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XXX CONFERENZA ITALIANA DI SCIENZE REGIONALI
EVIDENCE ON THE DETERMINANTS OF FOREIGN DIRECT INVESTMENT: THE CASE
OF EU REGIONS
Laura RESMINI(1) e Laura CASI
(2)
(1) Università della Valle d’Aosta, Aosta (Italy) e ISLA-Bocconi, Milan (Italy)
Università della Valle d’Aosta - Faculty of Political science and International relationships
Loc. Grand Chemin, 73-75 - 11020 St. Christophe (Aosta) Italy
Tel. ++39 0165 066718; Fax.: ++39 0165 066748
Mail: [email protected]
(2) ISLA – Università Bocconi, Via Roentgen, 1 – 20136 Milano – mail: [email protected]
This version: June 2009
(first draft: not to quote without permission)
ABSTRACT
This study analyses the determinants of Foreign Direct Investment (FDI) at regional level. While
the determinants of FDI in Europe have been extensively analysed at country level, the literature on
location patterns and on the determinants of FDI at regional level is only at its beginning. This
study follows this line of empirical research by using original data on the number of foreign
investments over the 2005-07 period disaggregated by regions of the EU27 and by sectors. In
particular, we perform a detailed analysis of the location determinants of foreign investments using
different econometric specifications, in order to consider a large set of variables potentially
explaining FDI location. We attempt do demonstrate, on the one hand, whether and to what extent
variables usually employed to explain the determinant of FDI at country level, also influence the
location of FDI at regional level, and which local characteristics, if any, can be associated with the
determinants of FDI, and, on the other hand, whether and to what extent these variables are
constant over different manufacturing and service sectors and country of origin of the foreign
investors.
Keywords: Foreign Direct Investment, Regions.
JEL: F20, O18, R10.
2
EVIDE�CE O� THE DETERMI�A�TS OF FOREIG� DIRECT I�VESTME�T:
THE CASE OF EU REGIO�S
1. Introduction
The current wave of globalization that has been affected world economy since the beginning of the
past decade has seen multinational enterprises (MNEs) to play a leading role in shaping and driving
cross-border integration through the transfer of production facilities, functions and or technology
across space. These trends have been reinforced by the liberalization of new markets, especially in
the service sectors, the reduction of capital movement restraints, and the creation of a friendly
environment for Foreign Direct Investment (FDI) in a growing number of countries. The EU has
been a major player in these processes, since they coincided with three important milestones of the
European integration process, such as the single market program, the introduction of the Euro and
the East enlargement. Despite the cyclical character of FDI flows and their dependence from
economic fundamentals, inward FDI stocks in the EU have increased exponentially since the 1980s,
reaching their peak in 2007, with more than 7,000 billions of USD and a percentage of world stocks
of about 45%.1 Also, the EU’s capacity to attract FDI – defined as FDI stocks adjusted with GDP –
has increased over time and overcome world average from 1990 onwards, thus suggesting that the
EU has been able not only to maintain but also to further improve their attractiveness for foreign
investments, despite the emergence of new interesting destinations all around the world, such as
China, India and Brazil.
While the determinants of these impressive flows of FDI in Europe have been extensively analysed
at country level, the literature on location patterns and on the determinants of FDI at regional level
is only at its beginning.2 This study follows this line of empirical research by using original data on
the number of foreign investments over the 2005-07 period disaggregated by regions of the EU27
and by sectors. In particular, we perform a detailed analysis of the location determinants of foreign
investments using different econometric specifications, in order to consider a large set of variables
potentially explaining FDI location. We attempt to demonstrate, on the one hand, whether and to
what extent variables usually employed to explain the determinant of FDI at country level, also
influence the location of FDI at regional level, and which locational characteristics, if any, can be
associated with the determinants of FDI in Europe, and, on the other hand, whether and to what
1 See UNCTAD, World Investment Reports, various issues for an in-depth analysis of FDI flows and stocks at European
and world levels. 2 Data constraints largely explain why few papers have attempted empirical analysis on FDI location patterns at more
disaggregate levels.
3
extent these variables are constant over different manufacturing and service sectors and country of
origin of the foreign investors.
The paper is ideally divided into two interdependent parts. In the first, we develop a qualitative
description of the FDI trends by sector and by region (section 2) and discuss the importance of
several potential determinants in attracting FDI (section 3). Then, we provide an econometric
exercise to identify a relationship between FDI by region and sector and those potential
determinants (sections 4 and 5, respectively). We show that regional FDI inflows rely on a
combination of factors that differs from one region to another and by sectors.
2. The Spatial distribution of FDI in Europe
2.1 Data source and sample
This paper exploits a new database, FDIRegio, which has been built up starting from Amadeus
database.3 It consists of company accounts reported to national statistical offices concerning 11
million public and private companies in 41 European countries. For each company Amadeus
provides the year of incorporation, the country/region and the ownership structure by nationality.
The data also include the region where the firm were founded, as well as the sector of activity.
Firms newly created during the 2005-07 period whose percentage of assets owned by non-residents
was at least 10% have been considered as foreign. Then they were aggregated in each European
NUTS2 region by sector and by origin within or outside Europe. The overall sample includes 264
NUTS2 regions and 25 NACE1 manufacturing and service sectors.4
A limitation of these data for studying the geographical patterns of foreign firms is that they include
either plant or firm level information. This can potentially bias the location of FDI in favour of
regions and/or countries where headquarters tend to locate.5 An advantage of this approach is
instead represented by the fact that the regional distribution of foreign firms is directly observed and
not indirectly derived from a “regionalization” of national data. This top-down approach, in fact, is
based on the simplifying assumption that the sensitivity of foreign firms to employment data – or
whatever it is used to regionalize patterns of FDI – is constant across foreign firms, regardless the
3 Amadeus is a product by the Bureau Van Dijk. For more information on it, see www.bvdep.com
4 We considered as European investors also Norway, Island, Lichtenstein and Switzerland because they have signed
agreements with the EU which allow them to participate to the single market without being member of the EU. Malta
and Cyprus are not considered because of data limitations. In the Baltic Republics there are no NUTS2 regions;
therefore, the regional level coincides with the national one. The list of sectors included in the analysis is provided by
Table A.1 in the Annex. 5 Previous analyses based on similar data exclude this possibility. See, for example, Pusterla and Resmini (2007), and
EC (2006).
