Geographical Distribution and Regional Specialization of Knowledge-Intensive Business Services: An Empirical Investigation Across European Regions

Abstract

This chapter is aimed at investigating both geographical concentration and regional specialization in KIBS during the last years. Specifically, this chapter focuses on European regions and seeks (a) to identify the location of KIBS, (b) to investigate regional specialization profiles in KIBS and (c) to shed light on the variables driving the location of KIBS. Results on spatial concentration show a process of spatial dispersion between 1995 and 2007 that is modified afterwards due to the economic crisis. Furthermore, the data also reveal important differences in terms of regional specialization for each category of KIBS. Finally, our analyses confirm the existence of significant influences of several variables related to economies of agglomeration and accessibility to clients and knowledge resources on regional specialization in KIBS across European regions, as stated by the previous literature.

1 Introduction

Tertiarization processes characterize structural changes at both national and regional levels. In this context, knowledge-intensive business services (KIBS) have made a profound contribution to the creation of employment, increases in production levels and the promotion of investment, especially in industrialised regions. During the expanding cycle of the European economy (1995–2007), the amount of net employment increased by 26 million in the EU-15 and more than a third were generated by KIBS (and the two other thirds by Other KIS and Less KIS, respectively). Thus, employment in KIBS has been one of the main drivers of employment over the last years. Moreover, during the period of economic crisis (2008–2010), whilst total employment decreased by more than five million, net employment in KIBS increased by 226,000 throughout the EU-27. As a consequence, the literature has paid an increasing attention to the study of KIBS over the last years (Alvesson, 2004; Cuadrado-Roura & Maroto-Sánchez, 2010; Fischer & Frolich, 2001; Gallego & Maroto, 2010; Koch & Stahlecker, 2006; Mudambi, 2008; Muller & Doloreux, 2009; Rubalcaba-Bermejo & Cuadrado-Roura, 2001; Vence & González, 2002).

Previous works have stressed the crucial role of such services in the dissemination of both tacit and explicit knowledge (Gertler, 2003). Nonetheless, while advances in Information and Communication Technologies (ICT) have fostered geographical distribution of knowledge-intensive services, this achievement mainly refers to explicit knowledge (i.e. knowledge that can be codified through data, formulae, books, papers, etc). Hence, diffusion of tacit knowledge (i.e. knowledge that resides in the minds of individuals and cannot be codified) still faces strong geographical barriers (García-Velasco, 2005; Howells, 2002).

Interest in recognition of the increasingly central role of KIBS in Western economies began in the late 1970s and the early 1980s (Coffey, 2000). As a consequence, the location of knowledge-intensive business services has been extensively documented for the 1970s and 1980s when researchers turned their attention to the effects of tertiarization upon regional development (Beyers & Alvine, 1985; Coffey & Polése, 1987; Drennan, 1987; Illeris, 1996). A key focus of their attention was the potential for regional development centred on KIBS, and, in more general terms, the uneven spatial development that seemed to be reinforced by the spatial concentration of these services towards the top of the urban hierarchy. Whilst an increasing proportion of recent literature has adopted a non-geographic perspective to investigate the role of KIBS in innovation systems and, more generally, in the process of innovation (Antonelli, 1999; Attewell, 1992; den Hertog, 2000; Miles, 2005; Muller & Zenker, 2001; Strambach, 2001; Wood, 2006), simultaneously the attention paid to the spatial distribution of this type of service has not decreased. The profound and increasing interest in the geographical location of KIBS is justified by their widely-recognized central role as facilitators in local milieu and in flexible production as well as being vectors of information procurement and exchange (e.g., innovation and growth in manufacturing (and other) companies is linked to their access to, and use of, producer services (Cooke & Leydesdorff, 2006; MacPherson, 1997)).

