The location of technological activities of MNCs in

European regions:

The role of spillovers and local competencies

John Cantwella, Lucia Piscitelloa,b,*

aUniversity of Reading, Reading, MA, USAbDipartimento di Economia e Produzione, Politecnico di Milano, Piazza Leonardo da Vinci 32,

Milan 20133, Italy

Abstract

This paper examines the relative attractiveness of the Italian, German and UK regions for the siting

of foreign-owned corporate technological development, controlling for the geographical distribution of

the equivalent innovative efforts of established indigenous firms in each country. The data used are

patents granted in the US to the world’s largest firms from 1969 to 1995, identifying the ultimate

nationality of ownership and the location of the research facility responsible for each patent. Through

econometric models based on count data techniques, it is shown that the relative attraction of a location

for foreign-owned research depends positively upon the local market size, the local scientific and

educational infrastructure and the potential for intra- and interindustry spillovers. D 2002 Elsevier

Science Inc. All rights reserved.

Keywords: Location; European regions; MNCs; Spillovers

1. Introduction

The existing knowledge base of a region plays an important role in the decisions of the

largest foreign-owned firms as to where to locate their technological activities (Cantwell and

1075-4253/02/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved.

PII: S1075 -4253 (01 )00056 -4

* Corresponding author. Dipartimento di Economia e Produzione, Politecnico di Milano, Piazza Leonardo da

Vinci 32, Milano 20133, Italy. Tel.: +39-2-2399-2740; fax: +39-2-2399-2710.

E-mail address: lucia.piscitello@polimi.it (L. Piscitello).

Journal of International Management

8 (2002) 69–96

Iammarino, 1998, 2000; Cantwell and Noonan, 2001) as well as other location-specific factors

mainly related to the market. Thus, in each country, the local technological efforts of foreign-

owned firms tend to be strongly agglomerated at a subnational and regional level. The present

paper analyses this agglomeration effect at the regional level (Braunerhjelm and Svensson,

1998; Barrel and Pain, 1999), having controlled for the cross-regional and cross-sectoral

distribution of technological activities in indigenous firms. Specifically, the purpose of this

paper is twofold: (1) to analyse the regional distribution of technological activities carried out

by large multinational corporations (MNCs) in Germany, the UK and Italy over the last 30

years and (2) to explain the locational preferences of foreign-owned firms across the regions in

each of the three countries considered.

The empirical investigation uses patents granted in the US to the world’s largest industrial

firms for inventions achieved in their European-located operations, classified by the host

European region in which the research facility responsible is located. We examine the

regional distribution of corporate research activity in Italy, Germany and the UK, distinguish-

ing between domestically owned and foreign-owned firms in each of these three countries.

The spatial patterns of activity in foreign-owned firms are considered while controlling for

indigenous firms’ patterns. Departures from a linear proportionality between the locational

distributions of these two sets of firms constitute differences in their locational behaviour.

These differences in the cross-regional distribution of technological development between

locally-owned and foreign-owned firms1 could be explained through variables related to the

local market size, to the knowledge base and to the potential for intra- and interindustry

spillovers. We discuss some explanations for these differences and propose an econometric

model based on count data techniques in order to explain these locational preferences of

MNCs across regions within each of the three European countries considered.

The paper is organised in the following way. Section 2 sets out the conceptual framework

for the analysis of the determinants of locational choice in the technological activities of

MNCs. Section 3 investigates the extent and evolution of the internationalisation of

technological activity in the German, UK and Italian regions in the period 1969–1995.

Section 4 reports the econometric model, the variables and the results obtained. Finally,

Section 5 presents some summarising and concluding remarks, draws out one of the policy

implications of our argument and indicates an agenda for future research.

2. The location of technological activities: a theoretical framework

At a general level, a firm’s operations may be dispersed across different types of

productive activity (the diversification of technologies or products) or over geographical

space (the internationalisation of the same). However, spreading the product markets in which

the firm is involved may be a matter of exploiting more effectively established competencies

while moving into new areas of technological development means creating new competence.

1 For a similar result in the UK, see also Cantwell and Iammarino (2000).

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–9670

In order to not only exploit effectively but also to consolidate an existing capability, it is

generally necessary for a firm to extend that capability into new related fields of production

and technology and across a variety of geographical sites (Cantwell, 1995). The corporate

internationalisation and diversification of technological activity are indeed both ways of

spreading the competence base of the firm and of acquiring new technological assets or

sources of competitive advantage.2

Attention has been increasingly focused on the emergence of the trend for MNCs to

establish internal and external networks for innovation (Cantwell, 1995; Cantwell and

Iammarino, 2000; Kuemmerle, 1999; Zander, 1999), which are characterised by different

levels of territorial and social embeddedness with reference to both the motivations for

overseas R&D and the location that hosts them. Indeed, the localisation of research activities

is partially determined by the specific function, which this activity fulfills within a given firm

(Carrincazeaux et al., 2001). The internationalisation of R&D may be motivated by several

considerations and specifically (Kumar, 2001) to support foreign production by adaptation to

host country markets [home-base exploiting R&D (HBE) in the words of Kuemmerle, 1999]

and to tap into the capabilities available in host countries, thus benefiting from the localised

knowledge spillovers [home-base augmenting (HBA)]. Despite these suggestions of intrafirm

differences in the influences on the internationalisation of R&D, the bulk of analysis of

overseas R&D has been carried at the level of the corporate group as a whole rather than the

individual subsidiary or laboratory or at the level of collective groups of firms, and it has

focused on the determinants of intensity of overseas R&D in an interindustry, interfirm or

intercountry context (see Zejan, 1990; Kumar, 1996, 2001; Niosi, 1999 for a number of recent

studies). Specifically, firms’ locational choices and their location-specific determinants have

been mainly analysed at the country level (Hakanson, 1992; Fors, 1996; Kumar, 1996;

Odagiri and Yasuda, 1996), and only a minority of studies has recently started to investigate

their regional or subnational dimension (e.g. Cantwell and Iammarino, 1998, 2000; Carrin-

cazeaux et al., 2001). In fact, although some authors have recently suggested that regions are

increasingly becoming important milieu for the competitive-enhancing activities of mobile

investors (Porter, 1996; Scott, 1998; Dunning, 2000), thus replacing the nation state as the

principal spatial economic entity (Ohmae, 1995), there is still only quite a scant existing

empirical research on multinational location at this subnational level.

The development of cross-border corporate integration and intraborder intercompany

sectoral integration, as new forms of global governance, makes it increasingly important to

examine where and how innovative activity by MNCs is internationally dispersed and

regionally concentrated. To the multinational firm, the innovativeness of the corporate group

as a whole depends upon the extent of the locational diversity that it can manage to combine

and sustain in its technological efforts and the degree to which it can choose to site activity so

as to reduce overlapping duplication but enhance technological complementarity between the

2 The background to this study is the relationship between the diversification and internationalisation of the

technological competence of large MNCs, which have been explored extensively in our earlier work (Cantwell

and Janne, 1999; Cantwell and Piscitello, 1999, 2000).

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–96 71

locations selected. Therefore, the locational choice of the MNC depends upon (1) the strategy

followed by the MNC (the extent to which it has developed a network with home-base

augmenting facilities, as opposed to the more traditional home-base exploiting) and (2) the

location-specific characteristics of alternative contexts in which research may be located. The

present paper specifically focuses on (2). In particular, we claim that the relevant location-

specific characteristics are the following.

