Where do Multinationals Locate Service andManufacturing Activities in Europe and Why ?∗Loriane Py †Fabrice Hatem ‡Preliminary version, August 2009
Abstract
The recent surge of FDI in services has the potential to increase the exposure of high educated workers to international competition. This tends toreinforce fears about offshoring, especially in Europe, where Western countries
are increasingly concerned about the possible disappearance of their service
and manufacturing industries. In this paper we try to quantify the threatof offshoring by investigating 14 000 location choices in Europe of multinational firms originating from 91 countries over the period 2002-2006. A firstdescriptive analysis reveals differences in the location of manufacturing andservices in Europe. We then try to explain these patterns by comparing location determinants, first at the sectoral level (to compare the service withthe manufacturing sector), and second at the functional level (by distinguishing production, headquarters, R&D, distribution, commercial offices, service
provision and call centers). Sectoral results indicate that most developed
economies remain relatively sheltered from international competition. Whilethe location of both the manufacturing and the services sector is very sensitive
to market size and to cultural proximity, skilled abundant economies are more
attractive for FDI in services. These findings are confirmed by the analysis
at the functional level. In contrast to the location of production units, thelocation of services functions is more driven by market access and skilled resources than by cost considerations. In particular, skilled labor resources are
an important determinant for the location of skilled intensive activities suchas headquarters or service supply whereas this is not the case for the locationof call centers. Hence the results suggest that in Europe, high income andskilled abundant countries tend to specialize in services. However, this trend
is less clear for low skilled and easily offshorable activities.

JEL classification: F12, F15, F23.
Keywords: Location choices, Multinational Firms, Conditional Logit, Services.
∗

We are grateful to Thierry Mayer for his useful comments and advices. We would like also to
thank Farid Toubal and Fabrice Defever for their helpful remarks and the French - German ANR
on FDI in services for financial support. All remaining errors are ours.
†
University Paris 1 - Paris School of Economics, Email: loriane.py@univ-paris1.fr.
‡
Economist at the UNCTAD(Geneva), Director of Research at the METIS laboratory (Normandy School of Management), Email: Fabrice.Hatem@unctad.org

1

1

Introduction

The increase in international investment in services is one of the most striking
features of current trends, with Foreign Direct Investment (FDI) in services accounting now for the largest share of global FDI stock and flows (UNCTAD, 2004 and2008). There are in fact two separate trends that contribute to the rapid growthof FDI in services: first the internationalization of the service sectors, and second,
the internationalization - including within manufacturing firms - of tertiary support
functions1 . As pointed out by Richard Baldwin (2006), one important implicationof this new trend is that “international competition - which used to be primarily between firms and sectors in different nations - now occurs between individual workers
performing similar tasks in different nations” 2 . This phenomenon, which increases
workers exposure to international competition, reinforces the anxiety about the potential negative consequences of firms’ internationalization, especially in developed
countries.
Not surprisingly, the question of service offshoring has generated a great dealof attention. Three questions have been addressed by the recent literature. First,
some studies have focused directly on the effect of offshoring on labour demand.
This strand of the literature concludes to quite limited effects in magnitude (see for
instance Barba Navaretti and Castellani 2004; Amiti and Wei, 2005), or effects thatvary depending on the income-level of destination countries (Ekholm and Hakkala,2006). Second some studies have analyzed whether, as for goods, the remote supply of services remains limited. Head et al.(2008) find that distance shields workers
to a significant extent from the threat of offshoring, but that distance costs havedeclined over the period of study. Finally, recent studies have focused on the location of FDI in services, in order to determinate whether, as for manufacturing,
investments are more driven by market size and agglomeration effects than by costconsiderations (see for instance, Head and Mayer, 2004; Crozet et al., 2004). Nefussi
and Schwellnus (2007) compare the location determinants in the manufacturing andin the service sectors of French firms abroad. While they find no fundamental difference between the location of these two types of activities, they demonstrate thatthe location of business services is dependent of the French downstream demand
generated by manufacturing affiliates, suggesting that domestic services production
will decrease together with manufacturing production. Defever (2006) examines thelocation determinants of services surrounding production activity. He demonstrates
that each function favors different country characteristics but also find evidence ofsome complementarities at the firm level, especially between the location of R&D
and production.
In this paper, we try to contribute to this latter strand of the literature by investigating the location determinants of FDI in services in Europe, where Western
European countries are increasingly concerned about the possible disappearance of
their manufacturing and service base. We first analyze the location patterns of
14 000 investments projects over the period 2002-2006. This descriptive analysis re1

This refers to the so-called international fragmentation of the value chain in which firms engage
in service activities that surround production and where the production stage itself only accounts
for one stage.
2
Baldwin (2006), The great unbundling(s),page 52

veals significative differences in the location of manufacturing and services especiallybetween Eastern and Western Europe. We then try to explain this difference by comparing location criteria, first, at the sectoral level (to compare the service with the
manufacturing sector) and second, at the functional level (by distinguishing headquarters, R&D, production, distribution, commercial offices, service provision andcall centers).
Our contributions are twoflod. First, contrary to Nefussi and Schwellnus (2007)
and Defever (2006) who focus respectively on the service sector only and on service
activities carried out by manufacturing firms only, our analysis incorporates boththe sectoral and the functional dimension of service activities. This presents two advantages. First, as pointed by Trefler (2005), with the international fragmentation of
the production process, the distinction between the manufacturing and service sectors is no longer relevant as many manufacturing firms carry out services activities
that surround production. Hence by focusing on the service sector only, we would
underestimate the importance of service activities. Second, our approach extends
the literature that has recently introduced the functional dimension of investmentin the analysis of location criteria (Strauss-Kahn and Vives, 2005; Defever, 2006;
Sachwald and Chassagneux, 2008)3 . In particular, it allows us to assess the relevance of the sectoral approach as compared to the functional one when it comes to
identify location criteria. Besides, our empirical analysis is based on a very-large
dataset which enables us to consider investment carried out by non European andEuropean multinational firms in 29 countries in Europe, including new members of
the European Union. Europe is of particular interest as it became the largest recipient of foreign direct investments over the past decade. Other studies have focusedon Europe (Disdier and Mayer, 2004; Defever, 2006; Basile et al., 2008), but our
database covers a relatively more recent period and a larger sample of origin andrecipient countries than the previous ones. Hence, our dataset accounts for a quite
representative sample of all investments received by European countries in a context
of rise in FDI in services and of European Union enlargement.
Our results indicate that, as for manufacturing, location decisions in services
are very sensitive to market size and to cultural proximity (measured by a sharedlanguage). But in contrast to the manufacturing sector, countries featured by highskilled labor resources are particularly attractive for FDI in services. This suggeststhat in spite of the present increase of FDI in services, developed economies remain
relatively sheltered from international competition. The results by function suggestthat functional approaches are more appropriate than sectoral ones when it comesto identify location criteria both for service and production activities. The market
size criterion, which is important when service activities are concerned, has no effectwhen it comes to call centers, which are likely to operate at some distance fromthe final consumer. The wage costs criterion is of decisive importance only whenchoosing where to locate production centers. Skilled labor resources are an important determinant for the location of functions especially skilled intensive ones suchas headquarters. Finally, results of the nested logit show that in manufacturing activities, location decisions follow an East-West structure which is mainly influenced
3

Strauss-Kahn and Vives (2005) examine the determinants of the relocation of headquarters in
the US; Sachwald and Chassagneux (2008) study the location of R&D centers in Europe; Defever(2006) compares location criteria by function (headquarters, R&D, production, logistics, sales)3

by a trade-off between wage costs and market size. However, the location of services
is more driven by national specificities.
The remaining part of the paper is organized as follows. Section 2 presents thedata on investment projects from which are presented the key features of FDI in
Europe. Section 3 presents the theoretical framework from which is derived the reduced form that we estimate. In section 4, we present the econometric methodology.
Section 5 describes the data and the explanatory variables that are introduced in ourestimations. The section 6 illustrates the econometric results. Section 7 concludes.