Given the relevance of KIBS as a source of local and regional competitiveness (Gallego & Maroto, 2010, Martínez-Alcocer & Maroto-Sánchez, 2010) as well as the role played by location in their performance (e.g., Wood, 2006), this chapter serves to identify the main trends in the evolution of regional concentration and specialization in KIBS across European regions over the last years. Specifically, the territorial unit under analysis corresponds to NUTS 2 regions in the European Union (EU), and the period analysed ranges from 1995 to 2010—so that the study covers the effects of the enlargement of the EU (2004 and 2007), the effects of cohesion policy (1994–1999 and 2000–2006), economic expansion (1995–2007) and the beginning of the economic crisis (2008–2010), among others. This text seeks to answer the following questions: Where have KIBS located in Europe? In which regions has the share of employment in KIBS as compared to total employment been more important? What has been the nature of the geographical allocation of KIBS in EU regions? Which factors have contributed to explain the location of KIBS?

To deal with the aforementioned objectives, the empirical analyses are structured into different parts. The descriptive part of the analyses relies on the study of geographical concentration and regional specialization in European regions. This part considers employment as the main variable that can explain the spatial allocation of KIBS across European regions taking Eurostat as its source. In general, the territorial units taken in account have been NUTS 2 level for the 27 EU member countries. The period considered is 1995–2010, although data for earlier years have not been available for several countries, and there have been some methodological changes. Then several phases have been considered: 1995–2000, 2000–2004, 2004–2007 (enlargement), and economic crisis (2008–2010). One important caveat is that by considering employment and not GDP, this implies that regional differences or progress in productivity are not taken in account.

After the descriptive analyses, the chapter investigates the main factors that may help to explain the location of KIBS in European regions, such as the influence of certain variables related to economies of agglomeration and accessibility to clients and knowledge resources on the regional specialization patterns of KIBS. Before undertaking those empirical analyses, the following section aims to provide a theoretical review of the relevance of proximity in the location of KIBS. Finally, the last section summarizes the main conclusions.

2 Relevance of Proximity in Location of KIBS: Theoretical Background

The concept of geographical proximity has traditionally been linked to the spatial distance between actors. Previous research has stated that, ceteris paribus, the greater the distance between actors, the less intensive the positive externalities (Knox & Agnew, 1994; Krugman, 1991).

As described by previous works, KIBS seek to result in the creation, accumulation or dissemination of knowledge in general and tacit knowledge in particular. Given the role of KIBS as facilitators and disseminators of knowledge, geographical proximity is crucial for efficient knowledge transfer. Lo (2003) points out three distinctive features which are important for knowledge transfer: (1) in contrast to data and information, knowledge is linked to individuals and context, (2) the more implicit knowledge there is, the more difficult the transfer is without personal contact, (3) knowledge is limited to specific organisational and spatial territories; this may also be valid for codified knowledge.

Although transport facilities and information and communication technologies have experienced a rapid development that is reducing the barriers of distance in service production, many economists claim that spatial proximity is still crucial in economic processes because while information diffuses rapidly across organisational and territorial borders, it is wrongly assumed that understanding does, too (Morgan, 2004: 3).

The advantages derived from proximity in terms of the performance of KIBS can be classified into different groups. Firstly, geographical proximity is assumed to foster processes of innovation (e.g., Boschma, 2005). These processes rely heavily on the exchange of information and knowledge between different actors and are frequently based on learning-by-interacting (Howells, 2002). Specifically, KIBS are intended to result in the creation, accumulation or dissemination of knowledge in general, and tacit knowledge in particular (e.g., know-how, know-who). The transfer of tacit knowledge requires trust, common understanding, frequent communication, and face-to-face contact (Roberts & Andersen, 2000). These factors are favoured by short physical distances amongst the actors involved (e.g., Howells, 2002; Morgan, 2004). Thus, provided that the experience of common work and co-location is essential within the exchange process of implicit knowledge, geographical proximity can be conducive to efficient knowledge transfer. As innovation is a crucial element in the early development of firms in the KIBS sector, spatial proximity can be assumed to have a positive effect upon the post-entry growth of these firms.