4
internationalization strategy they pursue (efficiency, market and resource seeking FDI), the country
of origin and the role foreign affiliates can play within the group (productive vs. research units).
In order to have an idea of the degree of inclusiveness of the dataset, we compared official
(UNCTAD) data on inward FDI flows at country level with the total number of foreign firms
extracted from Amadeus following the criteria described above. Figure A.1 in the Annex shows the
results. It is worth noticing that the correlation between the two measures of FDI flows is quite
high. Thus, by considering number of foreign firms instead of values of FDI we do not introduce
any significant distortion in the patterns of FDI, though foreign investments in some destination
countries have a relative importance that is different in terms of number of firms with respect to the
value of FDI inflows.
2.2 Distribution of FDI by region and sector
Map 1 shows the number of new foreign affiliates established in the EU during the 2005-07 period,
adjusted with millions of inhabitants in order to take into account regions’ size. Looking at spatial
patterns, the picture depicted generally corresponds to the existing evidence on MNE location
patterns across Europe. In particular, most new foreign affiliates appear to be located in the EU’s
core, i.e. the area going from the UK to the north of Italy, including regions on the border between
France and Germany, Ireland, Belgium, and the Netherland. The data also show that other areas,
such as Austria, as well as the Spanish regions of Madrid, Cataluña and Basque country have also
attracted a large number of new multinationals.
FDI in the new EU member states is largely concentrated in Romania, the Baltic Republics, and
also, to some extent, in Poland. As far as other new EU member states are concerned, only the
capital regions seem to be able to attract a significant number of new foreign firms. This trend is
particularly apparent in Czech and Slovak Republics, and in Bulgaria, though in all new member
states the concentration of FDI is stronger in capital regions than in other regions.
Most regions, notably in the Southern countries of EU and at the Eastern external borders, have
been clearly at the margin of the location patterns of MNEs in Europe. We refer here to Italy,
Greece and Portugal, whose regions have attracted a very low number of foreign firms, as compared
to other western European countries.
Overall, these results indicate that foreign firms tend to be spatially clustered. Most of these
clusters, however, coincide with national borders, suggesting that country-specific effects may
affect patterns of location of FDI. In particular, the most attractive regions locate in two countries,
the United Kingdom in Western Europe and Romania in Eastern Europe.
5
These patterns do not differ substantially between manufacturing and service sectors, though FDI in
the manufacturing concentrates mainly in EU10 regions and FDI in service in EU15 regions (see
Maps 2 and 3).
(insert map 1 about here)
(insert map 2 and 3 about here)
Table 1 lists the Moran’s I statistics and associated z- and p-values for a variety of foreign firm
penetration indexes. Positive values of the Moran’s I indicate positive spatial autocorrelation,
implying that similar values are more spatially clustered that could have been caused by chance.
Negative values indicate a clustering of dissimilar values (Anselin, 2001). The Moran’s I statistics
is positive, though very closed to zero, and significant in all cases but extra-Europe FDI, indicating
the presence of a weak positive spatial autocorrelation driven by European foreign firms. We will
take this information into account when specifying the empirical model.
(insert table 1 about here)
3. Theoretical background
The theoretical analysis of FDI determinants starts with the question why a firm seeks to undertake
FDI. This question may be answered by using either partial or general equilibrium models.
According to the former, FDI is a way to internalize trade costs and externalities from firm-specific
assets (Barba Navaretti and Venables, 2004), while the latter distinguish two main motivations for
becoming multinational (Markeusen, 1984; Helpman, 1984; Shatz and Venables, 2000): to avoid
trade frictions in servicing the local market (horizontal FDI), or to gain access to lower-cost inputs
(vertical FDI).
Due to the difficulty of building tractable general equilibrium models, the empirical literature is
mainly based on a partial equilibrium framework. The data used are generally at industry or country
level and, when it is available, at plant level (Bloningen, 2005).
There is a lack of consensus on the effect of several determinants of FDI, except for market size,
which seems to be the most robust FDI determinant (Kravis and Lipsey, 1982; Wheeler and Mody,
1992). The effects of trade barriers, trade openness, labour costs, taxes, and agglomeration forces
are instead more debated. They have been the subject of many papers with contradictory results.6
Apart from methodological differences, this lack of consensus indicates that the relevance of FDI
determinants may depend on location, and that geographic specificities can not be identified at
6 See among many others Bloningen and Feenstra (1996); Kravis and Lipsey, 1982; Feenstra and Hanson (1997),
Wheeler and Mody (1992); Bloningen, 2005.