2

Main features of FDI in Europe

2.1

Firm-level Data on Location Decisions

Each year, the Invest In France Agency (IFA)4 Observatory collects data on tangible investment projects in Europe. The data used in this study cover the period
2002-2006. During this short period, nearly 14 0000 investments have been carriedout by multinational firms from 91 countries in 29 European countries5 . European
investors were nevertheless responsible for 55% of the projects and North Americans
for one-third. The data relate solely to greenfields and brownfields projects (which
accounted, respectively, for 2/3 and 1/3 of investment). The dataset contains very
detailed information for each recorded project: name of the investing firm, country
and date of set-up, sector of activity and function within the firm. This dataset isgathered thanks to official announcements or by reading the international economic
press and the information available on the web (press agency, sites...). A comparison
with other databases (Ernst and Young’s European Investment Monitor, IBM-PLI’sworld base GILD) which points to quite good data compatibility, enables to limit
potential problems of non exhaustiveness. Such a comparison, together with a verification procedure, ensures that the quasi totality of projects registered have beenachieved.
The main advantage of this database is that projects are registered along two
dimensions. First, investments projects are classified according to their sector of activity, which corresponds to the main line of business in which the investing firm isactive. Second, investments projects are classified according to the function (headquarter, R&D, production, distribution, commercial office, service provision, callcenter and on-line service). For the reasons outlined in the introduction, this classification is particulary important for our purpose (table 7 and 8 of the appendix
present the sectoral and functional nomenclature od the IFA). As shown by table
1, most of the firms pertaining to the manufacturing sector engage in some service
activities that surround production. So in the following of the study we define two
measures of service activities. The first one, the narrow measure of services refers
to investments pertaining to the service sector. The second one, the broad measureof services refers to all functions which involve some service activities (whatever thesector) by opposition to production activities. Let’s turn now to the presentation of
the main patterns of international investment in Europe.
4

Public Institution under the supervision of French Ministry of Economy and Finance.
These are the 27 Member Countries of the European Union, with the exception of Cyprus.
The non-EU countries contained in the dataset are Switzerland, Norway and Iceland.
5

4

Table 1: Classification of the Invest in France Agency
Function
Production
Headquarters
R&D centres
Distribution
Commercial OfficeService Provision
Call centers, on-line Services
Total Service functions
Total number
Total in %% going toEastern Europe% going toWestern Europe

2.2

Manufacturingsector
4926
491
530
430
1635
248
48
3382
8308
59,76%
35,80%

Service
Sector
9
446
207
629
3045
1005
253
5585
5594
40,24%
16,20%

64,20%

83,80%

Total
Total
4035
937
737
1059
4680
1253
301
8969
13902

Total
in %
35,40%
6,74%
5,30%
7,62%
33,66%
9,01%
2,17%
64,60%% going toEastern Europe
47,90%
7,20%
18,00%
25,80%
15,20%
20,10%
25,90%
16,90%% going toWestern Europe
52,10%
92,80%
82,00%
74,20%
84,20%
79,90%
74,10%
83,10%

100%
27,90%
72,10%

Main location patterns of international investments in
EuropeEurope is a major recipient of FDI worldwide. While it has become the largest
host region over the past decade6 , countries within Europe remain very unequal asregards their performances in terms of inward investments, as shown by the twographics.

According to the dataset of the Invest in France Agency, France and the United
6

According to the UNCTAD (2008), the European Union attracted almost two thirds of total
FDI inflows into developed countries in 2007.

5

Kingdom are the top recipients countries, they each received more than 14% ofinvestments projects investments over the period 2002-2006. In contrast, peripheral
countries such as Greece, Malta, Iceland and Slovenia attracted less than 0,5% of
total inward FDI.

Turning to the sectoral and functional composition of inward investments, theanalysis of Table 1 confirms the importance of FDI in Services. First, while the shareof manufacturing is globally preponderant in Europe over the period of study, 40%of the projects carried out during the period in question belong to the service sector.
Second, turning to the broad measure, over 64% of the projects performed involve
service activities. More importantly, as shown by the graphics, the geographicaldistribution of projects reveals patterns of specialization that differ between Easternand Western countries. The first of these two regions appears to be very attractive
to manufacturing activities, while West European performance levels seem distinctly
preferable for high value-added and service activities. What is more, almost 84% ofservice sector projects were set up in Western Europe during the period in question.
Turning to the broad measure of services, the geographical repartition of activities
is quite similar. While production centers were located both in Eastern and Western
Europe, only 17% of projects pertaining to service functions were located in Eastern
Europe over the period. This descriptive analysis suggests that while multinationals
tend to locate manufacturing both in Eastern and Western Europe, services activities
are more prone to be located in Western countries. This suggests that there might
be some specificities in the location determinants of FDI in Services.

6

3

Theoretical Framework

The existing literature has largely benefited from recent developments in the
New Economic Geography theory 7 . The underlying models assume that a firm will
choose the location that maximizes its profits. Before describing the reduced form
that we estimate, we first present the conclusions of the existing literature that still
remain relatively limited as regards potential specificities of FDI in services.

3.1

Related literature

The existing literature suggests that the horizontal motive for FDI (Markusen,
1984) is more important than the vertical one (Helpman, 1984)8 . In such a context, most empirical studies which focused on the manufacturing sector, conclude
that the market access is a core determinant in location decisions (see for instanceHead and Mayer, 2004; Amiti and Javorcik, 2006). This criterion also appears decisive in the location of service sectors (Nefussi and Schwellnus, 2007). However,while the divide between these two motives is clear on the theoretical side, with theinternational fragmentation of the value chain, this frontier becomes less clear on
the empirical side. The location of some service activities, in particular those thatsurround production can be less sensitive to market size. The studies by Sachwald
and Chassagneux (2007) and by Kuemmerle (1997) show that R&D centers specialized in adapting products have a strong propensity to locate close to their final
markets, while this particular criterion does not seem decisive in the case of fundamental R&D. This criterium neither seems to be important in the case of call centersand on-line services, (Hatem 2005), or as regards the the location of headquarters(Strauss-Kahn and Vives, 2005).
Agglomeration forces are also central in location decisions. The location ofJapanese firms in the United States appears to be strongly influenced by the presenceof firms of the same nationality and belonging to the same sector (Head et al., 1995,
1999). Similar effects have been observed in France (Crozet et al., 2004). These
sectoral or industry influences have recently been confirmed by the work of Headand Mayer (2004)9 . Agglomeration effects can, however, differ in intensity depending on the type of activity. They can also obey functional arguments. Recent urban
economy studies (Duranton and Puga, 2005) point to the existence of a functional
specialisation dynamic at work in numerous metropolitan areas. Strauss-Kahn andVives (2005) show that headquarters relocation in the United States is greatly influenced by the above phenomenon, firms preferring to set up in urban areas where
there are already large numbers of headquarters, preferably in the same sector of activity. Defever and Mucchielli (2004)and Defever (2006) find evidence of functional
agglomeration in the case of headquarters and R&D centers in Europe. It may be,
therefore, that agglomeration forces affecting service activities are influenced moreby functional than by sectoral considerations. Besides, as the international communication costs decline, the different functions or stages of the value chain can be
7

These developments are mainly attributable to Fujita et al.(1999), Fujita and Thisse (2002),
Head and Mayer (2004).
8
See Markusen (2002) and Yeaple (2003) for a synthesis of these two motives in an integrated
framework.
9
These authors are the first ones to control for real market potential.