2.1. Agglomeration and industry-specific spillovers

The firms of each country tend to embark on a path of technological accumulation that

has certain unique characteristics and sustains a distinct profile of national technological

specialisation (Rosenberg, 1976; Pavitt, 1987; Cantwell, 2000). The kinds of linkages that

grow up between competitors, suppliers and customers in any regional district or country are

also, to some extent, peculiar to that location and imbue the technology creation of its firms

with distinctive features (Mariotti and Piscitello, 2001). For these reasons, other MNCs often

need to be on-site with their own production and their innovatory capacity if they are to

properly benefit from the latest advances in geographically localised technological deve-

lopment to feed their innovation (Cantwell, 1989; Kogut and Chang, 1991). Moreover, due

to the complexity of technological learning and the significance of maintaining face-to-face

contacts, the localisation of technological contacts tends to occur at a regional level within

host countries (Jaffe et al, 1993; Almeida, 1997; Cantwell and Iammarino, 1998; Verspagen

and Schoenmakers, 2000). It is therefore typically when there is already a strong existing

domestic technological presence that the R&D of foreign-owned affiliates is most likely

to become substantial and to gain a creative role with respect to the global technological

development strategy of the MNC as a whole. However, where indigenous technological

development is highly concentrated in just one or two major local firms, any industry-

specific agglomeration effect may be offset by a competitive deterrence effect, both in terms

of bidding for local resources and in terms of the availability of potential local technologi-

cal spillovers.

2.2. External sources of knowledge and science–technology spillovers

Firms’ efforts to advance technology do not generally proceed in isolation but are strongly

supported by various external sources of knowledge: public research centres, universities,

industry associations, an adequate educational system and science base and other firms (Kline

and Rosenberg, 1986; Nelson, 1993; Nelson and Rosenberg, 1999; Rosenberg and Nelson,

1996; Breschi, 2000). There is growing evidence, so far mainly from the US, that these

science–technology or university–industry linkages tend to be geographically localised (Jaffe

et al, 1993; Audretsch and Feldman, 1996; Audretsch and Stephan, 1996; Acs et al., 2000;

Adams, 2001). This is especially likely to be true of foreign-owned firms in an economy,

which tend to have a greater degree of locational mobility when siting their corporate research,

and so pay greater attention to being close to relevant public research facilities (see Gorg and

Strobl, 2001 on the greater international locational mobility of MNCs). Thus, in an earlier

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–9672

study, it was shown that foreign-owned firms in the UK are relatively more drawn (than are

UK-owned firms) to locate their research in regions such as Scotland and East Anglia, in which

the public research base and higher education infrastructure is also relatively good (Cantwell

and Iammarino, 2000). Therefore, we expect that compared to indigenous firms that have a

certain locational inertia around their major centre of technological activity, which is already

situated in their home base, foreign-owned research facilities are relatively more attracted

by external sources of knowledge and science–technology spillovers.

2.3. Localised intercompany spillovers

As knowledge is mainly tacit, geographical distance increases the difficulty in both

transmitting and absorbing it. In other words, tacit knowledge travels easily over small

distances but far less easily over longer distances (Caniels, 2000). This leads to the hypothesis

that the intensity of spillovers increases with geographical proximity (Verspagen and

Schoenmakers, 2000). Specifically, we distinguish three different types of interfirm spillovers

as follows.

(1) Cluster-based spillovers, associated with the presence of a wide-ranging collection of

technologically active firms within a given industry or sector, all concentrated in the same

geographical area (Baptista and Swann, 1998, 1999). The geographical concentration of firms

engaged in similar activities or within a common industry leads to further local clustering of

related firms and the local accumulation of relevant knowledge (Braunerhjelm et al., 2000).

(2) General purpose spillovers, entailing interindustry spillovers (Lipsey et al., 1998)

associated with the existence of firms working in several different fields of research but with a

common overlapping interest in certain general purpose technologies (GPTs), which are

relevant in most industries such as machinery or computing. Such spillovers are more likely to

occur in an all-round ‘‘higher order’’ centre of excellence, which facilitates a more favourable

interaction with indigenous firms, and greater opportunities for intercompany alliances for the

purposes of technological collaboration and exchange (Cantwell and Mudambi, 2000). Within

a host country, an all-round regional centre of excellence is likely to attract the research-based

investments of a wide variety of foreign-owned MNCs, as the attraction is related to spillovers

in core technological sectors (GPTs), which are of an interindustry kind. Moreover, there is

some evidence relating to cities in the US that diversity across industries may promote

innovation and knowledge spillovers (Feldman and Audretsch, 1999).

(3) Finally, we argue that, according with the bulk of the analysis on overseas R&D,

locational determinants related to the size of the local market have a positive and significant

influence on affiliate R&D location (Zejan, 1990; Kumar, 1996, 2001; Braunerhjelm et al.,

2000). While the other locational effects we have described relate especially to the attraction

of localised R&D in the newer competence-creating types of subsidiaries, the pull of local

market demand relates more to the attraction of R&D in the traditional competence-ex-

ploiting types of subsidiaries for the purpose of adapting products for local markets. How-

ever, when working at the level of firms as a whole or groups of firms, we must consider

these motives together so as to assess the relative significance of each of these pull factors

on average.

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–96 73

All these considerations suggest that innovative activities tend in general to favour specific

locations, and the same rationale explaining location decisions holds in principle both for

foreign-owned and domestic firms. Nonetheless, as the former have a home base located

outside the country, they might be more mobile within the host country in terms of their

choice of location, thus responding more effectively to regional differences. Therefore, we

expect the locational choices of foreign-owned firms to be especially strongly influenced by

the specific features of the region as well as by the potential spillover benefits, as far as their

technological activities are concerned.

3. Evidence on the globalisation of corporate technology at the European regional and

sectoral level

The use of corporate patents as an indicator of advanced technological capacity and the

ability to develop innovation is one of the most established and reliable methods of estimating

the cross-sectional patterns of innovative activities. The advantages and disadvantages of

using patent statistics are well known in the literature (Schmookler, 1950, 1966; Pavitt, 1985,

1988; Griliches, 1990; Archibugi, 1992). The use of patent records provides information both

on the owner of the invention (from which the country of location of the ultimate parent firm

has been derived through a consolidation of patents at the level of international corporate

group) and separately the address of the inventor, thus allowing the identification of where the

R&D underlying the invention was carried out in geographical terms.

The database used for the study consists of patents granted in the US to the world’s 792

largest industrial firms as of 1982, derived from both the Fortune 500 US and the Fortune 500

non-US firms listings (Dunning and Pearce, 1985).3 Of these 792 companies, 730 had an

active patenting presence during the period 1969–1995. Another 54 historically significant

firms were added to these, making 784 corporate groups in all. The additions include (mainly

for recent years but occasionally historically) enterprises that occupied a prominent position

in the US patent records, some of which are firms that were omitted from Fortune’s listing for

classification reasons (e.g. RCA and AT&T were classified as service companies) and others

that reflect recent mergers and acquisitions or new entrants to the population of large firms.

Patents have been consolidated at the level of the international group of ultimate ownership,

allowing for changes due to mergers and acquisitions since 1982. For patents that are

attributable to research facilities located in selected European countries, we have identified

the precise regional location of research, as is explained further below.

Table 1 indicates the share of European host countries in the foreign-located research of

large firms. In particular, it is shown that overall the most attractive European host countries

for the technological activity of foreign-owned MNCs were Germany (29% in 1991–1995),

the UK (21%) and France (16%) and only to a lesser extent Italy (6%). Since 1969–1972, the

3 Fortune 500 provided two separate listings, one for the largest US and one for the largest non-US firms.

While we included all the 500 US firms, non-US firms were then included so long as they were larger than the

500th US firm. Hence, the original 792 includes 292 non-US firms.

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–9674

UK has lost some of its earlier share (29%) to most other countries. Table 2 reports figures by

European host country on the share of foreign-owned firms in total corporate patents ema-

nating from locally based research. The proportion of European research activity undertaken

by foreign-owned companies has increased overall from 23% to 29%, having fallen slightly

during the 1970s and then risen during the 1980s before rising sharply in the 1990s. This is

consistent with the general increase in the internationalisation of technological development in

major firms already acknowledged elsewhere (e.g. Dunning, 1994; Cantwell, 1995).