6
national or firm level. To this respect, a regional perspective may be more appropriate. A few
papers have appeared recently with a regional focus, but most of them are single country studies or
analyse small groups of European countries. Crozet, Mayer and Mucchielli (2004) study the
determinants of FDI location in the French departments using plant-level data. The effects of
market size and agglomeration are positive while it is negative for wages. Interestingly, the sign and
the magnitude of these effects vary across sectors. Using aggregate FDI data at county level,
Boudier-Bensebaa (2005) finds that labor availability, local market size and agglomeration effects
have a positive and significant effect on FDI location in Hungary. Pusterla and Resmini (2007)
utilize firm-level data on foreign firm manufacturing plants in Bulgaria, Hungary, Poland and
Romania to analyse the determinants of foreign firms' location choice. They find that demand
factors are more important than cost factors, and that agglomeration effects are driven by
multinational rather than indigenous firms. More importantly, they find that the determinants of FDI
vary across manufacturing sectors and that national boundaries do not matter in foreign firms
location choice, thus further reinforcing the importance of a regional approach to the determinants
of FDI. More recently, Basile, Castellani and Zanfei (2008) analyze the determinants of location
choices of MNFs in Europe on a sample of 5509 foreign plants located in 50 European regions over
the 1991–1999 period. They found that a number of regional characteristics exert a different impact
on European and non-European foreign investors. In particular, while the former are attracted
towards regions with lower per-capita income, relatively high unemployment and large market
potential, the latter seem to prefer regions with higher wage and per-capita income. Agglomeration
economies, instead, play an important role in attracting FDI, regardless of their origin inside or
outside Europe. Finally, Basile, Castellani and Benfratello (2008) suggest that also the sector of
activity – i.e. manufacturing vs. services – may affect foreign firms’ location choice.
4. The determinants of FDI: a regional approach
In order to understand the patterns of FDI inflows to European regions we first test whether and to
what extent, traditional variables usually employed to explain the main factors associated with
patterns of FDI at country level also influence the location of foreign firms at regional level. Then
we introduce a set of new and more disaggregated variables we expect to be able to capture more
specific regional factors crucial in the location choice of multinational enterprises entering in the
European industrial panorama.
7
4.1 Econometric specification and explanatory variables
The model used is a simple log-linear equation of the form:
jjjjjj HSIMFDI εβββββ +++++= ****)ln( 43210
This specification includes different blocks of variables that we tested separately in order to derive
the most effective econometric model able to capture the main factors associated with regional
patterns of FDI. The dependent variable considered, jtFDI , is an FDI penetration index calculated
as the number of new foreign firms established in region j during the 2005-07 period, normalized by
population, where j=1,…,260 are the European regions considered. This is a way to control for the
so called “size effect” consisting in the capacity of a region to attract more FDI just because it is
bigger in economic term than other regions.7
The first block of independent variables, M j , includes the main traditional location characteristics
that have been proven to exert an impact on FDI by previous similar studies (Artige and Nicolini,
2006; Pusterla and Resmini, 2007), i.e. labour costs, local demand, market size and accessibility and
foreign agglomeration.
According to the theory, the magnitude of impact these variables may exert on foreign firms
depends, on the one hand, on the motive for FDI, and, on the other hand, on the type of foreign
investment (manufacturing vs. services). In particular, efficiency seeking FDI is more sensitive to
labour costs, which are generally considered to be a negative host-specific location determinant,
particularly for FDI in labour intensive industries. However, in case of intensity in highly skilled
labour activities – such as financial services – where wages are relatively higher, labour costs may
not matter.
Market size as well as the geographical and economic proximity of the local market to the main
economic centres, instead, is particularly important for foreign investors looking for new markets
for their products. Locations with good market accessibility to the main core markets provide
foreign firms to export to and import from the core cheaply. Hence, we expect that regions with a
good geographical and economic accessibility to the European economic core are attractive to
foreign firms and therefore, are likely to receive more FDI than other regions.
Finally, the role of agglomeration economies in explaining firms’ location choice is well
acknowledged by regional and international economics. Various theoretical concepts suggest that
clustering economic activities results in cost-savings and productivity gains through several
channels, ranging from client-supplier relationships, labour pooling, and competition effects (Fujita
7 Country specific effects are accounted for through specific dummy variables.
8
and Thisse, 2002). Several variables can be considered to capture different kind of agglomeration
economies. The one we consider in this block refer to foreign agglomeration, which we expect to be
particularly important because an existing concentration of foreign firms facilitates the gathering of
information via business relationships or because it demonstrates the economic potential of a
region. Therefore, the larger the number of foreign firms in a given location, the lower is likely to
be the risk (and the cost) for a new foreign firm of locating there.
The two subsequent blocks of regressors capture the importance of localization externalities, arising
when firms benefit from the presence of clients, suppliers and competitors in the same industry. We
measure location externalities through industry-specific agglomeration measures. Indeed, the I j
block includes variables that define the industrial specialization of region j. Four different macro-
sectors have been considered, i.e. low-tech, medium-low, medium-high and high tech sectors. The
third block, S j, represents instead the potential specialization of region j in different service
sectors. While localization externalities may occur either in manufacturing or in service sectors, we
expect the latter being more important than the former, given that recent developments in Europe
include not only an increasing liberalization in service markets, but also a constant
deindustrialization (OECD, 2007).
The last block of regressors, H j, focuses on regional human capital endowment. Differently from
previous studies, here we consider new and more disaggregated proxies for human capital
competencies, which can better capture foreign firms’ needs. Easy access to, as well as competition
among various local private services – such as professional services and command and control
functions – as well as a wide range of cultural diversities – such as the presence of university and
scientist professionals – may help foreign firms in overcoming several problems related to
inefficient bureaucracies, poor communication infrastructures, unreliable financial institutions, and
cultural issues.
Of course, all the explanatory variables have been lagged one period in order to avoid potential
endogeneity problems. 8 The full equation we constructed is thus the following one:
8 “Previous” period can not be exactly identified, because it depends on data availability.