7

located autonomously on a broadened geographical base. However, this autonomy
is not complete and the location of some functions can still be sensitive to the earlier presence of complementary functions. Defever (2006), analyses these co-location
phenomena and shows that there is, within the same given firm, a mutual attraction
between R&D activities and production. We will therefore verify the existence of
these co-location effects between all business functions10 .
Also tested in the literature is the effect that taxation and set-up subsidies haveon location decisions. On the whole, the level of tax has the expected negative
impact (Devereux and Griffith, 1998; Bénassy-Quéré et al., 2003), while the effectof subsidies often appears positive but marginal - even at regional level (Crozet etal., 2004, Basile et al., 2008). Our segmented approach will enable us to check theassumption that sensitivity to tax pressure may or may not vary according to thenature of the activity concerned.
Regarding wage costs, the existing empirical literature does not produce any
clear verdict as to whether wage costs have a major impact on location decisions.
A number of studies even arrive at the conclusion that they are not a significant
variable (Devereux and Griffith, 1998; Head et al., 1999; Head and Mayer, 2004).
However, as pointed out by Liu et al., (2006), the effect of wages may previously havebeen underestimated in empirical work 11 . A number of analyses can be put forward
to explain these findings. First, wages also reflect the skill level of workers. Second,regions with a high market potential are also those where wages are highest (Headand Mayer, 2006). Lastly, it seems that estimations that are too global can mix up
activities whose location is sensitive to wage costs to varying degrees. In particular,
Defever (2006) finds that the wage cost criterion is important only for the locationof the production stage. A more detailed analysis should allow to disentangle theeffect of wages on location decisions.
Finally, a number of surveys conducted among multinational firms confirm theinfluence of labour skills on location decisions. According to UNCTAD (2007), it
plays an important role. However, it has often been used as a control variable to
avoid the cost of labour also reflecting skill levels. In reality, it is a factor that canhave a decisive influence where certain activities are concerned. Maurin and Thesmar(2004) show that the overall increase in the need for skilled staff is due to structuralchanges taking place in firms, the fact being that the share of labour involved inbasic production tasks is diminishing because the said activities can be automatized,
whereas the share of functions that are difficult to programme in advance (R&D,
marketing) is increasing. So what is needed is to measure the differing impact of thevariable in question by sector and by function. For the reasons just outlined, theinfluence of this variable can be expected to be generally greater in tertiary activities
than in manufacturing.
10

Nefussi and Schwellnus (2007) also demonstrate the existence of complementarities between
the location of services and manufacturing but for the subsector of business services only, that is
why we test the existence of such complementarities only in the analysis by function.
11
There could in fact be potential endogeneity problems. However, we are unlikely to be facedwith this concern. For the entry of firms to have a significant impact on the cost of labour, there
would have to be a sudden, massive influx of firms in a limited space of time and only reduced
labour displacement. These conditions were contained in their study on China at the regional level,
but can hardly be applied in national-level studies.

8

3.2

Underlying Model

The reduced form that we estimate is based on the recent development of the
New Economic Geography Theory. Our theoretical framework follows Head andMayer (2004). Take a firm which locates its production in region i, i=1,...,R where
R is the number of regions. The firm uses the labor and inputs available in theregion in order to produce. Each firm produces a variety of a differentiated good in
an industry assumed to be representative. Consumers have a constant elasticity of
substitution between sub-utilities and maximise this utility function in relation totheir expenditure. Demand emanating from a representative consumer in region jfor a firm located in region i is given by:
p−σij
1−σ Ejr=1 nr prj

qij = PR(1)

where Ej is expenditure by a representative consumer in region j, where s is theelasticity of substitution between varieties and where pij is the price “after delivery”
paid by the consumer in region j for a good produced in all possible regions R. The
above delivery price is a combination of the mill price and iceberg-type transport
costs τ . If it is assumed that the representative industry is in a monopolistic competition à la Dixit-Stiglitz (1977), to obtain the optimum price the firm sets a constantmark-up over costs:
σ
ci
(2)
pi =
σ−1
where ci is the marginal cost of production of the representative firm located inregion i. By substituting (2) into (1), we obtain the quantity that a firm in region i
can supply in each destination j:qij =(ci τij )−σ
σ−1
E
PR
1−σ j
σ
r=1 nr (cr τrj )(3)

By introducing the fixed costs F attaching to setting up a new plant, the profit
obtained by a firm located in region i for each destination region j is given by:
πij = (pi − ci )τij qij − Fi(4)

By substituting expressions (2) and (3) into (4), the profit obtained by a representative firm located in region i on the destination market j is given by:ci σ − 1(ci τij )−σ1(ci τij )1−σ
πij = τij
E − Fi = PR
Ej − Fi
PR
1−σ j
σ−1 σ
σ r=1 nr (cr τrj )1−σ
r=1 nr (cr τrj )(5)

By adding together the potential profits earned on each market, we obtain thefirm’s net aggregate profit earned in each potential location r:
Πr =

R
X

πrj =

j=1

9

cr1−σ
Mr − Fr
σ

(6)

with
Mr =

R
X
j=1

φrj
E
1−σ j
r=1 nr (cr τrj )

PR

(7)

In line with Krugman (1992), Mr represents the “Real Market Potential”. At
this point, we add a further assumption to the Head and Mayer model (2004) : like
Devereux and Griffith (1998), we assume that the firm pays a rate of tax on grossprofits. The firm’s net profit is therefore represented by:c1−σ(8)Πr = (1 − Tr ) r Mr − Frσ
When choosing a location, the firm compares the profits to be made in different potential locations. To derive a profit equation which is easier to manipulate
at the estimation stage, Head and Mayer (2004) propose to perform a number oftransformations which we have adopted too. They begin by assuming that the fixed
production cost is the same everywhere (i.e. Fr = F, ∀r), and they then add it toprofits; next, they multiply the expression obtained by s, and lastly they write thisexpression to the power 1/σ − 1. Initially, they thus obtain Vr :1

Vr = [σ(Πr + Fr )] σ−1(9)After a transformation on logrithms:
Ur ≡

1
1
lnMr − lnCr +ln(1 − Tr )
σ−1
σ−1

(10)

The equation above shows that profits increase with Market Potential (Mr ) anddecrease with variable costs (Cr )and with tax rate on corporate profits (Tr ). Where
variables costs are concerned, let’s assume as in Mayer et al. (2007) that they
depend on transaction costs (tcr ) and on production costs. The former capture thefacts that it is probably easily to manage production in a country which shares thesame official language and which is not too far. Regarding the latter, let’s assume
that the production function is Cobb Douglas with constant returns, that it uses
work (wr ) and other inputs (zr ) such as intermediate goods or land. Taking α as
the share allocated to work and Ar as total factor productivity, we obtain:

Ur ≡

1
1
lnMr − αln(wr ) − (1 − α)ln(zr ) − ln(tcr ) +ln(1 − Tr ) + ln(Ar ) (11)σ−1σ−1

We do not observe zr and Ar which will be captured by several proxies and arandom term, observed by the firm but not by the econometrician. In particular, inorder to take into account cross regional differences in human capital, we will assume
that the productivity of the region r is an increasing function of the proportion ofskilled workers. At this point of the analysis, we depart from the literature byassuming that even if the underlying model of location decisions decisions is likely
to be quite similar for manufacturing and service, there might be huge variation in
the importance of determinants according to the activity i considered. The reduced
form that we estimate is then given by:
π = β1i Demand+β2i Agglomeration+β3i Wage+β4i Skills+β5i Tax+β5i Distance+i104Econometric methodology

The reduced form that we want to test will first be estimated using the conditional
logit model. Next, we shall look at the possibility that choice structures are actuallyhierarchical, i.e. that investors begin by choosing a major region and then choose
a country inside the said region. This type of estimation is made possible by using
the nested logit model. These two types of approach need to be briefly described.