In order to analyse the location of corporate R&D activities at a more detailed level of

geographical disaggregation, we focused on the subnational entities4 that derive from

normative criteria, as classified by Eurostat in the Nomenclature of Territorial Units for

Statistics (NUTS). The NUTS classification is based on the institutional divisions currently in

force in the member states, according to the tasks allocated to territorial communities, to the

sizes of population necessary to carry out these tasks efficiently and economically and to

historical, cultural and other factors.

To provide a single uniform breakdown of territorial systems, we referred to the NUTS 2

level for the three countries considered. The NUTS 2 level (206 Basic Regions) is generally

used by the EU members for the application of their regional policies and thus is the most

appropriate to analyse the regional distribution of technological activities. Indeed, although

other studies about various regional issues in the EU consider different subnational NUTS

levels for different countries in order to assure economic homogeneity,5 in the present

context, considering NUTS 2 assures a more uniform distribution of patent data across

regions in the period considered. The one exception is that in the case of Lombardia, which is

comfortably the largest region for technological development in Italy, we created a

4 There is evidence that it is in Europe in which cross-border MNC networks have reached their most

advanced state (Cantwell and Janne, 1999), and so examining the determinants of the geographical pattern of

MNC innovation in these regions offers a good test of our alternative hypotheses outlined above.

Table 1

Patenting activity attributable to European-located, foreign-owned research across host countries, 1969–1995 (%)

Total patents from foreign-owned facilitiesEuropean

host country 1969–1972 1973–1977 1978–1982 1983–1986 1987–1990 1991–1995

Germany 27.03 30.23 31.81 35.63 33.47 28.87

UK 29.34 26.78 25.03 22.63 21.00 21.15

Italy 4.34 4.94 4.37 4.50 5.97 6.46

France 13.21 14.95 14.52 14.21 14.92 15.60

Rest of Europe 26.08 23.10 24.27 23.03 24.64 27.92

Total Europe 100.00 100.00 100.00 100.00 100.00 100.00

Source: US patent database developed by John Cantwell at the University of Reading, with the assistance of the

US Patent and Trademark Office.

5 For example Paci (1997) considers 109 regions corresponding to NUTS 0 for Denmark, Luxembourg and

Ireland, NUTS 1 for Belgium, Germany, Netherlands and the UK and NUTS 2 for Italy, France, Spain, Portugal

and Greece. Likewise, Cantwell and Iammarino (1998) and Breschi (1999) consider NUTS 1 for the UK and

NUTS 2 for Italy.

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–96 75

subdivision between Milano and the rest of Lombardia. The empirical investigation uses

patents granted to the world’s largest industrial firms for inventions achieved in their

European-located operations, classified by the host European region in which the responsible

research facility is located.

The regionalisation of our US patent database consists of attributing a revised, although

still compatible, NUTS 2 code for each patent record, according to the location of inventors in

the EU countries, with reference to the period 1969–1995 (Cantwell and Iammarino, 1998,

2000; Cantwell et al., 2001) that has been extended to cover Germany, UK and Italy. The

three host countries substantially differ each other in terms of the magnitude of the

phenomenon under investigation. The total number of corporate patents due to German-

located activity registered in the database over the period 1969–1995 (91,433) is more than

twice that registered for the UK (45,136), which in turn is more than six times that registered

for Italy (7030).

Tables 3–5 report the total number and the percentage share of patents granted to the

domestic firms and to foreign-owned firms in each region considered. Concerning Germany

(see Table 3), it is worth noting that the number of patents granted to domestic firms (76,215) is

about five times that for foreign-owned firms (15,218), while for both the UK and Italy, the

efforts of indigenous firms is about twice that of foreign-owned firms (23,350 as against 11,786

and 4490 as against 2540, respectively). However, in the UK, this is due to a high degree of

both inward and outward internationalisation, while in Italy, it is due in large part to the

comparative weakness of very large indigenous companies in the Italian industrial structure.

In Italy (see Table 5), just as for indigenous Italian firms, foreign-owned firms record the

highest concentration of research (40.16%) in Milano. Outside of this very striking

geographical agglomeration, however, as highlighted by Cantwell and Iammarino (1998),

foreign-owned research appears to be relatively more dispersed than that undertaken by their

indigenous counterparts. Whilst foreign-owned firms locate approximately 68% of their R&D

in the two core regions of Lombardia and Piemonte, 82% of patenting by indigenous firms is

located there.

Some variations in foreign-owned by comparison with indigenous R&D location patterns

are also recorded in the UK. Similarly to the case of Italy (Lombardia), foreign-owned firms

are more highly concentrated in the core region (the Southeast) than are their indigenous

Table 2

Patenting activity attributable to foreign-owned research, as a proportion of all patenting from the local research of

large firms, by European host country, 1969–1995 (%)

Proportion of patents from foreign-owned facilitiesEuropean

host country 1969–1972 1973–1977 1978–1982 1983–1986 1987–1990 1991–1995

Germany 16.32 15.57 15.16 18.77 18.09 17.37

UK 27.66 30.80 31.30 36.00 35.44 45.23

Italy 27.32 31.09 26.49 32.85 43.93 57.50

France 24.17 24.73 24.04 25.13 27.05 28.94

Total Europe 22.70 21.63 21.43 24.40 24.97 28.63

Source: as for Table 1.

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–9676

Table 3

Patents granted for research inGermany in large foreign-owned and domestically owned firms by region, 1969–1995

Region

No. of foreign-

owned patents %

No. of domestically

owned patents %

Stuttgart 1851 12.16 7768 10.19

Karlrushe 992 6.52 2755 3.61

Freiburg 1342 8.82 808 1.06

Tubingen 599 3.94 1089 1.43

Baden-Wurttemberg 4784 31.44 12,420 16.30

Oberbayern 381 2.50 10,785 14.15

Niederbayern 90 0.59 819 1.07

Oberpfalz 283 1.86 559 0.73

Oberfranken 89 0.58 533 0.70

Mittelfranken 422 2.77 3806 4.99

Unterfranken 593 3.90 1238 1.62

Schwaben 284 1.87 1101 1.44

Bayern 2142 14.08 18,841 24.72

Berlin 141 0.93 1875 2.46

Brandenburg 24 0.16 56 0.07

Bremen 47 0.31 128 0.17

Hamburg 754 4.95 315 0.41

Darmstadt 1959 12.87 9195 12.06

Giessen 191 1.26 650 0.85

Kassel 47 0.31 174 0.23

Hessen 2197 14.44 10,019 13.15

Meckelburg-Vorpommern 24 0.16 94 0.12

Braunschweig 110 0.72 913 1.20

Hannover 495 3.25 1048 1.38

Luneburg 147 0.97 349 0.46

Weser-Ems 41 0.27 280 0.37

Niedersachsen 793 5.21 2590 3.40

Dusseldorf 951 6.25 9444 12.39

Koln 1484 9.75 9586 12.58

Munster 135 0.89 1345 1.76

Detmold 44 0.29 300 0.39

Amsberg 282 1.85 1268 1.66

Nordrhein-Westfalen 2896 19.03 21,943 28.79

Koblenz 351 2.31 585 0.77

Trier 86 0.57 253 0.33

Rheinhessen-Pfalz 322 2.12 6212 8.15

Rheinland-Pfalz 759 4.99 7050 9.25

Saarland 62 0.41 137 0.18

Sachsen 33 0.22 112 0.15

Dessau 1 0.01 19 0.02

Halle 4 0.03 15 0.02

Magdeburg 5 0.03 24 0.03

Sachsen-Anhalt 10 0.07 58 0.08

Scheleswig-Holstein 530 3.48 511 0.67

Thuringen 22 0.14 66 0.09

Total 15,218 100.00 76,215 100.00

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–96 77

Table 4

Patents granted for research in the UK in large foreign-owned and domestically owned firms by region, 1969–1995