9
tjtjtjtjtj
tjtjtjtj
tjtjtjtjtj
tjtjtjtj
tjtjUKjROjtj
ISCOISCOISCOISCO
ISCOlqO;PlqGlqH
lqIlqJlqKlqHTlqMHT
lqMLTlqLTApcGDP
GDPFDIDDFDI
,1,451,441,431,42
1,411,361,351,34
1,33,32,31,24,23
,221,211,141,13
1,121,11,02,0100,
)13ln(*)12ln(*)2ln(*)4ln(*
)8ln(*)_ln(*)_ln(*)_ln(*
)_ln(*)_ln(*)_ln(*)_ln(*)_ln(*
)_ln(*)_ln(*)ln(*)_ln(*
)ln(*)ln(***)ln(
εββββ
ββββ
βββββ
ββββ
βββββ
+++++
+++++
++++++
+++++
+++++=
−−−−
−−−−
−
−−−
−−
More specifically, the independent variables included in the regression are:
- ROjD , and UKjD , , which are two dummy variables equal to 1 when the region j belongs to
Romania / United Kingdom and equal to 0 otherwise. These dummies control for those potential
country-specific effects that have made United Kingdom and Romania two outstanding receivers of
FDI in Europe.
- 1, −tjFDI , which is the lagged FDI penetration index calculated as the number of new foreign firms
established in region j in the period 2001-2003 over region j’s population (mean over the same
period).
- 1, −tjGDP , which is the Gross Domestic Product (GDP) of region j in 2004 and 1,_ −tjpcGDP is the
pro-capita GDP of region j in 2004. These variables capture the effects of market size and labour
cost on foreign firms, respectively.
- 1, −tjA , which is an index of market accessibility. Market accessibility formula is derived from the
index of market potential, as defined by Harris (1954)9, but considers time distances instead of
physical distances. This is to take into account the accessibility to the core EU markets from each
region. As usual, market accessibility is considered a non linear (and inverse) function of transport
costs, proxied by time distances. Indeed we consider the inverse of squared time distances as
weights for GDP. The exact formula of the accessibility index we use is: ∑−
− =i
ji
ti
tjT
GDPA
2
,
1,
1, where
GDPi,t-1 represents the size of the location i in 2004 and Tij is the time distance between i and j. The
traditional formula for market potential, using great circle distances, probably underestimates true
distances, while time distance is a good measure of transport/communication costs.
- 1,1,1,1, _,_,_,_ −−−− tjtjtjtj lqHTlqMHTlqMLTlqLT are the location quotients, calculated on value
added, of 4 groups of manufacturing sectors, i.e. low-tech medium-low-tech, medium-high-tech
9The original formula is as follows: ∑= i
ij
ij
D
GDPPOT , where GDPi represents the size of each location ji≠ , and
Dij is the physical distance between i and j.
10
and high-tech manufacturing sectors.10 The location quotients have been computed as follows:
∑∑=
k
EUk
EUk
k
jk
jk
jkVA
VA
VA
VALQ
,
,
,
,
, / , where ,.kVA is the value added created by sector k in region j and in
the EU, respectively. Location quotients capture localized agglomeration economies.
- Similarly, 1,1,1,1,1,1, _,_,_,_,_,_ −−−−−− tjtjtjtjtjtj lqO;PlqGlqHlqIlqJlqK , are the location
quotients of the following service sectors: real estate, renting and business activities (K); financial
intermediation (J); transport, storage and communication (I); hotels and restaurants (H); wholesale
and retail trade (G); repair of motor vehicles, and motorcycles (O); health and social work, activities
of households and other community (N); social and personal service activities (P).
- 1,1,1,1,1, 13,12,2,4,8 −−−−− tjtjtjtjtj ISCOISCOISCOISCOISCO refer to different ISCO functions and,
respectively, skilled workers, clerks, professionals and managers (SME managers and corporate
managers). These variables are calculated as three year means over the 2002-04 period of the
respective regional shares.
- tj ,ε is the error term referred to the period 2005-2007, which is assumed to be independently and
identically distributed across regions.
The estimation method used in this first part of the analysis is a simple OLS regression. Aware of
the general result found in previous literature on country-level analysis of FDI flows, we then test
for spatial autocorrelation applying the Moran’s I test for global spatial autocorrelation and the
Geary’s C test for local spatial autocorrelation to the final model. Spatial correlation exists when
locations close to each others exhibit more similar values than those further apart. The presence of
spatial correlation either in the distribution of FDI, or in regional characteristics would bias the
results because, if not controlled for, it would violate the standard assumption of any OLS analysis
that the error terms of the model are independent among each others. As it will be discussed later
on, spatial autocorrelation does not seem to affect our results.
4.2 Empirical results
In order to obtain a general model explaining patterns of inward FDI in Europe, we start by testing
the different blocks of explanatory variables mentioned above. Table 2 shows the results.
(insert table 2 about here)
Generally speaking, specifications (1) and (2), where traditional determinants of FDI are taken into
considerations, indicate that FDI inflows in EU regions are more market than efficiency oriented.
10 The branch grouping is taken from OECD. See Table A.1 in the Annex for a detailed description of each group.
11
The estimated coefficients for regional GDP per capita, which we consider as a proxy for labour
costs, are negative but insignificant, while the coefficients for the market accessibility variable are
positive and significant in both specifications. Notwithstanding this result, the reason why GDP
pro-capita turns out to be not significantly different from 0 could be also the fact that this variable is
a very spurious proxy for labor costs. Indeed, it could capture different and contrasting effects: on
the one hand, it could actually represent labor costs – which are negatively related to FDI – but also
productivity effects, and, mainly, wealth effects, which are positively related to FDI. The latter are
very difficult to separate.
Turning to the other explanatory variables, FDI inflows seem to follow a self-sustaining pattern of
agglomeration, given that already existing foreign firms exert a positive effect on new comers.