4.1

The Conditional Logit

The principle is to assume that firm’s location decisions are based on the maximization of a profit function subject to uncertainty. Econometricians are not in aposition to observe each country’s potential profitability; instead, they observe thelocation choices made by firms in countries with characteristics that can be observed.
Let R= (1,...,r,...N) all the potential locations. Each potential location offers to firmi a profit Πir given by:Πir = Vir + ir(12)

Vir = βXir

(13)

with
where Vir depends on the observable characteristics Xir of each location r, on avector β of coefficients to be estimated and on a set of unobservable characteristics
captured in the stochastic error term ir . Firm i chooses the location which provides
it with the highest profits. In other words, the probability of firm i choosing regionr is expressed as:
Pir = prob(πir > πik ) = prob(ik < ir + Vir − Vik ),(14)

McFadden (1974) shows that, if the error terms are independently and identically
distributed according to a type I extreme value distribution, the probability of firm
i choosing location r is expressed as:eβXir
Pir = PN βX
ir
r=1 e

(15)

This type of model is then estimated by means of the maximum likelihood
method. The problem with this type of model is that it rests on the assumption of
the independence of irrelevant alternatives (IIA): the probability of region r being
chosen in preference to region k has to depend solely on the characteristics of thesaid two locations and in no way on the characteristics of a third location. What
this means, in other words, is that if two regions appear to investors to be close substitutes, the error terms will be positively correlated and the parameters estimatedwill be distorted. Now it is highly likely that we will be faced with this problem if
investors have a hierarchical choice structure (Mayer and Mucchielli, 1999; Crozet etal., 2004). Disdier and Mayer (2004), in particular, find that there is an East-Westtype structure in French firms location choices in Europe. Given the descriptive
analysis presented before, it may be that this structure is also valid for all international investors. The nested logit method can be used to model this sort of choice11

structure and enables to limit the problem of IIA.

4.2

The Nested Logit

The nested logit model (Maddala, 1983; Train, 2003) consists of gathering together those countries that appear similar in the eyes of investors in a given group(or nest) so that they present the same degree of substitutability. Thus the IIA
holds within each of these groups but does not hold between these groups. We makethe assumption that alternative countries R are grouped in Z nests (or regions).Let
Z = (1, ..., z, ...L), all the possible nests, and R = (1, ..., r, ...Nz ), all the countries
belonging to each zone z. In this model, the upper decision structure (for instancethe choice between East and West) and the lower structure (choice of country within
the zone) are not independent. Clearly, the choice of a region depends on the characteristics of each region, but also on the attributes of the countries in the region.
The choice of a country also depends on the choice of region. A firm (which we taketo be representative so as to simplify the ratings) that chooses to locate in country
r belonging to region z obtains the following profit:Πzr = Vzr + zr(16)

Vzr = αYz + βXzr

(17)

with
Unlike the preceding model, certain observable characteristics Vzr depend on
the characteristics of both the countries and the regions (i.e.Xzr ), whereas certain
others vary solely between regions (ie Yz ). The same goes for the unobservablecharacteristics of the potential locations captured by the stochastic error term zr .
The probability of choosing country r can thus be expressed as the product of two
probabilities: the probability of choosing country r conditional on the choice of regionz, (Pr/z ), and the marginal probability of choosing region z, (Pz ). Whence:
Pzr = Pz × Pr/z

(18)

The probability of choosing region z depends on the characteristics of the said
region, but also on the characteristics of all the countries in the region:eαz (Yz +(1/βz ))V Iz
αm (Ym +(1/βm ))V Im
m=1 e

Pz = PL
with

V Iz = log(

NzXeβXzr )

(19)

(20)

r=1

Here V Iz is called the inclusive value. It corresponds to the anticipated utility
that the representative firm derives from setting up in a country belonging to regionz. In a second phase, the probability of choosing country r conditional on the choiceof region z is given by:
eβXzr
Pr/z = PNz βX
zr
r=1 e
12

(21)

Lastly, by substituting (20) and (22) into (19), the probability of choosing country
r belonging to region z:eαz (Yz +(1/βz ))V Iz
eβXzr
Prz = PNz βX × PL
αm (Ym +(1/βm ))V Im
zr
r=1 e
m=1 e

(22)

This last equation can be estimated using the maximum likelihood. The key
parameter in this equation is the coefficient, φ = αz /βz , of the inclusive value whichshould show the relevance of the proposed structure.

4.3

Relevance of the hierachical Structure

Two aspects are to be taken into account in order to asses the relevance of thehierachical structure, the likelihood ratio test and the inclusive value parameter.
The likelihood ratio test, which appears in nested logit estimations, indicates that
it is relevant to model firms location choices like a hierarchical structure. What is
involved is a test of heteroscedasticity against the null hypothesis of homoscedasticity(in which case the inclusive value parameters are equal to 1). It is therefore a
conditional logit test against a nested logit test.
The inclusive value coefficient is of special interest because (1 − φ) provides anindicator of the silimarity bewteen countries within a nest. The difficulty is that innested logit estimations, only βz appears. Hensher and Greene (2002) observe thatthe estimations require a certain normalisation and suggest making the numerator
equal to one, so that φ = 1/βz . The regression tables should then be interpreted
as follows. If βz = 1, then φ = 1, there is complete independence and the nested
logit model is no longer necessary, it is the conditional logit model that must be
used. If βz < 1, then φ > 1, the countries within the areas (nests) are less similar
than the regions or nests, suggesting that the proposed structure is inappropriate. If
βz > 1, then φ < 1 so that the regions within the nest are more similar than outside,suggesting that the proposed structure is appropriate.
The proposed choice structure is therefore relevant if and only if 1/βz is in therange between 0 and 1 and significantly different from 1.

5

Data

As pointed out before, the data on individual location choices come from theInvest in France Agency. Regarding the independent variables (see Table 2 for thestatistical sources), they were constructed on the basis of the information available
on host country characteristics for the period 2002-2006. In line with the theoreticalframework presented earlier, these variables are converted into logarithmic form tocarry out the estimations, the exception being the qualitative variables.
We use two types of measurement to gauge the size of national markets. The
first is GDP by country at constant prices (Eurostat). The second is the Harris-type
market potential (1954), adding to distance-weighted GDP by country an externalmarket potential (GDP of neighbouring countries weighted by the bilateral distanceto the country considered):13

M Pi =

GDPi X GDPj
+
Dii
Dij

(23)

