting up entirely new plants abroad (Head and Mayer
2004). Considering that these decisions can have a significant economic impact on recipient locations (e.g. Bellak et al.
2008; Ascani and Gagliardi
2015), the attraction of FDI has rapidly gained centre stage in the agenda of policy makers all over the world. This primarily implies that countries and regions become more appealing for the activities of MNCs. In fact, a healthy and enabling environment for business is needed in order successfully attract global FDI, retain it over time and maximise the gains associated with its presence (OECD
2002). Indeed, a vibrant business environment encourages both domestic and foreign investment, it stimulates innovation and the accumulation of skills, as well as it contributes to a more competitive climate.
Although prior academic works have devoted a large effort to understand the entry choice of MNCs (e.g. Nocke and Yeaple
2007; Raff et al.
2009; Becker and Fuest
2011) as well as their role in fostering economic development, growth and innovation (e.g. Liu and Zou
2008; Wang and Wong
2009), most studies either focus on single-country cases or only concentrate on a narrow set of location determinants and mostly adopt an a‑spatial lens of analysis. In this context, the objective of this research is not only to provide novel insights into the factors that affect the location strategies of MNCs, but also to produce an integrated framework of analysis of MNCs’ location decisions of greenfield FDI and M&A, by building on and expanding the findings of recent contributions (e.g. Crescenzi et al.
2014; Ascani et al.
2016). By means of a quantitative analysis of the location factors that influence the geography of European MNCs’ investment projects within the EU over the period 2012–2017, therefore, this paper integrates in a unified theoretical and empirical framework the (i) traditional location factors of foreign investment with (ii) the innovation capabilities of regions, as well as (iii) their institutional contexts. Integrating these diverse elements also calls for a careful consideration of the heterogeneous spatial levels at which every pull factor of global FDI operates, as some locational determinants have an inherently local flavour while others are connected to country-level considerations (Iammarino and McCann
2013). Furthermore, the changing composition of FDI in the EU in recent years with the share of foreign firms producing manufacturing goods declining over time, and the number of foreign firms providing services increasing (Capello et al.
2011) requires a careful investigation of the different location choices by adopting a fine-sliced division of the different economic activities, as there can be remarkable differences in the location determinants of manufacturing plants and service facilities (Py and Hatem
2009).
This research, hence, aims at contributing to the current academic debate in at least four respects. First, it introduces a third set of explanatory variables (i.e. institutional factors) in the framework used by Crescenzi et al. (
2014), in consideration of the findings of Bartik (
1985), Ang (
2008), Bellak and Leibrecht (
2009) and Ascani et al. (
2016), who all provide evidence that the institutional environment of recipient locations matters for the location decision of MNCs. Hence, this research aims at providing a comprehensive analysis of the location determinants of MNCs’ investment projects. Second, the present analysis of FDI location determinants is not limited to greenfield projects, as customary in the literature on firm location choices, but we extend our reach by accounting for M&A. Previous contributions, in fact, suggest that the location determinants strongly differ according to the entry mode of the foreign company (e.g. Basile
2004). Third, this research provides insights at the local level, thus narrowing down the analysis within countries, by including data on NUTS3 level for multiple nations, as advocated by recent studies on MNCs and FDI (Iammarino and McCann
2013; Iammarino
2018). The existing academic literature on the location determinants of MNCs, instead, mainly consists of national-level studies (e.g. Devereux and Griffith
1998b; Cleeve
2008; Mohamed and Sidiropoulos
2010; Ascani et al.
2016). Besides, several contributions have included data on the subnational level for NUTS1 regions (e.g. Basile et al.
2008) or NUTS2 (e.g. Cantwell and Piscitello
2005; Crescenzi et al.
2014), or US subnational units (e.g. Head et al.
1995,
1999). Only a limited number of studies employ data at a lower geographical level than NUTS2, including Guimaraes et al. (
2000) and Crozet et al. (
2004) who provide an analysis of the location choices of MNCs using data on NUTS3 regions for Portugal and France, respectively. However, there is a relevant lack of empirical evidence employing such a geographical level of data refinement for multiple countries or for political and economic unions comparable to the EU. Hence our research also aims at filling this gap by providing insights about the location determinants of MNCs’ investment projects across the EU at the NUTS3 spatial scale. Last but not least, the scope of the dataset used in this research is not limited to one level of analysis only, since this research also takes into account factors operating at the national level shaping the decisions of MNCs. This hierarchical structure of the data requires the use of a multilevel model (MLM), which allows for the introduction of factors on two or more levels of observation. Through the application of this methodological approach, this research aims to offer new, comprehensive and original insights into the location determinants of foreign direct investment projects within the EU.