Secondly, relations between firms providing KIBS and their clients are highly complex, i.e., both the provision and the development of new and innovative services take place mainly via close interaction between service providers and clients. Such interaction involves a high degree of customization, and the communication involved must be highly intensive; consequently, practices such as standardization, routinization, and supervision are difficult to apply (Alvesson, 2004; Miles et al., 1995). In this context, KIBS firms must keep in permanent contact with clients. Specifically, in the cases of certain KIBS firms offering tailor-made knowledge of a product for a particular customer, the relationship with the client may be crucial. Since the establishment and continuity of these relations is facilitated by spatial proximity, locally embedded firms may have better expectations for growth (Boschma & Weterings, 2005). Furthermore, intense interaction between service providers and clients, which is the norm in the provision of knowledge intensive services, is improved by short distances between agents (Illeris, 1994).

Thirdly, the socialisation and learning procedures necessary for the successful transfer of tacit knowledge require co-presence and co-location between transmitter and receiver (Roberts & Andersen, 2000). The provision of knowledge intensive services requires specialized knowledge and cumulative learning processes, which can mainly be achieved through intense interaction between service suppliers and clients (Johannisson, 1998; Strambach, 2002). A great deal of tacit knowledge is developed interactively and shared within networks, so that geography becomes relevant in the transfer of knowledge by KIBS firms. Moreover, the accumulative nature of knowledge generates spatial spillovers, as stated by Marshall (1890). Consequently geographical—as well as social, political, psychological—distance is an influential factor in the diffusion of tacit knowledge (Acs et al., 2002). The partly tacit character of knowledge is likely to be responsible for the importance of localised networks of personal contacts in innovation activities of firms in some metropolitan regions (Fischer & Frolich, 2001). From this point of view, the location of KIBS firms may be of great importance in major metropolitan regions (Wood, 2002).

Fourthly, the relevance of distance to clients in the locational decisions of KIBS firms obviously depends on the degree of tacitness in knowledge, and the greater the need for a customized service, the lesser the locational freedom. KIBS firms supply advanced services, which are usually tailored to the requirements of other companies or public agencies (Aslesen & Isaksen, 2007; Wood, 2002), and they may be considered to be critical in terms of achieving greater competitiveness for firms or increased efficiency in the public sector. In this case, a decisive factor in location could be the wish to be as close as possible to government institutions. Thus, KIBS firms are frequently located in capital cities or set up a branch close to them. Nonetheless, some authors have pointed out that a lower importance of the distance derived from an intensive use of the ICT in many activities has turned the output of these companies into a tradable good. This implies the reduction of the weight of demand location as an explanatory factor of its location in favour of other factors. Obviously, from these changes it is not deduced that the location pattern must be necessarily different, but rather that one of the factors explaining the location has partly lost its importance and, therefore the location pattern may be different.

Furthermore, location is important not only from the clients’ perspective, but also in terms of accessibility to resources. In light of this question, KIBS firms try to locate near to information and knowledge sources (Doloreux et al., 2008). In this sense, purely neoclassical macroeconomic theory justifies business growth in terms of available factors so that the production of advanced services would be concentrated on those areas with plenty of productive factors (mainly human and technological capital) and would be less costly. Nonetheless, this approach needs to be complemented by new endogenous growth, thus reinforcing the role of knowledge and other intangible elements, i.e., factors that are hidden behind technological change and total factor productivity. Therefore, the second group of location factors includes a heterogeneous set of drivers such as access to highly-educated workers and accessibility to information and other resources.

It is important to note that tacit knowledge, because of its nature, is context-specific (Gertler, 2003). Proximity to related professional services (competitors) and experts (Keeble & Nachum, 2002; Sokol et al., 2008) and suppliers (Cook et al., 2007; Freel, 2006; Koch & Stahlecker, 2006) may also be important from this point of view. Proximity to government may sometimes be important due to the need for KIBS firms to know the modifications in public regulations as soon as possible.

Nonetheless, it is important to note that, in terms of greater accessibility to resources derived from geographical proximity, the most relevant resource is human capital. KIBS firms are attracted to locations with a great pool of educated people. The core activities of these companies are based on the intellectual skills of a very large proportion of the labour force and also are often based on the sale of products and on service work. A large number of employees typically have an academic education and relevant experience. Formal education is seen as very useful for facilitating theoretical and analytical abilities essential to such organizations (Alvesson, 2004). Thus, proximity to this factor becomes crucial when there is some degree of immobility in human capital, because of professional factors—their skills and abilities may be related to the environment—social or family reasons. Thus, KIBS firms are attracted to areas where there is a great availability of human capital (Cook et al., 2007; Keeble & Nachum, 2002; Koch & Stahlecker, 2006; Sokol et al., 2008).