This measurement has the advantage of considering that demand that can be
exploited by a firm stems both from local demand and from demand emanating fromthe surrounding locations. Countries with strong geographic centrality (in terms of
GDP distribution) are therefore favoured by this indicator. It should be noted that
we are not using the Krugman-type (1992) real market potential described previously
for two reasons. First, the trade flows needed to construct the said variable are notavailable for the whole of the period in question. Second, Head and Mayer (2004)
having compared the use of these different measurements, it transpires that using
real market potential gives forecasts of location behaviour that are slightly inferior
to those produced by Harris-type nominal market potential (1954).
Three variables were used to measure agglomeration effects. The first is designed
to take account of the overall size of the sector in the host country. Size is approximated by the total number of firms -domestic and foreign - in the sector and country
concerned in the year preceding the decision to invest (Eurostat). The second is designed to measure the overall extent of foreign presence in the country and sector
concerned. This factor is measured by the cumulative stock of foreign projects, as
registered by the IFA Observatory during the year preceding the investment. The
third variable, finally, is designed to measure the agglomeration effects between firms
in the same sector and from the same country of origin. The indicator used was constructed in the same way, on the basis of projects stemming from the same countryof origin and belonging to the same sector of activity.
The local labour market can be characterized by three criteria: the cost of labour,
its skill level and its availability. The first variable to be introduced is therefore thatof the “cost of labour”, measured by the average unit cost of labor by sector of
activity 12 . This measure has the advantage to account for potential differences in
productivity. The second variable, designed to measure skill levels, is the percentageof the population to have had a university-level education. Lastly, an “unemploymentrate” variable is introduced to take account of the degree of saturation of the labour
market. This variable is therefore expected in principle to have a positive coefficient.
There is, however, a certain ambiguity about it since high unemployment can also
signal labour market rigidity, which would not be attractive to foreign investors. So
a negative coefficient cannot be ruled out.
Two distance variables have been introduced to measure the volume of the transaction costs (attaching to distance, to adjusting to a new cultural and legal environment) resulting from setting up a subsidiary in a foreign country. The first, designed
to measure geographic proximity, relates to the spatial distance between the countries of origin and the countries hosting the projects. The second, which concerns
cultural proximity, is represented in the model by the existence of a shared official language. The latter ought to counterbalance the negative effect of geographic
distance. The data in question come from the CEPII.
Overall tax pressure on firms is represented by the rate of tax on company profits.
12

Since the IFA and NACE nomenclatures of activity are not exactly similar, a correspondence
table has been created so as to be able to calculate sectoral wages on the basis of Eurostat data
(this table is available upon request).

14

Table 2: Presentation of independent variables
Variable
GDP

MarketPotential
Wage

Skill
level
Tax

Unemployment

Distance

Sharedofficial
language
Numberof firms
Total FDI
in same sector
Total FDI
in same sector
and of same origin
Total FDI in
the same function

Definition

Availability

SourceGDP in euro million
at constant prices(1995 prices and exchange rates)
Harris-type market potential (1954)
in euro million at constant prices(1995 prices and exchange rates)
Average unit labour costat industry level (19 industries IFA)
(19 industries IFA)
Percentage of the population
with a university-level education
for persons aged between 25 and 34
Rate of tax
on corporate profits

2001-2006

EurostatNational Accounts

2001-2006

Rate of unemployment
as a percentageof the labour force
Distance between origin
and destination country,
based on bilateral distances between
the biggest towns in these countries
Dummy taking a value of 1
when the two countries sharethe same official language
Total number of firms(domestic and foreign)at industry level (19 industries IFA).
Sum of the number of projects
carried out in the same sector in
the year preceding the investment
Sum of the number of projects
originating from the same country
carried out in the same sector
in the year preceding the investment
Sum of the number of projects
carried out in the same functionin the year preceding the investment

2001-2006

Constructedusing Eurostat
and Distance-CEPII
EurostatIndustry andConstruction
IMD
World Competitiveness
Yearbook
IMD
Competitiveness
Yearbook
IMD
World Competitiveness
Yearbook
CEPII
Distance(distc epii)

15

2004

2004

2001-2006

2002-2005

2002-2005

2002-2005

2002-2005
2002-2005

CEPII
Distance(distc epii)
EurostatIndustry andConstruction
Constructedusing theIFA database
Constructedusing theIFA database
Constructedusing the I
IFA database

One would rather expect the value of this variable to be negative, but its effect isthought to be limited. For one thing, the rate of tax on company profits is only apartial and no doubt biased indicator of the level of tax pressure on companies; foranother, a high level of taxation may be counterbalanced by an abundant supply of
quality public goods, which will increase attractiveness. However, this latter factor
is not represented by an explicit variable in our formulation.
A number of explanatory variables have had to be reconstructed in order to conduct the function analysis. First of all, the average unit cost of labour by function
was calculated by constructing a table of correspondence between Eurostat data andthe functions shown in the IFA database 13 . Then, in order to measure functional
agglomeration effects, a specific variable was constructed using the IFA database bymeans of a process similar to that used for sectoral agglomeration variables. Thisvariable corresponds to the total stock of projects of foreign origin in the function
and the country concerned during the year preceding the investment. Lastly, analysis of co-location phenomena required a functional agglomeration variable which wasconstructed using the same method but was introduced separately for each function.
It should be noted that, not having the data with which to identify the parent company of the subsidiaries, these phenomena are considered at country level between
different firms whereas Defever (2006), for his part, analyses this aspect within firms.
In order, finally, to avoid problems to do with variables that are omitted, moststudies analysing location choices at the infranational level introduce country fixed
effects. To the extent that several of our explanatory variables defined at country
level are constant over time, we introduce fixed effects at a higher geographical level.
Given the stylized facts described earlier, they are East-West fixed effects.

6

Empirical Results

We begin by presenting the overall results, with all activities combined, so as
to make them comparable with earlier studies. They are on the whole very consistent with those contained in the existing literature. More detailed analyses arethen presented by sector and by function, and they make it possible to identify numerous specificities in the location criteria, particularly where service activities areconcerned.

6.1

Location determinants of multinational firms in Europe

The results14 for the projects as a whole (see Table 3), all activities combined, arevery consistent with those found in the existing empirical literature. In columns (1)and (2) of table 3, we compare the use of two indicators for market size. In column
13This table is available upon request.
The number of observations corresponds to the number of projects multiplied by the numberof possible choices (apart from the missing values). Six countries are not in the sample for want ofdata (Iceland, Latvia, Lithuania, Malta, Switzerland and Romania). Initially, firms outside Europe
had a choice between 29 countries, compared to only 28 for European firms. This is not a problem
as if the IIA holds up, the choice between two countries has not to be affected by the choice of athird.
14

16

(3),(4),(5) and (6), we have added separately different measures of agglomeration
forces. In column 7, we have introduced East-West fixed effects.
Table 3: Conditional Logit: Overall Results

Wage
Unemployment
Skill
Tax
Distance
Same language
PIB(1)-1.19***(0.03)-0.15***(0.04)
0.46***(0.04)
0.12*(0.06)-0.07***(0.02)
1.15***(0.03)
0.74***(0.01)

Market Potential

Dependent Variable: Choice of Location
(2)
(3)
(4)
(5)
(6)
-0.22*** -0.40*** -0.19*** -0.20*** -0.26***(0.03)(0.03)(0.03)(0.03)(0.03)
0.70*** 0.35*** 0.55*** 0.41*** 0.34***(0.03)(0.03)(0.03)(0.03)(0.03)
0.18*** 0.55***
0.05
-0.04
0.16***(0.04)(0.04)(0.04)(0.04)(0.04)
0.05-0.35***-0.11*
0.04-0.30***(0.06)(0.07)(0.06)(0.06)(0.07)
0.07***-0.00-0.09***
0.01-0.10***(0.02)(0.02)(0.02)(0.02)(0.02)
1.03*** 1.01*** 0.50*** 0.83*** 0.60***(0.03)(0.03)(0.03)(0.03)(0.03)

1.49***(0.04)

Number firms (sect)

1.09***(0.05)
0.48***(0.01)FDI (origin)

1.03***(0.05)

0.67***(0.05)

0.73***(0.01)

0.68***(0.05)
0.26***(0.01)
0.48***(0.02)
0.28***(0.02)