Localization externalities are no longer a factor of attraction for FDI, with the exception of those
generated within low-tech manufacturing sectors, where cost advantages still matter to successfully
compete with more efficient worldwide producers (i.e. China and India).
Finally, regions belonging to United Kingdom and Romania, all regional characteristics being
equals, receive more FDI than other regions, as indicated by the positive and significant coefficients
of the corresponding dummy variables. These results suggest that country-specific effects play a
role in foreign firms’ location choice.
In columns (3) and (4) we check the explanatory power of what we consider as the “new”
determinants of foreign firms location patterns. Indeed, what empirical evidence tells us is that there
is a clear shift from manufacturing towards service FDI and that the logic underlying MNEs
restructuring is no longer based on production delocalization but on outsourcing of tasks and
functions (…). Our results indicate that specialization in services, and, mainly, in financial
intermediation, transports and communication and retail sales, as well as in specific functions help
in attracting foreign firms. As expected, the latter look for a wide variety of functions, ranging from
specialized workers to corporate managers (ISCO 8, 4, 2, 12), with the exception of clerks.
It is worth noticing that when traditional and new determinants of FDI are considered together, as
we did in specification (5), traditional determinants loose completely their significance, and new
determinants seem to be the only ones able to explain location patterns of FDI. In particular our
results suggest that sector-based production specialization per se is no longer relevant in attracting
FDI: MNEs look for specific functions which are transversal to all industries. Therefore, what
makes a region attractive for foreign firms is its specific human capital endowment. More in details,
MNEs seek plant and machine operators and assemblers but not workers performing elementary
occupations. Moreover, they prefer regions with large endowments of customer service clerks rather
12
than offices clerks. Among professionals, MNEs look for business and teaching professionals11.
Note also that the endowment of SMEs managers has a negative impact on FDI. Thus, empirical
evidence tells us that foreign firms, on average, do not seem to consider as location advantage the
presence of a well developed local industrial fabric, made of small and medium enterprises with
which to establish input-output linkages. A large concentration of small and medium enterprises
may in fact be a signal of the difficulties foreign firm may face in penetrating existing economic
network. Moreover, what really matters in a globalized world is the participation to international
rather than local networks of production. The positive and significant sign of the coefficient of both
already established foreign firm variable and corporate managers regional endowments support this
consideration.
To complete the first part of the analysis we control for spatial autocorrelation by applying the tests
for global spatial autocorrelation to specification (5). This is because existing literature on regional
FDI pattern found evidence of spatial autocorrelation in the distribution of FDIs across Europe (see
Blonigen et al. 2004) and indeed in our previous test (see table 1) we found a significant positive
spatial autocorrelation, even though the Moran’s I was very low, i.e. 0.009.
However, as it is clear from table 3 there is no evidence of spatial correlation, neither for a spatial
error nor for a spatial lag model. This could be explained by the fact that previous literature always
compares regions inside a single European country, while here we are analyzing the distribution of
FDI across all European regions. The only paper that adopts a similar perspective is the one by
Basile, Castellani and Benfratello (2008), who indeed find no evidence of spatial autocorrelation.
These results may indicate that spatial autocorrelation in FDI location patters is likely to occur
within rather than across European countries. As before, we can thus conclude that country-specific
factors matter in FDI location patterns.
(insert table 3 about here)
In the second part of this analysis we test the effectiveness of the general model (model 5)
considering different groups of regions (EU15/ EU12 regions) and a different origin of FDI (extra-
European/ intra-European FDI). The results are reported in table 4 below.
(insert table 4 about here)
11 These disaggregated results are not reported in Table 1 but are available upon request.
13
If we distinguish the effect of independent variables for EU15 regions and EU12 regions, we can
notice a number of differences: first of all services’ sectors specialization of regions play a different
role in attracting foreign firms’ location in EU15 and in EU12. Indeed FDI in EU15 are attracted by
regions specialized in financial intermediation and transport and communication. In EU12 regions
instead the specialization in retail sector emerges as a key determinant of FDIs. Indeed, as explained
very effectively in a book by Kiril Stanilov (2007), there has been an explosion of the retail sector in
CEEC in the post-socialist era. Starting from the end of the 1990s and continuing to expand after
the turn of the millennium, the construction of hypermarkets and malls is mainly driven by two
factors: a psychological effect on a new wealthy population that wants to reach Western standards
and to acquire a cosmopolitan lifestyle and the permissive planning scheme of the region. In fact
international retailers, whose sales remains stagnant in Western Europe, continue to expand in
CEEC. This result offers a plausible interpretation also for the estimated coefficients of the market
accessibility variable, which is positive for EU15 regions and negative for EU12 member states.
Considering that multinational enterprises that located in EU15 regions mainly in financial
intermediation and business activities, while MNE in EU12 are mainly in retail sector we can easily
understand that the dimension of market demand that MNE are interested in is quite different if they
choose to locate in eastern European regions or in western ones. Indeed market demand can be
proxied by total or per capita regional GDP (see Chung and Alcacer, 2002; Coughlin, Terza and
Arromdee, 1991; Braunerhjelm and Svensson, 1996; Head, Ries and Swenson, 1999) and this is the
dimension that best fit retail MNE that locate in EU12. However in some cases multinational firms
can have a market horizon larger than the region’s one, as in the case of business and financial
services enterprises locating in EU15 regions. Indeed, if we consider only these two variables in a
regression, once we control for agglomeration forces and the two outliers, we can find that market
accessibility has a positive effect in attracting FDI, being significantly different from 0 in EU15
regions while GDP pro-capita has a significant and positive effect only in EU12 regions.
Note also that the negative impact of SME managers on regional FDI attractiveness is driven by
EU15 regions, while in EU12 regions empirical evidence tells that foreign firms are linked with the
local related industries. This result, again, could depend on the different sector distribution of FDIs
in eastern and western regions.