Region

No. of foreign-

owned patents %

No. of domestically

owned patents %

Cleveland, Durham 82 0.70 629 2.69

Cumbria 19 0.16 136 0.58

Northumberland, Tyne and Wear 164 1.39 166 0.71

North 265 2.25 931 3.99

Humberside 66 0.56 213 0.91

North Yorkshire 107 0.91 362 1.55

South Yorkshire 133 1.13 199 0.85

West Yorkshire 157 1.33 255 1.09

Yorkshire and Humberside 463 3.93 1029 4.41

Derbyshire, Nottinghamshire 143 1.21 921 3.94

Leics., Northamptonshire 319 2.71 503 2.15

Lincolnshire 71 0.60 61 0.26

East Midlands 533 4.52 1485 6.36

East Anglia 621 5.27 342 1.46

Bedfordshire, Hertfordshire 1353 11.48 1528 6.54

Berks., Bucks., Oxfordshire 1231 10.44 1669 7.15

Surrey, East–West Sussex 1250 10.61 1703 7.29

Essex 815 6.91 991 4.24

Greater London 1300 11.03 2487 10.65

Hampshire, Isle of Wight 801 6.80 463 1.98

Kent 432 3.67 574 2.46

South East 7182 60.94 9415 40.32

Avon, Gloucs., Wiltshire 370 3.14 1103 4.72

Cornwall, Devon 62 0.53 64 0.27

Dorset, Somerset 65 0.55 166 0.71

South West 497 4.22 1333 5.71

Hereford-Worcs., Warwicks 135 1.15 983 4.21

Shropshire, Staffordshire 90 0.76 620 2.66

West Midlands 187 1.59 2200 9.42

West Midlands 412 3.50 3803 16.29

Cheshire 235 1.99 1161 4.97

Greater Manchester 455 3.86 1202 5.15

Lancashire 98 0.83 516 2.21

Merseyside 132 1.12 1100 4.71

North West 920 7.81 3979 17.04

Clwyd, Dyfed, Gwynedd, Powys 66 0.56 153 0.66

Gwent, Mid-S-W Glamorgan 323 2.74 398 1.70

Wales 389 3.30 551 2.36

Bord.-Centr.-Fife-Loth-Tayside 187 1.59 175 0.75

Dumfr.-Galloway, Strathclyde 231 1.96 251 1.07

Highlands, Islands 3 0.03 2 0.01

Grampian 53 0.45 42 0.18

Scotland 474 4.02 470 2.01

Northern Ireland 30 0.25 12 0.05

Total 11,786 100.00 23,350 100.00

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–9678

counterparts. Also, as in Italy (Piemonte), indigenous firms locate a substantial proportion of

their innovative activity outside of the core as well, in the West Midlands and the North-

west—regions that, relative to their overall shares, have failed to attract so much foreign-

owned activity. Interesting also is the ability of regions such as East Anglia or Scotland to

attract relatively higher foreign-owned firm innovative activity despite their low overall share

in the UK-owned figure. A similar and indeed stronger result is found in the German case.

Despite the fact that Baden Wuerttemberg is only the third most popular location for German-

owned research, hosting approximately 19%, this region represents the prime location for

foreign-owned firms, which undertake 31% of their research there. The same pattern is

recorded in the northwest region of Niedersachsen, despite the fact that it hosts a low overall

share of total activity (under 4%) but where foreign-owned firms locate over 5% of their

patenting activities.

Indeed, the German case contrasts with patterns recorded for both the UK and Italy on a

number of fronts. Whilst both foreign-owned and indigenous firms concentrate their research

Table 5

Patents granted for research in Italy in large foreign-owned and domestically owned firms by region, 1969–1995

Region

No. of foreign-

owned patents %

No. of domestically

owned patents %

Piemonte 287 11.30 1430 31.85

Valle d’Aosta 0 0.00 1 0.02

Liguria 170 6.69 16 0.36

North West 457 17.99 1447 32.23

Milano 1020 40.16 1986 44.23

Lombardia (excluding Milano) 431 16.97 274 6.10

Lombardia 1451 57.13 2260 50.33

Trentino Alto Adige 2 0.08 13 0.29

Veneto 86 3.39 127 2.83

Friuli Venezia Giulia 16 0.63 87 1.94

North East 104 4.09 227 5.06

Emilia Romagna 169 6.65 186 4.14

Toscana 36 1.42 107 2.38

Umbria 3 0.12 52 1.16

Marche 4 0.16 7 0.16

Centre 43 1.69 166 3.70

Lazio 188 7.40 148 3.30

Abruzzo 2 0.08 20 0.45

Molise 0 0.00 0 0.00

Abruzzo-Molise 2 0.08 20 0.45

Campania 47 1.85 16 0.36

Puglia 34 1.34 13 0.29

Basilicata 0 0.00 0 0.00

Calabria 2 0.08 0 0.00

South 36 1.42 13 0.29

Sicilia 42 1.65 6 0.13

Sardegna 1 0.04 1 0.02

Total 2540 100.00 4490 100.00

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–96 79

in the same region in the UK (the Southeast) and Italy (Lombardia), the same does not hold

for Germany. Nordrhein Westfalen (which borders Belgium and the Netherlands in the west

of the country) hosts the highest concentration of indigenous activity (29%) but only

represents the second most popular location for foreign-owned research. Foreign-owned

firms, as noted above, record their highest concentration of innovative activity in the

southwest region of Baden Wuerttemberg. This differing pattern for Germany, we believe,

can be explained by considering the type of technological activity associated with Nordrhein

Westfalen. This region is the traditional home of the German chemical/pharmaceutical

industry and continues to record substantial technological advantage for indigenous firms

that base their research there (see Table 6 below). This strength is further reflected in the

research profiles of the universities and research institutes located in the region. We

tentatively suggest therefore that because foreign-owned chemical firms may experience

difficulty in trying to access the deeply entrenched technology networks and communication

channels that have evolved through time, they disperse their research more widely and

account for a relatively low share of total German research in chemicals. This deterrence

effect on foreign-owned firms within the industries of primary indigenous strength is quite

common in most countries (Cantwell and Kosmopoulou, in press), but in Germany, it is

distinguished by its strong locational influence, given the heavily regionally specific character

of the leading companies in domestic German industry.

The sectoral forms of innovative activities is shown in Tables 6–8, which examine the

contribution to local research of both foreign-owned and domestic firms by industry. In

Table 6

Patents granted for research in Germany in large foreign-owned and domestically owned firms by industry,

1969–1995

Industry

No. of foreign-

owned patents %

No. of domestically

owned patents %

Food, drink, tobacco and allied products 271 1.78 1 0.00

Chemicals 2234 14.68 33,759 44.29

Pharmaceuticals 803 5.28 5070 6.65

Metals 1008 6.62 6030 7.91

Mechanical engineering 1505 9.89 4352 5.71

Electrical equipment 5425 35.65 12,931 16.97

Office equipment 825 5.42 133 0.17

Motor vehicles 967 6.35 10,838 14.22

Aircraft 166 1.09 970 1.27

Other transport equipment 8 0.05 0 0.00

Textiles 4 0.03 16 0.02

Paper, printing and publishing 91 0.60 12 0.02

Rubber products 99 0.65 190 0.25

Nonmetallic mineral products 755 4.96 0 0.00

Coal and petroleum products 483 3.17 156 0.20

Professional and scientific instruments 510 3.35 1187 1.56

Other manufacturing 64 0.42 570 0.75

Total 15,218 100.00 76,215 100.00

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–9680

Germany (Table 6), foreign-owned firms contribute relatively much in electrical and

computing equipment (35.65% vs. 17%) and in general engineering (9.9% vs. 5.7%) but

relatively little in chemicals (14.7%), the area of greatest indigenous strength (44.3%). This

helps to explain why foreign-owned firms may be less attracted to the main centre for

chemical research in Germany (in Nordrhein Westfalen). The most attractive macroregion for

foreign-owned R&D is Baden-Wurtemburg, which is a centre of engineering excellence in

the motor vehicle industry (in which sphere of technology creation is very highly specialised)

and which has proved to be a magnet for foreign-owned development efforts in the areas of

electrical and computing equipment and general engineering (Cantwell and Noonan, in

press). This area is also well known for the innovativeness of local small and medium-sized

firms (SMEs), whose expertise in developing specialised machinery, equipment and compo-

nents and in engineering may also provide a fruitful interaction with the R&D of large

foreign-owned firms.