2129830.122070610.14

0.85***(0.02)FDI (sect)
Dummy East
Investment x countries
Pseudo R2

2129830.11

2129830.07

2070610.11

2129830.12Standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%

In the first place, location decisions are very sensitive to market size. In column(2), a 10% increase in market potential corresponds to a 7% rise in the probabilityof attracting new investors. This result can be observed whatever the indicator
selected. It is apparent, however, that investors are twice as sensitive to changing
market size in the specification incorporating ”market potential” as they are in thespecification based solely on domestic GDP. This result, which is consistent withthe results by Head and Mayer (2004), confirms the notion that in the integrated
European area, the perception of a ”market area” accessible from a given location
extends well beyond the borders of the host country alone.
Secondly, the agglomeration phenomena appear to be broadly confirmed by our
results. The three variables introduced in our model have a positive and significant
sign. Location decisions appear especially sensitive to the presence of firms fromthe same country, which have invested in the same sector. This can probably be
17(7)-0.36***(0.04)
0.35***(0.03)
0.13***(0.04)-0.33***(0.07)-0.10***(0.02)
0.59***(0.04)

0.71***(0.05)
0.25***(0.01)
0.49***(0.02)
0.27***(0.02)-0.20***(0.06)
2070610.14

explained by a better information about the area. It has already been referred to ina number of earlier studies (Crozet et al., 2004).
Our study also points to the positive impact of geographic and cultural proximity,
which is as expected. As initially thought, having the same language as the investor
acts as a powerful attraction. Similarly, geographic proximity seems to encourage
investment - a finding consistent with that described in earlier studies (Disdier andMayer, 2004; Mayer et al., 2007).
Alongside the factors that impact positively on location decisions, we have as
expected identified a negative impact exerted by the rate of tax, which is consistent
with the findings of earlier studies (Devereux and Griffith, 1998; Bénassy-Quéré etal., 2003). Our findings also show that wage costs have a significantly negative
impact on location decisions - even at an aggregated level. It should be noted,
though, that wage costs have less effect in the specification incorporating market
potential than in that incorporating GDP. This comes back to the idea that thepotential profits expected by firms located in a central region offset the inevitably
higher wage costs. Lastly, the introduction of East-West fixed effects does not alter
the magnitude of the coefficients, although the dummy variable for the East appears
with a negative and significant effect. This suggests that there are other factors
making the East less attractive than the West in investors’ eyes. These could include
a difference in the perceived quality of the respective institutions - a result found by
Disdier and Mayer (2004) with respect to French firms setting up in Europe between
1980 and 1999.
In the continuation of the analysis we will take specification 7 which corresponds
to our theoretical reduced form.

6.2

Location determinants in the service and the manufacturing sectors

In columns (1) to (4) of table 4, we carried out estimations separately for the
manufacturing and service sectors. In colums (5) to (9), we estimated separate
regressions for different subsectors of Services in order to account for potential heterogeneity between each of them. Overall, the coefficients have the expected sign.
Globally, the model of location decisions in the manufacturing sector explains welllocation determinants in services. However, a certain number of specificities appear
in the location criteria of the service sector.
In the first place, location choices in the services sector appear very sensitive toskilled labor resources, whereas the coefficients are close to zero where manufacturing
is concerned. This finding is consistent with the facts that services are especiallyskilled intensive activities except for telecom operator 15 .This result is particularlyinteresting in the context of the present increase in FDI in services, as it suggeststhat skilled workers remain relatively sheltered from international competition.
Second, market size is an important criterion in both manufacturing and service
15

However, this could be also the case for some manufacturing activities. Several studies showthat the availability of skill labor affects positively location decision of the manufacturing sector (seefor instance Toubal, 2004; Becker et al., 2005). This is certainly because, inside the manufacturingsector, some activities (headquarters, R&D are also skilled intensives. The analysis by function
might be more appropriate as regards this point.

18

sectors. Besides, market size is very important for all services subsectors except for
telecom operators and internet, a sector that not necessarily requires an immediateproximity between the supplier and the final consumer. These results are important as they suggest that country with a high market potential remain attractive
attractive for FDI in services.
Third, proximity (cultural and physical) affects positively the location of FDI in
the manufacturing sector. However, the location of FDI in services is sensitive only
to the fact to share a common language. As pointed out by Navaretti and Venables(2004), trade costs are very high in some service sectors so that it is more viable toserve foreign markets via FDI than via export. This could explain the non significant
sign of distance in the service sector.
Table 4: Conditional Logit by Sector

MarketPotential
Wage
Unemp.
Skill
Tax
Dist.
Same
language
FDI(sect)
FDI(origin)
Number
firms
Dummy
East
Inv. x Ctry
Pseudo R2

Total Manuf(1)(2)
0.65***
0.64***(0.07)(0.07)-0.24***-0.23***(0.04)(0.06)
0.37***
0.37***(0.05)(0.05)-0.02-0.01(0.05)(0.05)-0.43*** -0.42***(0.09)(0.09)-0.13***-0.13***(0.03)(0.03)
0.54***
0.54***(0.05)(0.05)
0.33***
0.33***(0.03)(0.03)
0.58***
0.58***(0.03)(0.03)
0.22***
0.22***(0.02)(0.02)
0.03(0.09)
1099041099040.12
0.12

Dependent Variable: Location Choice
Total Service
Busin. Serv Comp. Soft
(3)
(4)
(5)
(6)
0.81***
0.92***
1.17***
1.19***(0.07)(0.08)(0.16)(0.13)-0.12**-0.44***-0.67***-0.67***(0.05)(0.07)(0.14)(0.15)
0.38***
0.45***
0.40***
0.59***(0.05)(0.05)(0.12)(0.09)
0.46***
0.43***
0.92***
0.63***(0.07)(0.07)(0.16)(0.11)-0.30***-0.43***-0.88***-0.52***(0.10)(0.10)(0.24)(0.18)
-0.01
-0.02
0.00
-0.01
(0.03)(0.03)(0.08)(0.07)
0.73***
0.71***
0.91***
0.67***(0.05)(0.05)(0.11)(0.08)
0.11***
0.08***-0.20***-0.06(0.03)(0.03)(0.08)(0.06)
0.40***
0.41***
0.53***
0.36***(0.03)(0.03)(0.07)(0.05)
0.35***
0.35***
0.53***
0.44***(0.02)(0.02)(0.05)(0.04)-0.65***-1.15***-1.38***(0.11)(0.23)(0.21)
97157
97157
19999
39330
0.18
0.18
0.19
0.23

Transport(7)
1.27***(0.21)-0.52***(0.19)
0.73***(0.15)
0.10(0.15)-0.30(0.28)-0.08(0.08)
0.43***(0.14)-0.06(0.11)
0.50***(0.08)
0.29***(0.05)-0.52**(0.26)
14002
0.11

Telecom(8)
0.23(0.39)
0.42(0.46)
0.96***(0.33)-0.50*(0.30)-0.49(0.49)-0.04(0.16)
0.98***(0.28)
0.35*(0.21)
0.09(0.25)
0.31***(0.10)-0.21(0.58)
3055
0.11Standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%

Last, the dummy variable “East” introduced in the regressions take a very significantly negative value for most of the services sector. This fully corroborates thestylized fact alluded to above, according to which the bulk of international investment projects in the service sector were located in Western Europe during the period
in question.
This initial segmentation suggests that investors in the services sectors locate
preferably in countries with high market potential and with a high availability ofskilled resources. However, approaches by major sector only partially account for location decisions because, apart from the existence of sectoral specificities within the
categories themselves, the boundary between the manufacturing and service sectors
is no longer as relevant as beforehand (Trefler 2005). This is because firms have not
only to manufacture the product or provide the service they market, but they havealso to put in place a whole raft of support functions upstream and downstream:
19

Finance(9)0.56***(0.16)-0.26*(0.14)-0.10(0.13)
0.73***(0.16)-0.36(0.22)
0.01(0.07)
0.93***(0.11)
0.15**(0.07)
0.39***(0.06)
0.52***(0.07)
0.24(0.22)
20771
0.16

research and development, head offices, commercial and representative offices, logistics and distribution. The location of each of these types of function can be basedon specific arguments that are more or less independent of the sector to which theinvestor belongs, whence the symmetrical occurrence at territorial level of functional
specialization (a phenomenon described by Duranton and Puga, 2005).