Finally, when considering separately the determinants of FDI location depending on whether they
are extra-European or intra-European flows, the main difference is that extra-EU multinationals
give more importance to local market demand and they look mainly for regions specialized either in
financial intermediation or in retail activities. Retail specialization is much more important for intra-
European FDI flows, which seems to indicate that retail explosion in EU12 region is mainly an
14
intra-European phenomenon, driven by the expansion of big retail companies, whose sales in
Western Europe remain stagnant.
(insert table 5a about here)
(insert table 5b about here)
A sector-based perspective, though potentially useful, do not allow us to further enrich previous
results (see tables 5a and 5b). The most striking difference with respect to the general trends
described above concerns the role of foreign agglomeration. In particular, we found that while FDI
in manufacturing is attracted by other already existing manufacturing foreign firms, FDI in service
is sensitive to foreign agglomeration, regardless of the sector of activity (manufacturing vs.
services) of incumbent foreign firms. Romania and the UK collect, ceteris paribus, a larger number
of manufacturing but not service foreign firms. It is also interesting to notice that service FDI
seems to serve mainly low-tech sectors in EU15 and high-tech sectors in EU12 regions, where
services FDI support also local firms, given the positive elasticity to regional endowments of SME
managers. The negative sign of location quotients’ coefficient in the case of low-tech for EU12,
instead, could be interpreted as a sign of the fact that in the case of EU12 regions services FDI
support mainly other FDI in low-tech sectors rather than local low-tech firms. This idea is
confirmed by the fact that EU12 regions attracted mainly low-tech FDI and that the latter were
attracted mainly in regions with low concentration of low tech firms, as corroborated by the
negative elasticity of manufacturing FDI inflows with respect to low-tech location quotients. This
effect could be explained by the fact that foreign firms operating in low-tech sectors show a similar
productivity as local firms (see Nicolini and Resmini, 2007), thus may prefer to locate in regions
where local low tech sector is less developed because competition effect is weak.
5. Conclusion
This paper investigated factors influencing the distribution of foreign firms across European
regions. Using sectoral and regional data on FDI inflows during the 2005-07 period, we found that
traditional variables, typically used to explain FDI at country level, are no longer significant when
new variables are included in the model. The latter concern mainly human capital endowments,
measured through functions rather than levels of education. Among traditional determinants, only
market accessibility and foreign agglomeration play a key role in explaining observed foreign firms
patterns of location. The latter are also affected by unobserved country specific effects.
15
We also found that further insights into the determinants of FDI location patterns in the EU can be
provided by considering not only different branches of activities, but also the origin of foreign firms
inside or outside Europe.
6. References
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case of three European regions”, HEC-ULg working paper n. 200611/07
Barba Navaretti G. and Venables A. (2004), Multinational Firms in the World Economy”, Princeton
University Press.
Basile R., Castellani D. and Benfratello L. (2008), “Location determinants of foreign firms’
business functions in the enlarged Europe: evidence from negative binomial additive models”,
paper presented to the XXIX AISRe National Conference, Bari (Italy, 24-26 September 2008.
Blonigen B.A. (2005), “A Review of the Empirical Literature on FDI determinants”, NBER
Working Paper No. 11299.
Blonigen B.A., Davies R.B., Waddel G.R. and Naughton H.T. (2004), “Fdi in space: spatial
autoregressive relationships in foreign direct investment”, NBER working paper n.10939.
Braunerhjelm P. and Svensson R. (1996), “Host country characteristics and agglomeration in
foreign direct investment”, Journal of Applied Economics, (28), pp. 833–840.
Chung W. and Alcacer J. (2002), “Knowledge seeking and location choice of foreign direct
investment in the United States”, Management Science, 48(12), pp.1534–1554.
Coughlin C., Terza J. and Arromdee V. (1991), “State characteristics and the location of foreign
direct investment within the United State”, Review of Economics and Statistics, 73, pp. 675–683.
Crozet M., Mayer T. and Mucchielli J.L. (2004), “How do firms agglomerate? A study of FDI in
France”, Regional Science and Urban Economics, 34, pp. 27-54.
European Commission (2006), “Where do multinationals locate? The location patterns of
multinationals across European regions”, in The EU economy: 2005 review, Part III – Specific
Studies, Bruxelles.
Fujita M. and Thisse J. (2002), Economics of Agglomeration, Cambridge University Press.
Head C., Ries J. and Swenson D. (1999), “Attracting foreign manufacturing: investment promotion
and agglomeration”, Regional Sciences and Urban Economics, 29, pp. 197–218.
Helpman E. (1984), “A Simple Theory of International Trade with Multinational Corporations”,
Journal of Political Economy, 92(3), pp. 451-471.
16
Kravis I.B. and Lipsey R.E. (1982), “The Location of Overseas Production and Production for
Export by U.S. Multinational Firms”, Journal of International Economics, 12, pp. 201-223.
Markusen J. R. (1984), “Multinationals, Multi-Plant Economies, and the Gains from Trade”,
Journal of International Economics, 16(3-4), pp. 205-226.
Mody A. and Wheeler D. (, “International Investment Location Decisions: The Case of U.S.
firms”, Journal of International Economics, 33, pp. 57-76.
Nicolini M. and L. Resmini (2007), “Productivity Spillovers from foreign direct investment: the role of the
neglected conditionalities” (with M. Nicolini) DYNREG working paper n. 11
Pusterla F. and Resmini L. (2007), “Where do foreign firms locate in transition countries? An
empirical investigation”, The Annals of Regional Science, 41 (4), pp. 835-856.
Shatz H. and Venables A.J. (2000), “The Geography of International Investment”, World Bank
Policy Research Working Paper Nº 2338.