Turning now to the British experience (Table 7), foreign-owned firms contribute most to

the UK research base again in mechanical engineering (7.12% vs. 4.22%), electrical (24.74%

vs. 16.4%) and office equipment (7.92% vs. 1.23%) and instruments (3.33% vs. 0.04%).

They have also participated well in the British success in pharmaceuticals research (16.99%

vs. 8.51%) and have made a roughly average contribution in chemicals (19.11% vs. 22.62%).

As a general consequence, the development efforts of foreign-owned firms in the UK are

most attracted as we have seen already the wider technology base and infrastructure of the

higher-order centre of London and the Southeast, and this is especially true in the fields of

Table 7

Patents granted for research in the UK in large foreign-owned and domestically owned firms by industry,

1969–1995

Industry

No. of foreign-

owned patents %

No. of domestically

owned patents %

Food, drink, tobacco and allied products 223 1.89 1225 5.25

Chemicals 2252 19.11 5281 22.62

Pharmaceuticals 2003 16.99 1987 8.51

Metals 545 4.62 1228 5.26

Mechanical engineering 839 7.12 985 4.22

Electrical equipment 2916 24.74 3826 16.39

Office equipment 933 7.92 287 1.23

Motor vehicles 508 4.31 3359 14.39

Aircraft 200 1.70 1737 7.44

Other transport equipment 1 0.01 35 0.15

Textiles 8 0.07 271 1.16

Paper, printing and publishing 78 0.66 119 0.51

Rubber products 123 1.04 581 2.49

Nonmetallic mineral products 199 1.69 507 2.17

Coal and petroleum products 312 2.65 1587 6.80

Professional and scientific instruments 392 3.33 10 0.04

Other manufacturing 254 2.16 325 1.39

Total 11,786 100.00 23,350 100.00

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–96 81

electrical equipment and pharmaceuticals (Cantwell and Iammarino, 2000). Foreign-owned

efforts are relatively much less attracted to the intermediate centres of the Northwest and the

West Midlands than indigenous activity might suggest, but insofar as they are active there,

they match local specialisation in chemicals in the Northwest and in engineering and transport

equipment in the West Midlands.

In the Italian case as well, foreign-owned firms make their greatest contribution to the

domestic research base in general engineering (8.90% vs. 0.0%), electrical equipment

(25.59% vs. 1.40%) and pharmaceuticals (6.42% vs. 0.0%). We know that the development

efforts of foreign-owned firms are drawn even in relative terms to the major centre of

Lombardia due to the availability of general technological skills and wider infrastructure there

rather than for any particularly specialised expertise. However, it is Lombardia outside

Milano that is relatively most attractive for the siting of R&D by foreign-owned firms, while

Milano itself is ranked only moderately by foreign-owned firms. This may be consistent with

what we know of the lack of technological cospecialisation between indigenous and foreign-

owned firms in Lombardia as a whole (Cantwell and Iammarino, 1998). While foreign-owned

companies are keen to access the regional infrastructure, as latecomers (compared to the

established domestically owned firms), they wish to do so while avoiding the costs of

congestion within Milano itself.

Foreign investment in innovation has therefore as much a regional scope as it has a

national one. In particular, recent trends in the EU support the conjecture that a comparative

analysis at the subnational scale is the most appropriate way to identify the effects of

globalisation (Cantwell and Iammarino, 2000).

Table 8

Patents granted for research in Italy in large foreign-owned and domestically owned firms by industry, 1969–1995

Industry

No. of foreign-

owned patents %

No. of domestically

owned patents %

Food, drink, tobacco and allied products 35 1.38 0 0.00

Chemicals 738 29.06 1592 35.46

Pharmaceuticals 163 6.42 0 0.00

Metals 71 2.80 43 0.96

Mechanical engineering 226 8.90 0 0.00

Electrical equipment 650 25.59 63 1.40

Office equipment 171 6.73 609 13.56

Motor vehicles 71 2.80 924 20.58

Aircraft 111 4.37 0 0.00

Other transport equipment 0 0.00 0 0.00

Textiles 1 0.04 113 2.52

Paper, printing and publishing 3 0.12 0 0.00

Rubber products 75 2.95 508 11.31

Nonmetallic mineral products 66 2.60 0 0.00

Coal and petroleum products 82 3.23 638 14.21

Professional and scientific instruments 6 0.24 0 0.00

Other manufacturing 71 2.80 0 0.00

Total 2540 100.00 4490 100.00

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–9682

Having illustrated the geographical and sectoral distribution of the technological activity

carried out by domestic and foreign-owned firms across the regions of the three countries

considered, the main issue that arises is the determinants of asymmetries between the

geographical distribution of foreign-owned firm activity compared to that of domestically

Fig. 1. Domestically owned and foreign-owned patents, Germany.

Fig. 2. Domestically owned and foreign-owned patents, United Kingdom.

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–96 83

owned firms. In other words, we investigate whether foreign-owned labs mirror domestic

location, and if they do not (see Figs. 1–3), the question becomes why, i.e. whether a linear

proportional mapping from the geographical dispersion of indigenous company activity

(a linear agglomeration effect) would exhaustively explain foreign-owned firms’ locational

patterns or whether the effect is instead more complex and reinforced by the attraction exerted

by other location-specific factors.

4. The econometric model and specification of the variables used

The phenomenon under study is the locational preference of foreign-owned firms as

between alternative regions once companies have decided to locate their technological

activities in a given country. Therefore, the dependent variable is the number of patents

granted to foreign-owned firms in each region i and industry j, as follows:

NPAT�FORij = number of patents granted to foreign-owned firms in region i and industry

j over the period 1969–1995: i = 1,. . ., 38 for Germany; i = 1,. . ., 35 for the UK; i = 1,. . ., 21for Italy; j = 1,. . ., 17 industries.

The industrial dimension j allows us to take into account the sectoral disparities in the

innovation-related activities’ propensity to cluster as well as in the propensity to patent.6

Fig. 3. Domestically owned and foreign-owned patents, Italy.

6 The authors are grateful to an anonymous referee for this helpful suggestion.

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–9684

Indeed, while innovative activities tend in general to agglomerate within specific locations,

the intensity of the geographical concentration and the spatial organisation of the innovative

processes may differ remarkably across sectors (Breschi, 1999). The classification of firms

into one of 17 primary industries of output is as shown in Tables 6–8.

As the dependent variable is clearly a count variable, a binomial regression model was

fitted to the data.7 This kind of linear exponential model offers an improved methodology for

count models for the cases of patents and innovation counts (Hausman et al., 1984; Blundell

et al., 1995; Cameron and Trivedi, 1998).

According to the conceptual model developed in Section 2, the independent variables

relate to:

(1) The agglomeration effect or industry-specific spillovers has been proxied by the

number of patents granted that are attributable to the research of domestically owned

firms in region i and industry j over the period 1969–1995 (IND�SPILLij).(2) Local knowledge externalities (external sources of knowledge). In order to capture

the complex character of local knowledge externalities, we considered proxies both

for non-corporate R&D activities and the size of tertiary education in each region.