6.3

Analysis of location determinants by function

There have also been some empirical studies on specific location criteria for thevarious functions of manufacturing firms. Our study, which focuses on a more overall
set of functions, all sectors combined, also arrives at new and interesting results inthis connection (see Table 5). A number of results observed at the overall levelremain valid for each of the functions concerned: sensitiveness to the market size toagglomeration effects, effects of geographic 16 and cultural proximity, negative impactof tax rates. Also, however, there are strong functional specificities, in particularconcerning the following points.
In the first place, the location of production is sensitive to wage costs and this isalso the function with the least sensitiveness to Market Potential. This result is inline with that found by Defever (2006), who demonstrates a substantial difference
between the location of production units (where wages and agglomeration phenomena are very significant) and services surrounding production (sensitive above all tomarket size and to functional agglomeration effects).
The market size criterion seems to be very significant for tertiary support functions and for production center, though the magnitude is weaker for the latter. Butinterestingly, market size is not a significant factor for the “call center and on-line
services” a result which is perfectly consistent with the fact that it is an activity
likely to be performed at some distance from the final consumer.
Regarding the effect of the skill level on location decisions, the results are encouraging. The results confirm previous findings at the sectoral level. They indicatethat the location of services activities, especially high skilled intensive ones such as
headquarters, commercial offices and service provision, is sensitive to the availabilityof skilled labor. However, this is not the case for low skilled intensive activities suchas production units or call-centers and on-line services. One should note that thecoefficient of the skill variable is not significant for R&D. This suggests that several
robustness checks using other measures of skilled workers should be carried out.
Where head offices are concerned, our estimations point to fairly high sensitivity
to skill levels, to market proximity, to shared language effects and to both sectoraland functional agglomeration effects. This latter result is especially consistent withthose obtained by Vives and Strauss-Kahn (2005). The significativeness of the market size criterion is less so, but Defever (2006) finds similar results. This is likely
due to the fact that central locations are more strategic when it comes to manageinternational production. Besides, this is likely to be influenced by the role playedby the sharing of a common language in the location of headquarters in our sample.
16

For R&D and headquarters the coefficient of the distance is positive. This is likely due tothe fact that in our sample, there are non-European and European investors. We separated oursample into non-European and European firms and the impact of distance becomes negative forthe former.

20

Table 5: Conditional Logit by Function

MarketPotential
Unemp.
Wage
Skill level
Tax
DistanceSamelanguage
FDI (sect)Co-location
Headquarter
R&D
Production
Distribution
Commercial OfficeService Provision
Call center andon-line Services
Dummy East
Observations
Pseudo R2

Headquarters(1)
1.37***(0.22)
0.18(0.17)-0.08(0.21)
0.83***(0.24)-0.38(0.30)
0.40***(0.13)
1.07***(0.12)
0.70***(0.10)

Dependent Variable: Choice of location
R&D
Production
DistriCommercial
butionoffice
(2)
(3)
(4)
(5)
0.53**
0.28**
0.89***
0.90***(0.23)(0.13)(0.20)(0.09)
0.44***
0.36***
0.45***
0.26***(0.17)(0.06)(0.13)(0.07)
0.05-0.25***-0.41**-0.45***(0.18)(0.07)(0.17)(0.07)
0.22-0.32***
0.26
0.32***(0.22)(0.09)(0.17)(0.08)-0.22-0.59***-0.05
0.31**(0.30)(0.12)(0.25)(0.14)
0.27**-0.27***-0.03
0.14***(0.12)(0.04)(0.07)(0.04)
0.74***
0.69***
0.68***
0.88***(0.14)(0.08)(0.12)(0.05)
0.30***
0.68***
0.14
0.30***(0.08)(0.05)(0.09)(0.04)

Service
Provision(6)
1.03***(0.20)
0.36***(0.13)
0.06(0.14)
0.34*(0.17)-0.59**(0.25)
0.02(0.07)
0.96***(0.10)
0.19**(0.09)

Call-center
on-line service(7)
0.33(0.45)
0.42(0.26)
0.01(0.27)
0.24(0.40)-1.25***(0.47)-0.06(0.14)
0.65***(0.23)-0.04(0.16)

0.19(0.16)
0.11(0.15)
0.16(0.15)-0.14(0.12)
0.04(0.15)-0.25*(0.14)-0.10(0.11)-0.37(0.31)
15320
0.24

-0.08
(0.16)
0.78***(0.17)
0.63***(0.15)-0.46***(0.13)
0.10(0.15)-0.41***(0.15)-0.13(0.12)-0.62**(0.28)
12022
0.13-0.50***(0.12)
0.04(0.13)
0.46***(0.12)-0.07(0.10)
0.41***(0.12)
0.33***(0.12)-0.11(0.10)-0.19(0.21)
20190
0.15

-0.10
(0.27)
0.10(0.28)
1.03***(0.25)-0.33(0.20)
0.34(0.23)
0.08(0.23)-0.03(0.20)-0.63(0.45)
4488
0.15

-0.05
(0.07)
0.07(0.08)
0.46***(0.06)-0.10*(0.05)
0.31***(0.06)-0.36***(0.07)-0.01(0.05)
0.16(0.11)
59838
0.14

-0.07
(0.13)-0.05(0.12)
0.54***(0.12)
0.15(0.10)
0.09(0.12)-0.06(0.12)-0.02(0.10)-0.67***(0.26)
17342
0.11-0.45***(0.06)
0.22***(0.06)
0.17***(0.06)-0.12**(0.05)
0.57***(0.06)-0.08(0.06)
0.12***(0.05)-0.85***(0.12)
83481
0.17Standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%

Indeed, over the period 2002-2006, the United Kingdom attracted the most head
offices over the period (27% of the total, compared to 10% for France which wasin second position). And of the head offices which set up in the United Kingdom(location with high centrality), 66% were of US origin.
Regarding agglomerations forces, sectoral and functional agglomeration effects
are found to coexist where most functions are concerned. This result corroborates
the work done by Duranton and Puga (2005), confirming that there is indeed afunctional specialization trend at territorial level.
Lastly, among the factors that determine project location, co-location phenomena

21

can play a significant role. With these effects being frequently observed in practice, it
seemed worthwhile confirming their existence by means of an econometric approach.
Because of the nature of the data available, it was not possible to test the existenceof such phenomena at firm level; it was possible, though, to do so at country level,
the feeling being that if this type of phenomenon was important at firm level, it
ought to be observable at the aggregated level. The results confirm the existence ofstrong complementarities between activities. In particular, the results show that thelocation of R&D centers is highly sensitive to the prior existence of production sites.
This result confirms that obtained by Defever (2006) with respect to firms anddoubtless explains the increase in the number of R&D centers located in Eastern
Europe during the period 2002-2006. Globally, the production function is a strong
explanatory factor for the co-location of other functions, with the exception of head
offices and commercial offices. The existence of such complementarities suggests thatcountries that experienced a decline of a part their manufacturing base are likely toexperience also a decline of a part of their service base.