This article is structured as follows: the next section establishes the theoretical background of the study by reviewing and contextualizing the previous literature on the location choices of MNCs. Subsequently, the data for the empirical analysis is presented and the methodological framework is described. Next, we discuss the results of different multilevel models. Finally, we offer some concluding remarks and recommendations for further research.
4 Results
In this section, the results of the multilevel models are presented. As described above, each model is built stepwise, starting with an intercept-only model which is followed by a random intercept model. Then, the model is extended by including additional sets of explanatory variables. Finally, a random slope model is reported, in case it fits the data significantly better than the complete random intercept model.
4.1 The regional attraction of greenfield investments
The impact of different location determinants on the regional concentration of greenfield investments is presented in Table
3. In the first column the base model is provided, including only the intercept. In the second model the intercept is allowed to vary across the 17 countries that are included in the analyses. The intra-class correlation in this model is 0.029 indicating that 2.9% of the variance is explained on country level. Since this model does not contain any explanatory variables, the residual variance (
σ 2e) represents unexplained error variance.
Table 3Results number of greenfield investments
(Intercept) | 8.5114*** | 11.1154*** | −9.3637 | −18.8577* | −15.4733 | −20.8843 | −23.1302** |
(1.3196) | (2.5277) | (7.3953) | (8.0554) | (12.7921) | (11.5314) | (7.9710) |
Traditional determinants |
nGDP_PCnGDP_PC | – | – | −0.0006* | −0.0008** | −0.0007* | −0.0007** | −0.0007*** |
– | – | (0.0002) | (0.0002) | (0.0002) | (0.0002) | (0.0002) |
lGDP_PC | – | – | 0.0011*** | 0.0011*** | 0.0011*** | 0.0011*** | 0.0010*** |
– | – | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0000) |
lGDP_GR | – | – | −0.0028 | −0.0030 | −0.0029 | −0.0029 | −0.0014 |
– | – | (0.0055) | (0.0054) | (0.0054) | (0.0054) | (0.0045) |
lEMPL_ALL | – | – | 0.0687*** | 0.0696*** | 0.0701*** | 0.0700*** | 0.1009*** |
– | – | (0.0047) | (0.0047) | (0.0047) | (0.0047) | (0.0280) |
rINC | – | – | −0.0002 | −0.0003 | 0.0001 | −0.0000 | 0.0005 |
– | – | (0.0005) | (0.0005) | (0.0005) | (0.0005) | (0.0004) |
lPOP_DENS | – | – | −0.0004 | −0.0016 | −0.0015 | −0.0015 | −0.0003 |
– | – | (0.0008) | (0.0008) | (0.0008) | (0.0008) | (0.0007) |
Innovation capabilities |
rEDU | – | – | – | 0.7304** | 0.6264** | 0.6476** | 0.3119* |
– | – | – | (0.2333) | (0.2175) | (0.2196) | (0.1385) |
lPAT_AP | – | – | – | −0.0226*** | −0.0225*** | −0.0224*** | −0.0237*** |
– | – | – | (0.0058) | (0.0058) | (0.0058) | (0.0048) |
rRNDEX | – | – | – | −1.9765* | −2.0409* | −2.0616* | −1.5239* |
– | – | – | (0.9431) | (0.9383) | (0.9395) | (0.7661) |
Institutional factors |
nPST | – | – | – | – | 9.0046 | 12.5510 | 2.7045 |
– | – | – | – | (8.0360) | (7.2325) | (3.8959) |
nEATR | – | – | – | – | −0.6472 | – | −0.2913 |
– | – | – | – | (0.4994) | – | (0.2516) |
nSCIT | – | – | – | – | – | −0.3403 | – |
– | – | – | – | – | (0.3730) | – |
Model statistics |
No. of obs | 1054 | 1054 | 1054 | 1054 | 1054 | 1054 | 1054 |
No. of countries | – | 17 | 17 | 17 | 17 | 17 | 17 |
AIC | 10,915 | 10,903 | 10,288 | 10,264 | 10,263 | 10,264 | 9917 |
σ 2j0 | – | 53.40499 | 53.24372 | 72.72317 | 40.41071 | 43.64508 | 248.24835 |
σ 2e | – | 1785.53811 | 979.66386 | 949.02155 | 949.93073 | 949.98216 | 657.21298 |
σ 2j4 | – | – | – | – | – | – | 0.01173 |
σ u04 | – | – | – | – | – | – | −0.997 |
The traditional location determinants are included in the third column. Starting with the national GDP per capita the estimated results show a negative and weakly significant impact on the number of greenfield investments in a region, suggesting that MNCs may prefer countries with relatively less large markets within Europe when it comes to setting up greenfield activities. All other traditional location determinants show the expected positive effect on the regional attraction of greenfield investments, or an effect that is statistically insignificant. The regional market size, proxied by the regional GDP per capita, shows highly significant results, indicating that market access considerations at the regional level have, instead, an important impact on greenfield location decisions of MNCs (Head and Mayer