The proxies used for the former are R&D expenditures and personnel engaged in

R&D in the government sector (RDEXP�Gi, RDPER�Gi) and in higher educa-

tion (RDEXP�Hi, RDPER�Hi). The commitment to higher and further educa-

tion has been proxied by the number of full-time students in total (EDUC�TOTi)and those in higher education (EDUC�Hi). All these data come from the Regio

dataset (Eurostat).

(3) Localised inter-company cluster-based spillovers have been proxied by a composite

variable (CLUST�SPILLij), taking into account the cumulative impact of a widespread

inter-company technological presence (CLUSTERi) in reinforcing industry-specific

spillovers (IND�SPILLij). Specifically, CLUSTERi is measured by the inverse of the

cross-firm concentration of activity in a region, which rises the more widely that

technological effort is dispersed across firms in each region. In particular, the

variable is the inverse of the coefficient of variation across firm shares of patenting

(CLUSTERi = mi/si, where mi and si are the mean and standard deviation, respectively,

over the shares of corporate patenting of all the individual firms active in region i). The

composite variable CLUST�SPILLij has been therefore calculated as CLUSTERi�IND�SPILLij.

(4) General purpose spillovers (GEN�SPILLi) relate to the breadth of technological

development in a region creating the opportunity for interindustry exchanges.

Therefore, they have been proxied by the inverse of the coefficient of variation over

7 The other possible model normally used for count data, the Poisson model, presents a major drawback

related to the fact that the conditional mean is assumed to be equal to the conditional variance, so that any cross-

sectional heterogeneity is ruled out. The negative binomial model provides a generalisation that solves the

problem by introducing an individual unobserved effect into the conditional mean (Greene, 1997).

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–96 85

the profile of regional technological specialisation across technological fields

(GEN�SPILLi = mi/si). The profile of regional technological specialisation is measured

by the RTA index, RTAik in region i and technological field k (where k= 1,. . ., 56).8

(5) Local market size has been proxied by the GDP per capita (GDP�PC). Data for this

variable come from the Regio dataset (Eurostat).

The summary characteristics of the variables and the correlation matrix are reported in

Tables 9 and 10, respectively.

5. Empirical findings

Empirical findings obtained for the three countries are reported in Tables 11–13. As using

absolute numbers of patents as a dependent variable might pose difficulties associated with

differences in the propensity to patent in different industries, this has been circumvented by

using industry dummies.

The results confirm that the geographical agglomeration of innovation is remarkable

(Cantwell and Iammarino, 2000) and demonstrate statistically that foreign-owned firms are

even more sensitive than are indigenous companies to agglomeration potential. Nonetheless,

the estimated equations confirm the differences already highlighted in Figs. 1–3 among the

three countries considered.

Local external sources of knowledge show a positive uniform impact on the locational

choice of MNCs for all three countries. Specifically, a larger educational base is attractive to

the location of foreign-owned research (EDUC�TOT9 is always significant at P < .01) as are

region-specific public science externalities. Indeed, the positive and significant signs for

RDEXP�G10 (at P < .05 in the UK as a supplement to EDUC�TOT and at P< .05 in

Germany and Italy when considered separately rather than in addition to EDUC�TOT) bearfurther testament to the role played by the governments in strengthening the regional science

base by providing the core general funding. The two effects are jointly positive and

statistically significant for the UK, while in Germany and Italy they had to be considered

separately. These results confirm that lower-order regions can be highly relatively attractive

9 The same results hold when considering higher education instead of total tertiary education. As a matter of

fact, EDUC�TOT and EDUC�H are highly correlated (the correlation coefficient is always above .9).

8 The Revealed Technological Advantage (RTA) index is a proxy for specialisation across technological fields,

which fields are groupings derived from the US patent class system (for a discussion and a list of the 56 fields

used, see, e.g. Cantwell and Iammarino, 2000), and is calculated in the following way: RTAik = (Pik/Pwk)/(P

kPik/PkPwk) where Pik= number of patents granted in field k to firms for research in region i and Pwk = number of

world corporate patents granted in sector k. It should be noted that patents associated with some field k may be due

to firms in any industry (any j), and so widespread regional technological development across a broad range of

fields k is usually indicative of the existence of areas of technological overlap between industries and hence

indicates the scope for technological spillovers between industries and especially in GPTs, which are those

technologies that are relevant to more than one industry.

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–9686

where they have a good local science base (Cantwell and Iammarino, 2000). Even more, they

also confirm the importance in Europe of co-location for science–technology linkages, as

demonstrated previously from US evidence (Jaffe et al., 1993).

Table 9

The dependent and independent variables

Mean S.E. Minimum Maximum

Germany

NPAT�FOR 23.557 77.111 0.000 761.000

IND�SPILL 117.980 635.766 0.000 8284.000

MAKT�SIZE 110.947 45.010 15.000 215.000

RDEXP�T 1076.103 583.570 183.900 2516.000

RDEXP�G 162.828 135.180 29.833 795.050

RDPER�G 4950.564 15,042.864 164.000 88,888.566

RDEXP�H 158.654 82.133 55.800 437.433

RDPER�H 3927.306 1718.195 1262.000 8937.500

EDUC�T 358.500 160.302 122.000 792.000

EDUC�H 55.447 33.765 9.000 160.000

CLUSTER 0.004 0.002 0.001 0.011

GEN�SPILL 0.006 0.002 0.003 0.012

UK

NPAT�FOR 19.808 61.407 0.000 825.000

IN�SPILL 39.244 114.793 0.000 1386.000

MAKT�SIZE 84.000 12.682 65.000 128.000

RDEXP�T 559.440 443.509 140.067 1240.829

RDEXP�G 73.054 78.244 3.050 237.525

RDPER�G 577.081 1497.594 0.000 6521.437

RDEXP�H 100.397 1.067 8.000 14.700

RDPER�H 3219.818 2301.895 1512.000 13,040.000

EDUC�T 364.028 101.315 226.333 533.429

EDUC�H 38.257 11.963 21.333 60.000

CLUSTER 0.004 0.002 0.002 0.110

GEN�SPILL 0.006 0.002 0.002 0.014

Italy

NPAT�FOR 7.864 29.614 0.000 323.000

IND�SPILL 13.900 76.877 0.000 881.000

MAKT�SIZE 80.152 43.080 13.100 127.000

RDEXP�T 498.573 550.196 9.000 1725.400

RDEXP�G 115.304 233.595 0.000 1125.700

RDPER�G 1890.190 3875.062 4.000 18,603.000

RDEXP�H 162.311 73.729 51.700 298.500

RDPER�H 3203.266 3037.054 123.000 11,300.000

EDUC�T 302.538 312.523 4.000 1156.238

EDUC�H 75.479 67.900 0.000 234.000

CLUSTER 0.005 0.001 0.003 0.006

GEN�SPILL 0.005 0.001 0.003 0.008

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–96 87

Table 10

Correlation matrix

IND�SPILL MAKT�SIZE RDEXP�G RDPER�G RDEXP�H RDPER�H EDUC�T EDUC�H CLUSTER

Germany

MAKT�SIZE .12

RDEXP�G .01 .01

RDPER�G � .04 .36 � .1

RDEXP�H .04 .01 .94 � .22

RDPER�H .04 .06 .86 � .26 .98

EDUC�T .07 � .14 .67 � .32 � .11 .89

EDUC�H .06 � .05 .69 � .22 .92 .93 .94

CLUSTER � .17 � .32 � .26 .16 � .26 � .24 � .25 � .25

GEN�SPILL .14 .11 .05 � .24 .05 .04 .08 .02 � .24

UK

MAKT�SIZE .14

RDEXP�G .16 .27

RDPER�G � .07 � .23 � .21

RDEXP�H .09 .25 .89 � .09

RDPER�H .07 .23 .84 � .07 .99

EDUC�T .19 .24 .83 � .17 .54 .43

EDUC�H .17 .35 .83 � .12 .64 .56 .93

CLUSTER � .17 .08 � .14 .11 � .14 � .1 � .18 � .14

GEN�SPILL .23 .4 .63 � .09 .5 .44 .57 .43 � .23

Italy

MAKT�SIZE � .2

RDEXP�G � .06 .2

RDPER�G � .04 .12 .99

RDEXP�H � .07 .26 .74 .74

RDPER�H � .08 .27 .87 .86 .97

EDUC�T � .06 � .08 .52 .54 .65 .61

EDUC�H � .1 .12 .73 .74 .85 .84 .93

CLUSTER � .32 .48 .38 .35 .2 .32 .02 .2

GEN�SPILL .18 � .64 � .003 .04 .3 .2 .1 .07 � .43

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Table 11

Estimation results, Germany

Model 1 Model 2 Model 3 Model 4

IND�SPILL 0.0001 (0.972) 0.0001 (0.656)