Spatial proximity contributes to the foundation of new firms. Previous research stresses the fact that most new firms are small and have limited financial and personal resources in order to explain the fact that the vast majority of start-ups are established in the region that the company founders have been living and/or working in before (e.g., Brüderl et al., 1996; Cooper, 1985). Access to information, knowledge and other resources is facilitated by existing personal and social networks that are usually best developed within a short geographical distance (Illeris, 1994; Johannisson, 1998).

Proximity to clients, suppliers, competitors, human capital, and information sources is very important for an efficient provision of KIBS. Thus, agglomeration is crucial driver for location, and many advantages are inherent in this kind of location. Moreover, uncertainty and vulnerability derived from the process of globalization mean that KIBS firms are attracted towards agglomerations.

Therefore, from a theoretical viewpoint, the factors that influence the geographical location of KIBS can be classified into different groups, i.e., demand (clients) and supply factors (information and knowledge mainly). Nevertheless, accessibility seems to be the crucial element and therefore the concept of agglomeration economies is fundamental. As a result of a careful review of a significant amount of research on services, in general, and on KIBS, in particular (Beyers, 2005; Coffey & Shearmur, 2002; Wood, 2002), we are in a position to provide a classification of drivers that influence the spatial distribution of KIBS.

2.1 Agglomeration Economies

Agglomeration economies can be defined as the benefits associated with the co-location of firms and advantages created by the concentration and co-habitation of economic activities in terms of access to: markets, suppliers, a varied and qualified workforce, formal and informal networks, specialised services and industries, and technological infrastructure (Malmberg & Maskell, 2002; Maskell & Kébir, 2006).

Previous research has shown that firms benefit and become more productive from interaction with each other, due to interaction and communication externalities (Fujita & Thisse, 2002). Consequently, large agglomerations—especially urban ones—gain an advantage that stimulates further, cumulative, growth. Individual firms can benefit from upstream and downstream externalities that bring about co-location advantages.

2.2 Knowledge Spillovers

Knowledge spillovers are derived from agglomeration economies. This term refers to positive milieu-externalities created by: research and other investments in knowledge, inter-firm labour mobility, and local skill development processes (Feldman, 1994). Knowledge spillovers benefit co-located actors with investments made by neighbours, while spatially separated actors are not able to access these kinds of effects (Marshall, 1890).

2.3 Region and Urban Size

Beyond other drivers, region and urban size remain the principal organizing factors for KIBS across space. According to theories of urban agglomeration, advanced services would benefit from the scale economies offered by metropolitan centres. For example, the empirical evidence for the existence of agglomeration economies and localised knowledge spillovers is often found in metropolitan regions.

This geographical concentration or selective agglomeration is crucial for the success and competitiveness of firms offering knowledge services (Scott, 1988). The strong concentration of KIBS in larger cities and the capital region especially, and the fact that KIBS derive a considerable share of their business from local customers indicate a more intense state of competition in these urban areas. Furthermore, KIBS encourage the innovative development of cities by strengthening connections amongst strategic planning of firms (Wood, 2002).

Already two decades ago, Coffey and Polèse (1989) undertook an investigation motivated by the attention KIBS received as policy-levers for developing lagging regions, and came to the conclusion that the potential for KIBS to locate outside of major metropolitan areas was limited. Additionally, as expected, location in a large urban centre seems to be relevant in the use of decision functions, at least in certain specific branches.

Indeed, urban agglomerations consist of interlinked urban areas, which in turn can be decomposed into zones. For each of these levels, there are co-location advantages so that spatial concentrations can be observed at different levels of spatial resolution. This implies that a system of urban agglomerations has a radial result, with major centres, centres of urban areas as well as sub-centre concentrations (Anas et al., 1998).