2004). The results for the growth rate of the regional GDP show that there is no statistically significant effect on the number of greenfield investments in a region. This means that only the actual regional market size plays a role in determining the location of a greenfield investment. With respect to the conditions of local labour markets, larger employment figures, indicating a more dynamic and well-functioning context for workers, are positively correlated with the number of inward greenfield FDI, in line with existing evidence (Disdier and Mayer
2004). As far as the regional income conditions are concerned, we cannot detect any significant relationship between MNCs greenfield activities and local average disposable income. Similarly, the role of agglomeration externalities in attracting MNCs’ greenfield FDI is not relevant in our results.
In the column 4 of Table
3, we enter regional innovation capabilities into the regression specification. A preliminary observation is that the coefficients on the traditional determinants of greenfield FDI remain similar to column 3, thus reassuring us on the stability of our estimates. In this specification, only the educational attainment of the regional population exhibit a significant and positive effect on the number of greenfield investments, among the regional set of knowledge indicators. Therefore, after controlling for other factors, MNCs more systematically invest into local economies with a relatively high participation rate in tertiary education, in line with existing evidence that more sophisticated skills and know-how are key drivers of corporate strategies, especially when it comes to strategic asset seeking investment (e.g. Schneider and Frey
1985; Cleeve
2008; Ascani et al.
2016). Surprisingly, however, the technological endowment of host locations as well as their R&D expenditure are negatively associated with greenfield FDI. While counterintuitive, this may suggest that, on average, MNCs’ greenfield activities are not primarily oriented towards the development of new knowledge and, consequently, tend to avoid regional technological hubs, which plausibly represent more expensive locations.
This contradicts the previous findings that these aspects constitute important drivers of firm location decisions (Crescenzi et al.
2014). The exploration of the model fit shows that the AIC has decreased, which means that the quality of the model as a whole has increased in comparison to the previous model where only traditional location determinants are included.
After including the institutional factors (columns 5 and 6), the model fit also does not show any significant improvement. This is due to the fact the most variables included in this set of predictors do not have a statistically significant effect on the number of greenfield investments in a region. Hence, we do not detect in our data the results of De Mooij and Ederveen (
2006) that corporate taxation has a negative impact on the attraction of foreign direct investment. With respect to the effect of political stability on the number of greenfield investments, the results indicate that there is no statistically significant relation.
5
In the last column of Table
3 the results of the random slope model are presented. This model allows the slope of the relationship between the number of employees and the number of greenfield investments in a region to vary across 17 countries (NUTS 0 regions). The decision to include the number of employees as the random slope variable is due to the better model fit compared to the random slope models that allow other predictors to vary across NUTS 0 regions. The random slope model fits the data significantly better than the other multilevel models. This means that the effect of the number of employees on the number of greenfield investments differs not only in terms of the average number of greenfield investments (intercept) but also in the intensity of the relationship (slope) across countries: in some countries, the relationship between regions with strong employment concentrations and greenfield investments—in other words, agglomeration effects—are stronger than in others.