MAKT�SIZE 0.0220*** (9.516) 0.0235*** (10.000) 0.0217*** (8.932) 0.0231*** (9.344)

RDEXP�G 0.0013** (2.161) 0.0013** (2.146)

EDUC�T 0.0016*** (3.073) 0.0015*** (2.941)

GEN�SPILL 271.1435*** (6.239) 249.7371*** (5.646) 270.7478*** (6.212) 250.6148*** (5.636)

CLUST�SPILL 0.0916 (1.238) 0.0698 (0.946)

Food, drink and tobacco 1.3356*** (3.332) 1.3183*** (3.304) 1.2005*** (2.904) 1.1954*** (2.905)

Chemicals 3.0989*** (7.624) 3.1395*** (7.783) 3.0180*** (7.011) 3.0751*** (7.195)

Pharmaceuticals 2.5016*** (6.388) 2.5289*** (6.500) 2.5142*** (6.248) 2.5437*** (6.359)

Metals 2.3863*** (6.042) 2.3809*** (6.066) 2.3799*** (5.834) 2.3821*** (5.877)

Mechanical engineering 3.0457*** (7.806) 3.0884*** (7.950) 3.0526*** (7.601) 3.0999*** (7.747)

Electrical equipment 4.1346*** (10.538) 4.1333*** (10.586) 4.0234*** (9.953) 4.0330*** (10.017)

Office equipment 2.0936*** (5.236) 2.1085*** (5.333) 2.1419*** (5.221) 2.1629*** (5.331)

Motor vehicles 2.4900*** (6.355) 2.4681*** (6.331) 2.4978*** (6.208) 2.4738*** (6.175)

Aircraft 0.6187 * (1.516) 0.6809 * (1.690) 0.6545** (1.565) 0.7201 * (1.743)

Other transport equipment � 2.4010*** (� 4.244) � 2.4368*** (� 4.316) � 2.5484*** (� 4.322) � 2.5765*** (� 4.382)

Textiles � 2.8694*** (� 4.275) � 2.8435*** (� 4.247) � 2.8215*** (� 4.169) � 2.7933*** (� 4.137)

Paper, printing and publishing � 0.1016 (� 0.237) � 0.0998 (� 0.233) � 0.1702 (� 0.391) � 0.1672 (� 0.384)

Rubber products 0.0461 (0.112) � 0.0845 (� 0.204) 0.1041 (0.247) � 0.0240 (� 0.057)

Nonmetallic mineral products 2.5039*** (6.390) 2.4637*** (6.314) 2.5728*** (6.394) 2.5293*** (6.309)

Coal and petroleum products 2.0351*** (5.171) 1.9600*** (4.990) 2.0177*** (4.990) 1.9419*** (4.809)

Professional and scientific

instruments

1.6823*** (4.228) 1.7143*** (4.363) 1.6693*** (4.082) 1.7125*** (4.243)

Constant � 4.1255*** (� 9.010) � 4.5581*** (� 9.448) � 4.1059*** (� 8.672) � 4.5022*** (� 9.062)

Included observations 595 595 561 561

Log likelihood � 1735.072 � 1732.8122 � 1650.8422 � 1648.941

LR statistic 52,327.673*** 52,332.192*** 50,142.951*** 50,146.755***

LR index (pseudo-r2) .938 .938 .938 .938

Numbers in brackets are z-ratios.

* Significant at P< .10.

** Significant at P< .05.

*** Significant at P< .01.

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The coefficients of the proxy used for localised industry-specific spillovers (IND�SPILL)act either autonomously or in harness with the variable built to proxy the existence of

localised cluster-based spillovers (CLUST�SPILL). In Germany, these two spillover effects

show a positive but not significant coefficient, while foreign-owned firms seem to be much

more attracted by the local market size. The latter is consistent with the observation above

that indigenous technological development is often highly regionally polarised in Germany

Table 12

Estimation results, UK

Model 1 Model 2 Model 3

IND�SPILL 0.0015*** (2.639) 0.0016*** (2.673)

MAKT�SIZE 0.0093 (1.556)

RDEXP�G 0.0028** (2.091) 0.0028** (2.084) 0.0027** (2.026)

EDUC�T 0.0048*** (5.009) 0.0049*** (5.029) 0.0049*** (5.061)

GEN�SPILL 42.8512 (1.185) 66.6064** (2.063) 65.9688** (2.036)

CLUST�SPILL 0.6143*** (2.835)

Food, drink and tobacco 0.1952 (0.600) 0.1899 (0.582) 0.1755 (0.538)

Chemicals 2.3530*** (7.047) 2.3354*** (6.971) 2.3123*** (6.852)

Pharmaceuticals 2.1644*** (6.850) 2.1502*** (6.791) 2.1255*** (6.704)

Metals 0.9622*** (2.996) 0.9672*** (3.003) 0.9378*** (2.905)

Mechanical engineering 2.1219*** (6.627) 2.1398*** (6.666) 2.1204*** (6.601)

Electrical equipment 2.0809*** (6.477) 2.0520*** (6.376) 2.0065*** (6.212)

Office equipment 1.4509*** (4.535) 1.4652*** (4.571) 1.4668*** (4.579)

Motor vehicles 0.8167*** (2.553) 0.7847** (2.448) 0.7482 * (2.326)

Aircraft 0.0861 (0.263) 0.0981 (0.299) 0.0774 (0.236)

Other transport

equipment

� 5.1721*** (� 4.850) � 5.1664*** (� 4.852) � 5.1606 (� 4.849)

Textiles � 2.9386*** (� 5.717) � 2.9446*** (� 5.727) � 2.9468*** (� 5.740)

Paper, printing and

publishing

� 0.9884*** (� 2.864) � 1.0006*** (� 2.892) � 0.9996*** (� 2.893)

Rubber products � 0.6100 * (� 1.817) � 0.5993 * (� 1.782) � 0.5976 * (� 1.779)

Nonmetallic mineral

products

0.7940*** (2.429) 0.7593** (2.322) 0.7507** (2.297)

Coal and petroleum

products

0.0521 (0.160) 0.1076 (0.332) 0.0858 (0.264)

Professional and

scientific instruments

0.0962 (0.299) 0.1337 (0.416) 0.1408 (0.438)

Constant � 1.6698*** (� 2.969) � 1.0497*** (� 2.654) � 1.0373*** (� 2.629)

Included observations 561 561 561

Log likelihood � 1650.012 � 1651.261 � 1650.952

LR statistic 36,042.526*** 36,040.028*** 36,040.646***

LR index (pseudo-r2) .916 .916 .916

Numbers in brackets are z-ratios.

* Significant at P< .10.