Stanilov K, The Post-Socialist City , Springer edition, 2007
17
Map 1 The location pattern of newly established foreign affiliates in EU (2005-07)
Map 2 The location pattern of newly established foreign affiliates operating in services sectors
(2005-07)
18
Map 3 The location pattern of newly established foreign affiliates operating in manufacturing
sectors (2005-07)
TABLE 1 Moran’s I statistics on the different dependent variables included in the analysis
Variables Moran's I z p-value*
Total FDI 0.009 2.227 0.013
Extra Europe FDI 0.000 0.609 0.271
Intra Europe FDI 0.010 2.444 0.007
FDI in services 0.009 2.199 0.014
FDI in manufacturing 0.009 2.119 0.017
*1-tail test
19
TABLE 2 FDI location patterns: traditional vs. new determinants
all regions - Total FDI
Variables (1) (2) (3) (4) (5)
GDP 0.12 - - -
GDP pro capite -0.13 -0.01 0.11 0.09 0.12
market accessibility 0.37 *** 0.41 *** 0.25 *** 0.12 *** 0.08
Manufacturing LT - 1.26 *** - - 0.36
Manufacturing MLT - -0.52 *** - - 0.38
Manufacturing MHT - -0.10 * - - 0.01
Manufacturing HT - 0.11 - - -0.17
Services K - - -0.88 * - -
Services J - - 0.77 *** - 1.09 ***
Services I - - 1.44 *** - 0.55 **
Services H - - -0.68 *** - 0.30
Services G - - 2.80 *** - 2.62 ***
Services ONP - - -0.54 - -
plant and machine operators and assemblers - - - 6.21 ** 8.78 ***
clerks - - - -0.46 0.07 **
professionals - - - 4.36 ** 4.60 **
corporate managers - - - 30.62 *** 24.96 ***
SME managers - - - -10.28 ***
-12.43 ***
Dummy RO 3.07 *** 2.50 *** 2.50 *** 3.78 *** 3.54 ***
Dummy UK 1.24 *** 1.13 *** 1.22 *** -0.45 * -0.32
FDI (t-1) 0.38 *** 0.39 *** 0.426 *** 0.32 *** 0.38 ***
Adjusted R2 0.59 0.60 0.73 0.74 0.83
OBS. 260 260 260 260 260 OLS estimation includes a constant term. *** = significant at 1% level; ** = significant at 5% level; * = significant at 10% level
TABLE 3 Spatial autocorrelation diagnostics
Test [model (5)] Statistic df p-value
Spatial error:
Moran's I -0.59 1 1.444
Lagrange multiplier 0.86 1 0.353
Robust Lagrange multiplier 0.81 1 0.369
Spatial lag:
Lagrange multiplier 0.11 1 0.745
Robust Lagrange multiplier 0.05 1 0.824
20
TABLE 4 FDI location patterns: EU15 vs EU12 regions and ExtraEU vs IntraEU investors
total FDI- different groups of regions eu15 eu12
¥
all regions - different origin FDI
extra-Eu
intra-Eu
Variables (5) (5) Variables (5) (5)
GDP pro capite 0.10 0.14 GDP pro capite 0.14 * 0.12
market accessibility 0.14 ** -0.37 ** market accessibility 0.03 0.06
Manufacturing LT 0.80 *** 0.08 Manufacturing LT -0.40 * 0.43
Manufacturing MLT 0.08 0.21 Manufacturing MLT -0.06 0.42
Manufacturing MHT -0.10 0.36 Manufacturing MHT 0.12 -0.02
Manufacturing HT -0.23 0.80 * Manufacturing HT 0.22 -0.26
Services J 0.75 *** 0.15 Services J 1.10 *** 1.04 ***
Services I 1.13 *** 1.12 *** Services I 0.12 0.49 *
Services H 0.54 *** -1.32 * Services H 0.08 0.34 *
Services G -0.33 4.66 *** Services G 1.30 *** 2.89 ***
plant and machine operators and assemblers 12.32 *** -8.21
plant and machine operators and assemblers 7.69 *** 9.28 ***
clerks 5.24 * -1.57 Clerks 1.27 0.82
professionals 3.40 -1.83 professionals 5.20 *** 4.62 ** corporate managers 28.02 *** -12.51 corporate managers 22.88 *** 24.30 ***
SME managers -10.66 *** 22.05 * SME managers -9.25 *** -12.04 ***
Dummy RO 4.48 *** - Dummy RO 4.64 *** 3.37 ***
Dummy UK -0.49 ** - Dummy UK 0.69 *** -0.57 **
FDI 0.40 *** 0.11 * FDI 0.30 *** 0.38 ***
Adjusted R2 0.85 - Adjusted R2 0.87 0.78
OBS. 260 - OBS. 260 260 OLS estimation includes a constant term. *** = significant at 1% level; ** = significant at 5% level; * = significant at 10% level
¥= these coefficient are not part of a separate analysis but they are calculated in a joint regression with the ones reported in the second column
(EU15), including both the independent variables reported in the first column and interaction terms between them and a dummy variable equal to 1 for regions belonging to EU12 member states and equal to 0 otherwise.