4.2 Greenfield investments in manufacturing and in the service sector
In Table
4 we evaluate whether the location determinants of foreign MNCs differ according to sectors, by considering greenfield investments in manufacturing and the service sector. For greenfield FDI in manufacturing the national GDP per capita has a significant negative effect, thus driving the aggregate results shown in Table
3. However, the results on the regional GDP per capita suggest that the market conditions of the specific region of the greenfield investment are important for the MNC strategy. Taken together, we interpret these results as an indication that MNCs prefer regions with good market opportunities in countries with a lower GDP per capita for their greenfield investments in manufacturing activities. These could be, for instance, the case of the most developed regions in the EU periphery, which are known to be particularly attractive for foreign activities, especially in manufacturing. This interpretation is supported by a number of empirical findings in Petrakos and Economou (
2002), Traistaru et al. (
2003), who highlight the process of relocation of manufacturing activity within the EU has benefitted capital cities and core regions in the EU periphery. For greenfield investments in the service sector, instead, we only detect a positive and statistically significant coefficient on the regional GDP per capita measure, thus, indicating that greenfield investments in this sector tend to locate in regions with larger local market, regardless of the national size of the economy. Based on this finding, it can be stated that MNCs tend to locate their greenfield investments in the service sector in core regions across the EU, while greenfield investments in manufacturing are preferably located in well-developed regions within relatively less advanced countries. With respect to the labour market conditions, greenfield investments in both business activities show statistically significant positive results for the effect of regional employment, indicating that MNCs prefer those regions where the employment rate is relatively high. However, this findings hold across specification for the manufacturing regressions, while it tends to lose statistical relevance in the case of services. Overall, we interpret this as evidence that MNCs are attracted to locations with well-functioning local labour markets, especially as far as manufacturing operations are concerned. Another important difference emerging from the sectoral analysis regard the role of paid wages in a region, proxied by disposable income. In fact, while MNCs setting up foreign greenfield activities in manufacturing are pulled to locations with higher wages, thus plausibly privileging more productive labour (c.f. Guimaraes et al.
2000; Defever
2006), in the case of services we find (weak) evidence that MNCs adopt a more efficiency-seeking strategy by locating in places with lower salaries (Dunning and Lundan
2008).
Table 4Results number of greenfield investments (Manufacturing & Services)
Traditional determinants |
(Intercept) | 1.9282 | 1.0608 | −1.2742 | −8.7336 | −9.5932 | −9.8698 |
(1.4027) | (1.2800) | (0.7294) | (10.1911) | (9.1433) | (6.5814) |
nGDP_PCnGDP_PC | −0.0001*** | −0.0001*** | −0.0001*** | −0.0002 | −0.0002 | −0.0003* |
(0.0000) | (0.0000) | (0.0000) | (0.0002) | (0.0002) | (0.0001) |
lGDP_PC | 0.0001*** | 0.0001*** | 0.0001*** | 0.0006*** | 0.0006*** | 0.0006*** |
(0.0000) | (0.0000) | (0.0000) | (0.0001) | (0.0001) | (0.0000) |
lGDP_GR | −0.0003 | −0.0003 | −0.0003 | −0.0014 | −0.0014 | −0.0002 |
(0.0005) | (0.0005) | (0.0004) | (0.0045) | (0.0045) | (0.0036) |
lEMPL_ALL | 0.0087*** | 0.0087*** | 0.0111*** | 0.0357*** | 0.0357*** | 0.0508* |
(0.0004) | (0.0004) | (0.0027) | (0.0039) | (0.0039) | (0.0253) |
rINC | 0.0002*** | 0.0002*** | 0.0001*** | −0.0011* | −0.0011* | −0.0004 |
(0.0001) | (0.0001) | (0.0000) | (0.0005) | (0.0005) | (0.0004) |
lPOP_DENS | −0.0002** | −0.0002** | −0.0002** | −0.0016* | −0.0016* | −0.0007 |
(0.0001) | (0.0001) | (0.0001) | (0.0007) | (0.0007) | (0.0006) |
Innovation capabilities |
rEDU | 0.0241 | 0.0248 | 0.0360** | 0.2104 | 0.2120 | 0.0501 |
(0.0210) | (0.0213) | (0.0126) | (0.1843) | (0.1841) | (0.1154) |
rRNDEX | −0.1961* | −0.1966* | −0.1700* | −1.1429 | −1.1437 | −0.8610 |
(0.0878) | (0.0880) | (0.0719) | (0.7791) | (0.7790) | (0.6138) |
rGVA_MANU | −106.2880* | −106.5760* | −110.6439** | – | – | – |
(43.9527) | (44.0148) | (37.2048) | – | – | – |
rGVA_SERVICE | – | – | – | 586.9248** | 591.6746** | 430.8056** |
– | – | – | (206.0814) | (205.2312) | (161.9645) |
lPAT_AP | – | – | – | −0.0102* | −0.0102* | −0.0136*** |
– | – | – | (0.0049) | (0.0049) | (0.0039) |
Institutional factors |
nPST | −0.1447 | 0.5666 | 0.0375 | 8.0236 | 8.3079 | 2.7366 |
(0.9105) | (0.8428) | (0.3683) | (6.2676) | (5.4851) | (3.0262) |
nEATR | −0.1485* | – | −0.0741** | 0.0010 | – | −0.0017 |
(0.0561) | – | (0.0234) | (0.3951) | – | (0.2006) |