** Significant at P< .05.

*** Significant at P< .01.

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–9690

Table 13

Estimation results, Italy

Model 1 Model 2 Model 3 Model 4

IND�SPILL 0.0050*** (3.238) 0.0050*** (3.250) 0.0051*** (3.097)

MAKT�SIZERDEXP�G 0.0000 (0.043) 0.0009** (1.844)

EDUC�T 0.0020*** (3.470) 0.0020*** (3.969) 0.0020*** (4.054)

GEN�SPILL 581.2624*** (6.003) 577.4209*** (5.984) 581.3784*** (6.006) 527.2761*** (4.760)

CLUST�SPILL 1.7068*** (3.264)

Food, drink and tobacco � 0.7540 (� 1.058) � 0.7568 (� 1.063) � 0.7546 (� 1.059) � 0.8225 (� (1.118)

Chemicals 0.5773 (0.823) 0.3867 (0.538) 0.5763 (0.822) 0.8731 (1.188)

Pharmaceuticals 0.4874 (0.711) 0.4914 (0.721) 0.4905 (0.720) 0.4289 (0.600)

Metals � 0.5134 (� 0.736) � 0.5238 (� 0.752) � 0.5149 (� 0.739) � 0.0115 (� 0.016)

Mechanical engineering 1.5371** (2.247) 1.5318*** (2.251) 1.5340** (2.254) 1.7366** (2.429)

Electrical equipment 1.8127*** (2.696) 1.8104*** (2.696) 1.8113*** (2.697) 2.0575*** (2.918)

Office equipment � 0.0750 (� 0.107) � 0.0763 (� 0.109) � 0.0750 (� 0.107) 0.0253 (0.035)

Motor vehicles � 1.6042 * (� 1.857) � 1.6370 * (� 1.878) � 1.6034 (� 1.857) � 1.5617 * (� 1.769)

Aircraft � 0.2790 (� 0.398) � 0.2785 (� 0.397) � 0.2803 (� 0.400) � 0.1876 (� 0.258)

Other transport equipment � 32.4333 (� 0.000) � 35.6421 (� 0.000) � 35.7371 (� 0.000) � 36.3427 (� 0.000)

Textiles � 4.7500*** (� 3.737) � 4.7641*** (� 3.750) � 4.7502*** (� 3.737) � 4.7633*** (� 3.593)

Paper, printing and publishing � 3.2660*** (� 3.535) � 3.2589*** (� 3.532) � 3.2644*** (� 3.536) � 3.1842*** (� 3.384)

Rubber products � 0.4253 (� 0.595) � 0.4179 (� 0.600) � 0.4184 (� 0.601) � 0.3739 (� 0.503)

Nonmetallic mineral products 0.1507 (0.217) 0.1459 (0.211) 0.1483 (0.215) 0.2618 (0.361)

Coal and petroleum products 0.0118 (0.016) � 0.0974 (� 0.134) 0.0123 (0.017) � 0.1150 (� 0.157)

Professional and scientific

instruments

� 3.1537*** (� 3.581) � 3.1439*** (� 3.610) � 3.1592*** (� 3.623) � 2.4018*** (� 2.828)

Constant � 2.1624*** (� 2.798) � 2.1235*** (� 2.756) � 2.1651*** (� 2.809) � 0.9709 (� 1.273)

Included observations 153 153 153 153

Log likelihood � 378.08471 � 378.143 � 378.086 � 385.0559

LR statistic 6673.6567*** 6673.540*** 6673.655*** 6659.7143***

LR index (pseudo r2) 0.900 0.898 0.898 0.896

Numbers in brackets are z-ratios.

* Significant at P< .10.

** Significant at P< .05.

*** Significant at P< .01.

J.Cantwell,

L.Piscitello

/JournalofIntern

atio

nalManagem

ent8(2002)69–96

91

and the qualification that the agglomeration effect can only work where there are a variety

of sources of spillovers and the absence of a single dominant firm that acts to competitively

deter its major rivals. In contrast, in the UK and in Italy, CLUST�SPILL and IND�SPILLare both highly significant (P < .01) attractors to foreign-owned activity. Conversely, the

local market size does not seem to particularly influence the foreign MNC’s relative lo-

cational choice. The combined industry-specific and cluster-based spillover effects show

their strongest impact in Italy. In Italy and the UK, there seems to be a general ag-

glomeration effect associated with the widespread dispersion of patenting firms in the same

region and in the same sector of activity. The presence of general purpose spillovers

(GEN�SPILL) shows a significant positive impact on the location of foreign-owned re-

search facilities especially in Germany and Italy and only to a slightly lesser extent in the

UK. The technological breadth of a region and the presence of innovative overlaps across

industries in the development of GPTs is an important factor in the attraction of foreign-

owned research facilities.

6. Summary and conclusions

Since the late 1970s (Cantwell and Piscitello, 2000), large MNCs have increasingly

extended or diversified their fields of technological competence through their use of

internationally integrated networks for technological development. In each location, in

such a network, MNCs tap into specialised sources of local expertise, and so differentiate

their technological capability, by exploiting geographically separate and hence distinct

streams of innovative potential. The recent emergence of internationally integrated MNC

networks is best observed in Europe, where the contribution of foreign-owned MNCs to

national technological capabilities is much greater than elsewhere. About one-quarter of

large firm R&D carried out within in Europe has been conducted under foreign ownership

(and this figure had risen to nearly 29% by the early 1990s), while the world average is

only just over one-tenth. Part of the reason is that European-owned MNCs are the most

internationalised in their strategies for technology development, while much of their fo-

reign-located R&D has remained within Europe and their European orientation has in-

creased (from a 30% share of foreign R&D in Europe in the late 1960s to a 40% share by

the 1990s).

Our results suggest that the relative attractiveness of regions in Europe to the technological

efforts of foreign-owned MNCs depends upon (1) the presence of external sources of

knowledge, (2) the presence of industry-specific and cluster-based spillovers and (3) the

breadth of local technological specialisation in the region, i.e. the opportunity to capture

general purpose spillovers. That has some implications in suggesting regional policy forms

mainly based on regional investments (rather than exclusively on regional incentives), which

enhance the attractiveness of the region as an appealing economic environment for potential

investors (Braunerhjelm et al., 2000).

Factors (1) and (3) seem to matter for regions throughout Germany, the UK and Italy. This

is consistent with other literature that has emphasised the growing importance of science–

J. Cantwell, L. Piscitello / Journal of International Management 8 (2002) 69–9692

technology spillovers in the current technoeconomic paradigm and which is now paying

increasing attention to the central role of GPTs. To these latter strands of recent literature,

what we add here is the dimension of corporate internationalisation: MNCs will develop

abroad in the appropriate international centre GPTs alongside the firms of other industries

and technologies that rely on linkages to a good local science base. Factor (2) depends

critically on the dispersion of technological development among a sufficient variety of local

actors to attract foreign-owned research to a localised cluster. This occurs quite often in the

UK and Italy but when, as is more frequently the case in Germany, local development is

heavily concentrated in just a few leading firms in a region (i.e. where the leading

domestically owned firms are strongly regionally separated and each have a clear regional

identity), then a crowding out effect is likely to outweigh any agglomeration attraction. In

Germany, each of the major companies, e.g. in the chemical industry, has ‘‘its own’’ region,

and so in a sense, the deterrence effect to technological entry in a region with an existing

dominant player is observed even among the large indigenous German firms themselves.

Naturally, it affects foreign-owned firms in the same industry (and hence which are

competitors of the dominant company in a region) just as much, and so there is much less

scope here for an agglomeration effect.

The present paper could be extended to consider the type of motivations of foreign

investment in each location. Countries and regions seek to attract MNC activity as a means of

improving their locational advantages through spillovers and linkages due to MNC activity.

However, the quality and the extent of the externalities due to MNC activities depends on the

motivation of their investment, which is itself dependent on the kinds of location advantages

available to them (Narula and Dunning, 2000; Cantwell and Narula, 2001).

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