2.4 Labor Market Effects

This factor refers to the quality of human resources and their level of skills and qualifications (Coffey & Shearmur, 2002). To have access to a labour force with better greater competences and skills as well as higher levels of experience has traditionally been considered as essential in order to develop advanced or highly technological activities (Illeris, 1996). In this sense, the relationship between the base of knowledge of human capital and the change and economic growth of an urban centre has a crucial relevance (Glaeser et al., 1995; Matthiessen et al., 2002).

2.5 Accessibility

A strong infrastructure for transport and communications are one of the criteria that drive a firm’s decision about whether to concentrate on certain areas or regions. Nonetheless, while some studies assume that firms supplying KIBS make their location decisions as a response to the accessibility to customer demand (customer contacts) that each possible location offers, there is no commonly accepted measure for accessibility in the literature (e.g., Johansson & Klaesson, 2007; Mattsson, 1984; Weibull, 1976).

2.6 Other Local Qualitative Aspects Characteristics

Among these factors, we can cite governmental structures, cooperation among firms or the institutional context existing in the different geographical areas (Cooke et al., 2004). Nevertheless, due to the qualitative nature of these factors, they are difficult to measure and to approach from a statistical point of view (Doloreux et al., 2001).

The influence of these factors upon KIBS location depends on the characteristics of the activity (degree of customization, specialization, etc.), as well as on the mix of factors mentioned previously. Thus, it is difficult to determine a priori whether KIBS development may improve territorial cohesion or not. In this point, empirical analyses constitute crucial tools to ascertain the geographical distribution of KIBS and the effects upon regional development.

3 Spatial Allocation of KIBS Across European Regions: A Descriptive Approach

Geography of KIBS in EU regions is characterized by a high degree of spatial concentration and polarization of activity across relatively few regions. Some areas show a high degree of specialization and they are located in regions containing capital cities, areas around them (Brussels and London), and some other central cities (Amsterdam, Rotterdam, Frankfurt, Hamburg and Munich) overall. On the contrary, regions with low level of specialization locate in Objective 1 regions and regions of New Member Countries (except capital cities).

The evolution of spatial allocation of KIBS in EU regions has shown a reduction in the geographical concentration until 2007 at least, thus benefiting Objective 1 regions during the first stage (1995–2007), and regions of New Member Countries during the last decade. However, the current economic downturn seems to have decelerated regional convergence in KIBS employment.

3.1 Profiles of Regional Specialization in KIBS and Geographical Concentration

Spatial allocation of KIBS in EU regions has been very concentrated. The Gini¹ index for KIBS achieves the highest scores among services (Fig. 13.1). Thus, 33 % of employment in KIBS (UE-27) was located in 34 regions—19 regions that hosted capital cities, and 6 regions in England (close to the London region), 5 in Benelux and 4 in Germany. This space contained 4.8 % of the area of EU, 18 % of the population and 26 % of the GDP in 2008. Several authors (e.g., Vence & González, 2002; Wood, 2002) have pointed out that KIBS have concentrated in major metropolitan regions, suggesting that this is where client-consultancy demand-supply interactions remain best developed.

Fig. 13.1

Evolution of Gini index in KIS employment in EU regions

It is evident that the greater the knowledge intensity in a service activity, the greater its geographical concentration. It can be observed in that values of the coefficient of variation² are reflected in the confidence intervals.³ These values are greater in every category of KIBS than for Other KIS and Less KIS (L-KIS), and Manufacturing (High Tech, Medium and Low Tech). Therefore, the confidence interval of specialization quotient⁴ (SQ) ranges from 0.65 to 1.35 in KIBS roughly, wider than L-KIS (0.85–1.15) and O-KIS (see Tables 13.1 and 13.2 in the Annex of Tables).

Figure 13.2 represents the share of employment in every level of SQ in L-KIS, O-KIS and KIBS. This figure shows a shape for KIBS that is very different from a normal distribution as compared to O-KIS and L-KIS, which are more normal-shaped and sharpened.