nSCIT | – | −0.0973* | – | – | 0.0458 | – |
– | (0.0430) | – | – | (0.2866) | – |
Model statistics |
No of obs | 1054 | 1054 | 1054 | 1054 | 1054 | 1054 |
No of. countries | 17 | 17 | 17 | 17 | 17 | 17 |
AIC | 5063.5771 | 5064.8347 | 4783.2896 | 9869.6497 | 9869.6240 | 9449.5240 |
σ 2j0 | 0.6455 | 0.7409 | 1.9090 | 22.7989 | 22.6387 | 216.3746 |
σ 2e | 6.7885 | 6.7855 | 5.01636 | 654.1343 | 654.1575 | 419.6978 |
σ 2j4 | – | – | 0.00011 | – | – | 0.0098 |
σ j04 | – | – | −0.995 | – | – | −0.999 |
Contrarily to expectations, investment projects in both manufacturing and services tend to be overall located in regions where the population density is lower, referring to less urbanised regions that are associated with lower land costs, fundamentally in line, once again, with a type of efficiency seeking rationale.
With respect to the innovation capabilities of regions some differences emerge depending on the main sector of economic activity of the MNC investment. The educational attainment of the local labour force has a significant and positive effect only in one specification regarding manufacturing activity (i.e. model with random slope), while it remains insignificant in the other regressions. Similarly, R&D expenditure is significant and negative only for manufacturing investment. These set of results, on average, signal that the MNCs in our data are not systematically undertaking strategic asset-seeking FDI, and therefore the knowledge-related feature of the regional economy do not matter for their greenfield activities.
Institutional factors are also included in this empirical exercise. For greenfield investments of both types of business activities the results suggest that this set of explanatory factors does not influence the number of investment projects, with the exception of taxation levels, that in the case of manufacturing activity represent a clear detrimental factor that discourages MNCs’ investment, in line the extant evidence (Voget
2011).
It is worth noting that, gain, the random slope models, estimated for greenfield investments in both business activities, significantly improves the model fit.
4.3 The regional attraction of M&A projects
Given their inherently different nature, it is possible that the MNCs’ strategies governing greenfield FDI differ from the location decisions behind M&A projects (Bertrand et al.
2007). Therefore, we perform the same empirical analysis for the case of M&A. Table
5, nevertheless, shows that the location determinants of MNCs activity do not differ much across entry modes. In fact, our results suggest that M&A follow a similar pattern to greenfield FDI, suggesting that the choice of the entry mode of foreign MNCs does not strongly depend on the attributes of the locations available. It is plausible that this choice instead is more dependent on individual company features, as indicated by several studies (Nocke and Yeaple
2007; Guadalupe et al.
2012). Overall, the pattern of localisation that we detect for the case of M&As reflect corporate strategies oriented towards the access of localised markets of core regions within countries that are economically peripheral within the EU, as indicated by the persistent positive sign on regional GDP per capita and the negative sign on the national GDP per capita variable. Invariably, a well-functioning the local labour market and the presence of a suitable workforce represent a key pull factor for investment (Crescenzi et al.
2014), while, similar to the above results, we cannot find univocal evidence that more developed regional innovation capabilities constitute a positive determinant of M&As. In fact, with the exception of an educated workforce, that is still positively evaluated by foreign capital investors, the regional stock of knowledge captured by patents as well as the local expenditure in R&D seem to be detrimental for the attraction of foreign investment. This could be the case if, on average, the rationale of most M&As in our dataset is market-seeking or efficiency-seeking, rather than directed towards the access of specific capabilities or knowledge bases (Guadalupe et al.
2012; Ascani
2018). In this sense, foreign MNCs may prefer less expensive locations, where it is plausible that the frequency of activities oriented towards the generation of new technologies are far from being relevant. The efficiency-seeking character of these investment decisions is also supported by the negative, yet weak, coefficients on the variables capturing the taxation level of alternative locations. This also represents the main difference with the case of aggregate greenfield activities examined above, where the relationship between the location choice and taxation was not statistically significant. Since M&A projects frequently have an efficiency seeking motive (Neary
2004), these findings are in line with the expectation that MNCs tend to cherry-pick locations with cost-advantages.