6.4

An Est-West Structure in location decisions ?

As was mentioned above, if countries appear to investors to be similar, this is
likely to distort the econometric results obtained from the conditional logit model.
This is likely what is happening in Europe where there exist potential market differences between Western and Eastern Countries. It may therefore be assumed, thatthe countries belonging to each of these groups are highly likely to be close substitutes. This being the case, using a hierarchical choice structure of the ”nested logit”
type would seem justified. However, one difficulty raised by this type of model is to
identify an appropriate choice structure. Disdier and Mayer (2004) find that there
is a significant East-West type structure in French firms’ location choices in Europe.
Mayer and Mucchielli (1999) show that there is, in Japanese firms’ location choices
in Europe, a country-region type structure which is more relevant in their study
than a center-periphery type structure.
Before selecting an East-West type structure, we therefore carried out a numberof prior tests by estimating several potential choice structures17 . The relevance ofthe proposed choice structure may be verified in two ways (see Table 618 ), using the
likelihood ratio test and the inclusive value coefficient.
Applying this model demonstrates the existence of a hierarchical choice structure
which is relevant for the manufacturing sector and for the location of production 19 .
For the majority of investors, therefore, the two regions have sufficiently separate
specialization profiles within which the countries are sufficiently close substitutes forone of the two regions to be favored in the quest for the location country. It can beseen that the coefficients obtained as a result of estimations with the nested logit
model are not very different from those obtained with the conditional logit model;
only their interpretation changes. Where the production function is concerned, for
17

Results are available upon request.
In nested logit estimations, pseudo R2 is not given directly. To calculate it, we used thefollowing formula: pseudo R2 = 1 − (L1/L2) where L1 is the ”likelihood function” and L2 thelikelihood function for the model with only a constant .
19
We also tested the presence of an East-West structure for each of others functions but thisstructure was not relevant.
18

22

Table 6: Nested Logit: East-West Structure
Dependent Variable: Choice of Location
Manufacturing Sector Service Sector Production
(1)
(2)
(3)
Market Potential
0.65***
0.91***
0.19*(0.07)(0.08)(0.11)
Unemployment
0.36***
0.44***
0.49***(0.04)(0.05)(0.05)
Skill level-0.03
0.42***-0.41***(0.05)(0.07)(0.08)
Tax-0.47***-0.35***-0.40***(0.09)(0.11)(0.10)
Distance-0.13***-0.01-0.25***(0.03)(0.04)(0.03)
Same language
0.51***
0.73***
0.61***(0.05)(0.05)(0.07)FDI (sector)
0.33***
0.11***
0.57***(0.03)(0.03)(0.04)FDI (origin)
0.56***
0.44***(0.03)(0.03)
Number firms
0.22***
0.32***(0.02)(0.02)
Wage (sector)-0.20***-0.45***(0.06)(0.07)
Wage (function)-0.14**(0.06)FDI (function)
0.34***(0.04)
Inclusive value Parameter (East-West)βz
1.24****
0.64***
1.72***
φz = 1/βz
0.8***
1.5***
0.58***
Investments x countries
10990497157
59838
Likelihood ratio test
8.98***
12.98***
26***
2
Pseudo R
0.12
0.18
0.14
Standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%

example, the wage is negative and significant, which means that investors are sensitive to wage costs in their choice of country within each of the two regions. The
magnitude of the coefficient is nevertheless not as great as in the estimations withthe conditional logit model, and this is also the case for the coefficient of market
size. This suggests that location choices between Eastern and Western Europe areinfluenced by a trade-off between wage costs and market potential.
Table 7 shows clearly, on the other hand, that there is no East-West type structure in the location of service activities (the inclusive value coefficient φ not being
between 0 and 1, the proposed choice structure is inappropriate). This can probably
be explained by the fact that very few service sector-related projects were located23

in the East during the period in question, so that investors are sensitive to national
specificities in the location of services.

7

Conclusion

In this paper we analyzed the location determinants of multinational firms in
Europe. The recent internationalization of FDI in services is a topic of anxiety thathas not until now been the focus of much attention in the literature on location
determinants. We first carried out a descriptive analysis which revealed significative
differences in the location of manufacturing and services in Europe. We then try
to explain these patterns by investigating wether there were differences in location
criteria between service and manufacturing activities. We began by using a sectoral
approach to compare the manufacturing sector with the service sector in which therole played by market access, skilled labor resources and cultural proximity is moredecisive. A functional analysis then enabled us to identify determinants specific toeach function.
The results indicate that in Europe, Western countries tend to specialize inservices while Eastern countries are more attractive for manufacturing activities.
The results show that multinational firms locate FDI in services in high-income
and skilled abundant countries, which suggests that educated workers in advanced
economies are relatively sheltered from international competition. However, this evidence does not hold for all service activities, especially for low skilled and easily
offhorable ones. Besides, the existence of complementarities between the location of
production units and surrounding services activities suggest that this trend is likely
to change.

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8

Appendix27

Table 7: Functional Classification of the Invest in France Agency
Function

Production
Commercial or
liaison office
R&D centre
Call centres andon-line services
Distribution, logistics,
packagingService provisionInternal administrative
service or Headquarter
Total, tertiary
support functions
Total

All
projects
4935
4680% Totalof allprojects
35,50%
33,70%% of projects
going toEastern Europe
47,90%
15,20%% of projects
going toWestern Europe
52,10%
84,80%

737
301

5,30%
2,20%

18,00%
25,90%

82,00%
74,10%

1059

7,60%

25,80%

74,20%

1254
937

9,00%
6,70%

20,10%
7,20%

79,90%
92,80%

8968

64,50%

16,90%

83,10%

13903

100,00%27,90%

72,10%

28

Table 8: Sectoral Classification of the Invest in France Agency
SectorAgro-food, agriculture
and fisheries
Furniture andhome equipment
Biotechnologies
Chemicals,
Plastics technology
Electronic components
Motor vehicule andcomponent manufacturers
Consumer electronics
Energy, otherconcessionary services
Electrical, electronic,
computer, medical equipment
Machinery, mechanical equipment
Aeronautical, naval
and railway equipment
Drugs, cosmetics
Metals, metal working, recycling
Textiles, clothing
recycling
Glass, ceramics, minerals,
wood, paper, publishing
Total, Manufacturing
Other Commercial
or financial services
Business Services
Computer softwareand services
Telecom operator
Internet
Transport, storage,publicbuildings and works
Total, Services
Total

All
projects
666% Totalof allprojects
4,80%% of projects
going toEastern Europe
34,40%% of projects
going toWestern Europe
65,60%

286

2,10%

52,80%

122
749

0,90%
5,40%

47,20%
4
9,00%
28,30%

238
1377

1,70%
9,90%

31,90%
51,10%

68,10%
48,90%

309
418

2,20%
3,00%

49,20%
25,40%

50,80%
74,60%

1368

9,80%

23,80%

634
190

4,60%
1,40%

36,30%
26,30%

76,20%
7
63,70%
73,70%

550
398
242

4,00%
2,90%
1,70%

17,10%
48,20%
51,20%

82,90%
51,80%
48,80%

761

5,50%

44,30%

55,70%

8308
1159

59,80%
8,30%

35,80%
19,80%

64,20%
80,20%

1204
2192

8,70%
15,80%

13,80%
10,40%

86,20%
89,60%

184

1,30%

22,80%

77,20%

856

6,20%

27,90%

72,10%

5595
13903

40,20%
100,00%16,20%
27,90%

83,80%
72,10%

29

91,00%
71,70%