Fig. 13.2

Employment allocation in EU regions in 2010

Fig. 13.3

Specialization quotient in KIBS and variation in SQ on 1995–2004 (EU-15)

Fig. 13.4

Specialization quotient in KIBS and variation in SQ on 2008–2010 (EU-27)

Fig. 13.5

Employment allocation in KIBS in EU regions

Fig. 13.6

Evolution of Gini index in KIBS employment in EU regions

Fig. 13.7

Employment allocation in KIBS in EU regions in 2010

Fig. 13.8

Specialization quotient in market KIBS in 1995 and variation in SQ 1995–2004. EU-15 regions

Fig. 13.9

SQ in financial KIS in 2008 and variation in F KIS employment on 2008–2010 (EU-27 regions)

The high spatial concentration in KIBS leads to great differences within EU regions. This may imply the existence of different groups according to the level of specialization.⁵ The results of that distribution are collected in Tables 13.1 and 13.2 (see the Annex of Tables). The data reveal that a few regions with a high level of employment in KIBS have benefited from a large share of that employment. On the contrary, many regions have shown very low levels of employment in KIBS.

The geography of KIBS in EU regions has shown an irregular central-peripheral pattern, very different to the regular central-place model. Economic and political relevance seems to have influenced in the location of KIBS activity. KIBS employment has been found in capital city regions since in every country the importance of KIBS in the total employment structure in capital cities has been higher than in the other regions of the country. However, the level of economic development has also explained to some extent the regional allocation of KIBS in general.

In 1995, KIBS were very important in capital city regions and in some regions of developed countries. There, the share of employment in KIBS as compared to total employment was higher than in other regions. Consequently, these regions achieved high SQ values. On the contrary, the lowest levels of SQ in KIBS employment were located in Objective 1 regions.

In 2010, capital city regions—including those belonging to New Member Countries—and developed regions again showed the highest relative level in KIBS employment. On the contrary, those regions with the lowest SQ in KIBS belonged to 12 New Member Countries that joined the EU after the enlargement that took place in 2004 and 2007 with the exception of their capital cities (Map 13.1 registers SQ levels in KIBS in EU regions). Therefore, one out of two regions with the lowest value in SQ in KIBS belonged to a New Member State. In particular, many regions of Bulgaria, Hungary, Poland and Romania have shown the least values.

Map 13.1

Specialization quotient in total KIBS employment in 2010

The Objective 1 regions of Cohesion Countries during the programming periods previous to 2007 (Portugal, Ireland, Greece and Spain) and Southern Italian regions have reached low levels of SQ, but they were higher than in the case of the regions of the 12 New Member States in general. Within this group, Ireland and some Objective 2 regions of Spain (Basque Country and Catalonia) have reached the medium index.

In the rest of the EU, only a few regions of developed countries located in central areas within France (6 regions out of 21) and peripheral regions of Germany (3 regions out of 33) and United Kingdom (5 regions out of 36) have obtained the lowest levels of SQ in KIBS employment.

3.2 Evolution in Regional Specialization in KIBS and Geographical Concentration

There have been several changes in the spatial allocation of KIBS employment in EU regions over the period 1995–2010. Throughout these years, the evolution of employment in KIBS in EU regions has been marked by several changes and periods of upheaval. Firstly, the EU expanded from 12 Member States to 27. Austria, Finland and Sweden joined the EU in 1995, 10 New Member Countries entered in 2004 and Bulgaria and Romania became members in 2007. During this period, there was a period of significant economic expansion until 2007 and a very profound crisis from 2008–2010. Moreover, there have been several programming periods of cohesion policy: 1994–1999, 2000–2006 and 2007–2013. During the first two periods, Cohesion Countries (Portugal, Ireland, Greece and Spain) benefited from the financial assistance of the EU cohesion policy; nonetheless, from 2007 onwards, those financial resources have lost relative importance in these countries, and have been reallocated towards the regions of New Member Countries. Thus, the analysis of the evolution of regional allocation of KIBS employment in EU-27 during this period should take in account these events and the methodological changes in data collection in 2000 and 2008, so that all of this period may be divided into several phases: 1995–2000, 2000–2004, 2004–2007, and 2008–2010.