Table 5Results for the number of M&A projects
Traditional determinants |
(Intercept) | −9.8356*** | −12.5347*** | −9.3624* | −11.3857*** | −6.3912 |
(2.9229) | (2.5951) | (3.6328) | (3.3642) | (4.0533) |
nGDP_PCnGDP_PC | −0.0002* | −0.0003** | −0.0002** | −0.0003** | −0.0002* |
(0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) |
lGDP_PC | 0.0004*** | 0.0004*** | 0.0004*** | 0.0004*** | 0.0004*** |
(0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) |
lGDP_GR | −0.0013 | −0.0013 | −0.0010 | −0.0010 | −0.0010 |
(0.0016) | (0.0016) | (0.0016) | (0.0016) | (0.0013) |
lEMPL_ALL | 0.0549*** | 0.0549*** | 0.0549*** | 0.0549*** | 0.0591*** |
(0.0014) | (0.0014) | (0.0014) | (0.0014) | (0.0091) |
rINC | 0.0003* | 0.0002 | 0.0002 | 0.0002 | 0.0001 |
(0.0001) | (0.0002) | (0.0001) | (0.0001) | (0.0001) |
lPOP_DENS | −0.0003 | −0.0007** | −0.0007** | −0.0007** | 0.0001 |
(0.0002) | (0.0002) | (0.0002) | (0.0002) | (0.0002) |
Innovation capabilities |
rEDU | – | 0.2762*** | 0.2797*** | 0.2805*** | 0.1368* |
– | (0.0710) | (0.0623) | (0.0637) | (0.0563) |
lPAT_AP | – | −0.0064*** | −0.0066*** | −0.0066*** | −0.0064*** |
– | (0.0017) | (0.0017) | (0.0017) | (0.0013) |
rRNDEX | – | −0.6104* | −0.6474* | −0.6533* | −0.3070 |
– | (0.2746) | (0.2706) | (0.2714) | (0.2201) |
Institutional factors |
nPST | – | – | 2.5951 | 4.3669 | 0.1788 |
– | – | (2.2759) | (2.1150) | (2.5332) |
nEATR | – | – | −0.3601* | – | −0.3027 |
– | – | (0.1415) | – | (0.1553) |
nSCIT | – | – | – | −0.2380* | – |
– | – | – | (0.1090) | – |
Model statistics |
Num. obs | 1054 | 1054 | 1054 | 1054 | 1054 |
AIC | 7677.5390 | 7650.7115 | 7643.5836 | 7644.7092 | 7230.2227 |
Num. countries | 17 | 17 | 17 | 17 | 17 |
σ 2j0 | 14.8950 | 9.5364 | 3.1765 | 3.7811 | 19.839 |
σ 2e | 81.168 | 79.1046 | 79.1998 | 79.1568 | 50.045 |
σ 2j4 | – | – | – | – | 0.0013 |
σ j04 | – | – | – | – | −0.875 |
Finally, a random slope variable is introduced in the equation. The results are presented in the last column of Table
5. The AIC has decreased substantially, which means that the model fits the data better compared to the random intercept models.
4.4 M&A projects in manufacturing and in the service sector
In Table
6 we present the results for the M&A location choice analysis split by sector. While the general trends identified above are also reflected in this set of results, some differences emerge across sectors of economic activity. Starting with the similarities, for M&As in both manufacturing and service the national market size weakly suggest that MNCs locate in countries that exhibit relatively lower GDP, in line with the aggregate results, thus potentially indicating that most corporate activities move to the periphery of the EU. On the contrary, for both business activities the regional GDP per capita has a significant positive effect, corroborating the existing evidence that the regional market size is highly valued by MNCs for their M&A projects. This is in line with the results of Brakman et al. (
2007) who have concluded that especially horizontal investments frequently have a market-seeking motive. Also, MNCs favour locations with a higher employment rate since it reflects a larger endowment of available labour force (Disdier and Mayer
2004). Regarding the differences across sectors, instead, we find mixed evidence on the average disposable income as a proxy for the wages paid in a region, as this is clearly negative in the case of services while ambiguous results emerge for the case of manufacturing. Nevertheless, considering that the random slope specification in column 3 delivers better estimates according to the AIC, we tend to consider this as our preferred specification. Therefore, these set of results support, again, the efficiency-seeking nature of most MNCs investment by M&As, in line with the idea that the cost-reducing rationale is a strong element in the corporate strategies of these actors (Gereffi and Korzeniewicz
1994). Our results also suggest that existence of heterogeneous preferences of MNCs regarding urban agglomerations, as the population density variable exhibits a negative and statistically significant coefficient in the case of manufacturing, signalling that these activities tend to locate in less urbanised areas where the land costs are lower, while it remains non-significant for services.