21
TABLE 5a The determinants of service FDI
services FDI- different groups of regions total total eu15 eu12
¥
Variables (5) (6) (5) (5)
GDP pro capite 0.17 0.16 0.15 0.04
market accessibility 0.02 0.03 0.05 -0.34 *
Manufacturing LT 0.26 0.19 0.73 ** -2.10 **
Manufacturing MLT 0.23 0.18 0.00 0.34
Manufacturing MHT 0.10 0.15 -0.01 0.44
Manufacturing HT -0.20 -0.17 -0.43 ** 1.25 ***
Services J 1.29 *** 1.33 *** 0.96 *** 0.04
Services I 0.62 ** 0.66 ** 0.92 *** -0.28
Services H 0.33 * 0.37 * 0.46 ** -0.82
Services G 2.79 *** 2.84 *** 0.53 3.85 ***
plant and machine operators and assemblers 8.02 *** 6.37 ** 9.94 *** -9.96
clerks 1.41 1.14 4.87 * 0.95
professionals 5.20 ** 4.17 ** 3.54 -1.62
corporate managers 25.81 *** 25.53 *** 28.72 *** -11.33
SME managers -
11.37 *** -
12.17 *** -12.05 *** 20.90 *
Dummy RO 3.84 *** 3.67 *** 4.84 -
Dummy UK -0.31 -0.44 * -0.51 -
FDI 0.47 *** - 0.51 *** 0.04
FDI manufacturing - 0.40 *** - -
FDI services - 0.14 * - -
Adjusted R2 0.84 0.84 0.85 -
OBS. 260 260 260 - OLS estimation includes a constant term. *** = significant at 1% level; ** = significant at 5% level; * = significant at 10% level
¥= these coefficient are not part of a separate analysis but they are calculated in a joint regression with the
ones reported in the second column (EU15), including both the independent variables reported in the first column and interaction terms between them and a dummy variable equal to 1 for regions belonging to EU12
member states and equal to 0 otherwise.
22
TABLE 5b The determinants of Manufacturing FDI
manufacturing FDI- different groups of
regions total total eu15 eu12¥
Variables (5) (6) (5) (5)
GDP pro capite 0.06 0.09 0.04 0.19 *
market accessibility 0.07 0.03 0.12 ** -0.34 **
Manufacturing LT 0.23 -0.02 0.58 ** -1.67 **
Manufacturing MLT 0.57 ** 0.42 * 0.31 -0.27
Manufacturing MHT -0.05 0.12 -0.11 0.19
Manufacturing HT -0.19 -0.05 -0.13 0.36
Services J 0.46 ** 0.81 *** 0.12 0.36
Services I 0.28 0.61 ** 1.04 *** -2.62 ***
Services H 0.02 0.05 0.36 ** -1.76 ***
Services G 2.12 *** 2.29 *** -1.42 *** 5.31 ***
plant and machine operators and assemblers 8.77 *** 5.04 ** 12.67 *** -3.26
clerks 0.95 2.38 7.95 *** -3.50
professionals 1.82 0.67 1.48 -3.69
corporate managers 21.11 *** 20.13 *** 24.34 *** -12.72
SME managers -10.34 *** -11.96 *** -6.72 ** 21.79 **
Dummy RO 3.18 *** 3.14 *** 3.80 *** -
Dummy UK -0.14 -0.37 * -0.28 -
FDI 0.22 *** - 0.20 *** 0.19 **
FDI manufacturing - 0.60 *** - -
FDI services - -0.23 *** - -
Adjusted R2 0.76 0.79 0.83 -
OBS. 260 260 260 - OLS estimation includes a constant term. *** = significant at 1% level; ** = significant at 5% level; * =
significant at 10% level
¥= these coefficient are not part of a separate analysis but they are calculated in a joint regression with the ones reported in the second column (EU15), including both the independent variables reported in the first
column and interaction terms between them and a dummy variable equal to 1 for regions belonging to EU12
member states and equal to 0 otherwise.
23
A��EX
Table A1_ List of sector included in the database.
AB Agriculture, hunting and forestry; Fishing
C Mining and quarrying
DA Manufacture of food products, beverages and tobacco
DBDC Manufacture of textiles and textile products; Manufacture of leather and leather products
DD Manufacture of wood and wood products
DE Manufacture of pulp, paper and paper products; publishing and
DFDG Manufacture of coke, refined petroleum products and nuclear fuels; Manufacture of chemicals, chemical products and man-made fibres
DH Manufacture of rubber and plastic products
DI Manufacture of other non-metallic mineral products
DJ Manufacture of basic metals and fabricated metal products
DK Manufacture of machinery and equipment n.e.c.
DL Manufacture of electrical and optical equipment
DM Manufacture of transport equipment
DN Manufacturing n.e.c.
E Electricity, gas and water supply
F Construction
G Wholesale and retail trade; repair of motor vehicles, motorcyc
H Hotels and restaurants
I Transport, storage and communication
J Financial intermediation
K Real estate, renting and business activities
L Public administration and defence; compulsory social security
M Education
N Health and social work
OP Other community, social and personal service activities; Activities of households
low-tech sectors: manufacture of food products, beverages and tobacco; manufacture of textiles
and textile products; manufacture of leather and leather products; manufacture of wood and wood
products; manufacture of pulp, paper and paper products; manufacturing n.e.c.;
Medium-low-tech sectors: manufacture of rubber and plastic products; manufacture of other
non-metallic mineral products; manufacture of basic metals and fabricated metal products;
Medium-high-tech sectors: manufacture of coke, refined petroleum products and nuclear fuel;
manufacture of chemicals, chemical products and man-made fibres; manufacture of machinery
and equipment n.e.c.; manufacture of transport equipment;
High-tech sectors: Manufacture of electrical and optical equipment.
24
Figure A1. Representativeness of the sample: Official inflows of FDI (millions of USD) vs. newly
established foreign firms (2005-07).
-10000
90000
190000
290000
390000
490000
590000
at be bg cy cz de dk ee es fi fr gr hu ie it lt lu lv mt nl pl pt ro se si sk uk0
5000
10000
15000
20000
25000
30000
35000
40000Inflow s of FDI
n. of foreign f irms
Pearson correlation coefficient: 0.626; p-value>0.000.