Table 6Results number of M&A projects (Manufacturing & Service)
Traditional determinants |
(Intercept) | −0.3475 | −0.8368 | 0.9694 | −3.7881*** | −3.9562*** | −2.2414* |
(1.7644) | (1.5134) | (1.1753) | (0.9109) | (0.8657) | (1.1096) |
nGDP_PCnGDP_PC | −0.0001 | −0.0001* | −0.0001* | −0.0001* | −0.0001* | −0.0001* |
(0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) |
lGDP_PC | 0.0001*** | 0.0001*** | 0.0001* | 0.0002*** | 0.0002*** | 0.0002*** |
(0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) |
lGDP_GR | −0.0000 | −0.0000 | −0.0001 | −0.0004 | −0.0004 | −0.0003 |
(0.0005) | (0.0005) | (0.0004) | (0.0007) | (0.0007) | (0.0006) |
lEMPL_ALL | 0.0148*** | 0.0148*** | 0.0148*** | 0.0151*** | 0.0150*** | 0.0161*** |
(0.0004) | (0.0004) | (0.0004) | (0.0006) | (0.0006) | (0.0032) |
rINC | 0.0002*** | 0.0002*** | −0.0001** | −0.0002** | −0.0002*** | −0.0002** |
(0.0001) | (0.0001) | (0.0000) | (0.0001) | (0.0001) | (0.0001) |
lPOP_DENS | −0.0006*** | −0.0006*** | −0.0004*** | −0.0000 | −0.0000 | 0.0001 |
(0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) |
Innovation capabilities |
rEDU | 0.0188 | 0.0192 | 0.0351* | 0.0643*** | 0.0652*** | 0.0356 |
(0.0225) | (0.0224) | (0.0141) | (0.0194) | (0.0194) | (0.0200) |
lPAT_AP | – | – | – | −0.0020** | −0.0019** | −0.0025*** |
– | – | – | (0.0007) | (0.0007) | (0.0006) |
rRNDEX | −0.2638** | −0.2644** | −0.1645* | −0.1670 | −0.1688 | −0.1169 |
(0.0894) | (0.0893) | (0.0748) | (0.1089) | (0.1089) | (0.0977) |
rGVA_MANU | 77.7610 | 77.3767 | 174.9382*** | – | – | – |
(44.4215) | (44.4282) | (39.9165) | – | – | – |
rGVA_SERVICE | – | – | – | 103.7190*** | 106.7420*** | 89.7291*** |
– | – | – | (28.7670) | (28.5658) | (25.8493) |
Institutional factors |
nPST | 0.2240 | 0.7529 | 0.1818 | 1.6849** | 1.7676** | 0.7351 |
(1.1627) | (1.0126) | (0.4792) | (0.4891) | (0.4436) | (0.5703) |
nEATR | −0.1267 | – | −0.0576 | −0.0106 | – | −0.0543 |
(0.0712) | – | (0.0315) | (0.0329) | – | (0.0368) |
nSCIT | – | −0.0954 | – | – | 0.0020 | – |
– | (0.0515) | – | – | (0.0242) | – |
Model statistics |
No. of obs | 1054 | 1054 | 1054 | 1054 | 1054 | 1054 |
No. of countries | 17 | 17 | 17 | 17 | 17 | 17 |
AIC | 5083.4502 | 5083.2225 | 4842.3036 | 5782.5803 | 5782.6779 | 5556.7953 |
σ 2j0 | 1.1729 | 1.1559 | 10.1846 | 0.000000046 | 0.000000042 | 3.9826 |
σ 2e | 6.8675 | 6.8674 | 5.3197 | 13.7354 | 13.736 | 10.4791 |
σ 2j2 | – | – | 0.000000018 | – | – | f |
σ j02 | – | – | −0.998 | – | – | – |
σ 2j4 | – | – | – | – | – | 0.00015 |
σ j04 | – | – | – | – | – | −0.989 |
With respect to the effect of regional innovation capabilities, the results of the random slope model suggest that, controlling for a varying effect of the regional market size across countries, the educational attainment and the sectoral GVA have a statistically significant effect on the number of investment projects in manufacturing, although the statistical relevance of the educational variable remains weak. For services, instead, while the sector GVA is also an important determinant of location choice, the educational level of the regional population remains insignificant in the random slope model, signalling that tertiary activities may not be dependent on this type of factor. Potentially, these are not high value added service activities for which specific skills are requested. Regarding the other variables, results remain similar to those presented above in the aggregate analysis. Overall, we find evidence that M&A activities also respond to a marked market access rationale, especially at the regional level, and that also efficiency-seeking motives play a substantial role in shaping the patterns of MNCs’ investment, especially as far as manufacturing activities are concerned. This is in line with the evidence that the location of European manufacturing has experiences a long shift towards locations offering a stronger cost-advantage (Traistaru et al.
2003).
5 Conclusion
In this study we explored the location determinants of European MNCs’ investment projects in the countries of the EU by means of a quantitative multilevel analysis focusing on both regional and national pull factors, thus accounting for the hierarchical structure of the data and business dynamics. In so doing, we incorporated and built on the main findings of the previous literature, by accounting for three main sets of locational factors, namely: traditional drivers of FDI, knowledge-based regional factors and the features of the institutional context. Moreover, in order to provide a detailed analysis, FDI projects are also distinguished based on their entry mode (i.e. greenfield investments and M&A). In order to test the sensitivity of MNCs choices to sectoral dynamics we also considered the changing composition of FDI in Europe by differentiating between manufacturing and service activities.
We identify a plethora of original results that only partially reflect existing empirical evidence and to some extent they expand the understanding of MNCs location strategies in new directions. The main results suggest that there are no major differences in the location determinants of greenfield FDI and M&A projects within Europe, as both types of foreign investment seems to be market-seeking and efficiency-seeking in nature. Contrary to the findings of Basile (
2004) who concludes that the location determinants of FDI differ according to the entry mode and to the hypothesis of Bertrand et al. (
2007) who state that it is not reasonable to assume that the location determinants of greenfield investments and M&As are identical, our results indicate the differences in the role of location determinants are minimal. More specifically, with respect to the traditional location determinants, MNCs seem to value a relatively larger regional market size, indicating that FDI is mostly attracted to economically “core” regions (Crescenzi et al.
2014). This is also supported by the results with respect to the labour market conditions since FDI projects are concentrated in regions where the employment rate and, consequently, the functioning of the local labour market is relatively efficient. However, the level of urbanisation, associated with land costs tends to discourage foreign MNCs, especially in manufacturing activities, as these type of activities might not necessarily need urbanisation externalities to thrive.
Regarding regional innovation capabilities, the results show that MNCs systematically prefer locations endowed with a relatively highly educated population for their international activities, as this is the case of greenfield FDI in manufacturing and M&As in tertiary activities mainly. Nonetheless, the more technologically-oriented features of regional economies, such as their patent stock and their expenditure in R&D, are negatively correlated with foreign investment. This may support a view of MNCs’ internationalisation in our data oriented towards a cost-reducing approach, whereby the most technologically advanced regions are not affordable locations for most foreign investment activities. Considering also that the main and most stable result of the analysis indicates that market access considerations are important, we can rule out that the search for novel knowledge capabilities guide the location behaviour of MNCs in our sample.
As far as the institutional dimension is concerned, the analysis provides some evidence that the effective average tax rate has a negative impact on the number of investment projects, in line with the cost reducing motivation of foreign direct investment projects. Political stability and government corruption both seem not to determine the location of MNCs within Europe, probably due to the low variation of these institutional features within the EU.
Considering these articulated results, policy making aimed at attracting FDI, in the form of both greenfield projects and M&A, should primarily reinforce the
regional economic system in terms of market opportunities, quality of the local labour force and functioning of the regional labour markets, as this emerge as the crucial spatial scale for successfully attracting MNCs. Importantly, this focus on the features of the local economy should lead to the attraction of specific inward FDI projects that can match the regional economic structure in terms of competences and inter-firm linkages, in order to generate additional local economic effects, as suggested by recent empirical evidence (Ascani et al.
2019). It is also fundamental to consider that the advantages of core European regions over disadvantaged locations could increase as a result of the stronger capacity of the former to attract MNCs. These dynamics can occur both between and within countries. Therefore, considering the location determinants of FDI in a regional perspective, thus opening the black box of country level analyses, paves the way for policy measures at national (or supranational) scale targeted at reinforcing the profile of specific lagging behind locations in participating in global production.
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