In general, employment in KIBS has increased at a higher rate than total employment in the EU, so the weight of KIBS in total employment has also increased. The annual average rate of variation in KIBS employment was 3.2 % for EU-15 over the period 1995–2007, and 1.3 % for total employment. Nevertheless, growth rates in KIBS employment have been decreasing throughout the different phases. During the period 1995–2000, the average annual rate of variation was 3.7 for EU-15. Throughout the first decade of the twenty-first century, these rates were decreasing for EU-27, however they were greater in EU-15 during 2004–2007 than in the previous period (2000–2004). Nevertheless, throughout the current economic crisis, employment in KIBS has hardly increased over the period 2008–2010.

Behaviour has also been different according to the kind of region. Overall, Objective 1 regions belonging to Cohesion Countries (and Southern Italian regions) performed better with regard to growth in KIBS employment in the EU during the period 1995–2007 (within EU-15) than other regions. In those regions, the annual average rate of variation in this variable was 5.8 %, and the areas where the growth was more intense were located in Irish and Spanish regions. Thus, the SQ in KIBS employment—the share of employment in KIBS as compared to total employment in relation to the EU mean—rose in these regions on average whilst the relative values of regions in developed countries decreased (see Fig. 13.3). Therefore, the gap between these Objective 1 regions and the rest of EU regions with regard to SQ values in KIBS employment has narrowed over the 1995–2007 period. Nevertheless, this general evidence of convergence has masked significant differences in the evolution of SQ values amongst the Objective 1 regions themselves.

Amongst the causes for this different expansion one may identify several factors: the low level of SQ values in KIBS employment in these regions at the beginning; a favourable business cycle; and the effects of cohesion policy which may also have contributed to those results.

However, during the current economic crisis, employment in KIBS in these regions has decreased at a high rate between 2008 and 2010 (−3.7 %), affecting Mediterranean regions more strongly than others. In addition to the effects of a profound economic crisis, European financial resources towards these regions have reduced in relative terms for the programming period of EU cohesion policy 2007–2013.

Overall employment in KIBS in regions of New Member States has increased at a high rate. During the period 2004–2007, many regions of Poland, the Czech Republic, Estonia, Latvia, Lithuania, Cyprus, Malta and Slovakia—even some Romanian and Bulgarian regions—reached a high rate of growth in KIBS employment.

Throughout the current economic crisis, KIBS employment in many regions of New Member Countries managed to grow—mainly in regions of Poland, Romania and Slovenia—during 2008–2010 when the annual average rate of growth in KIBS employment was 3.2 %. Similar causes to the aforementioned case—growth in KIBS employment in Objective 1 regions during 1995–2007—may explain this expansion: a low initial level and effects of cohesion policy may have contributed to these results. Since 2007, these regions have benefited most from the European financial resources devoted to cohesion policy. Overall SQ in KIBS employment has increased in these regions while the relative values of the other regions have decreased (see Fig. 13.4).

Thus, there has been a process of catching up and reduction of regional disparities in KIBS employment, but with different actors in each stage. At the same time, there has been a spatial reallocation of KIBS employment from their traditional sites towards Southern regions in the first stage and towards Eastern regions later. Among the regions that have most notably increased their position in the ranking of KIBS employment are the capital cities of Cohesion Countries (Athens, Dublin, Lisbon and Madrid) and New Member Countries (Bratislava, Budapest, Bucharest, Prague and Warsaw) and some other regions such as regions of Luxembourg or Germany (Bavaria, Bremen, Düsseldorf, Karlsruhe and Köln).

The evolution of the Gini index has been decreasing, in general terms, since 1998. Thus, there was a process of spatial dispersion in KIBS before 2007.⁶ Traditional regions where KIBS were agglomerated lost presence in favour of peripheral regions in general, mainly in Spain, Portugal, Greece, Ireland, Italy, Latvia and Lithuania. Other areas that benefited from the geographical dispersion of KIBS were located in regions where KIBS were not very important in the past (e.g., in some regions of France, Germany, Sweden and the United Kingdom).

Regional differences in employment in KIBS across EU regions have narrowed. As a result, the shape of figures is progressively approaching a normal distribution (see Fig. 13.5) and there has been a progressive reduction in confidence intervals.

As a consequence, polarization diminished between 1995 and 2007. The superior group lost some members and reduced its level of SQ in KIBS employment throughout this period (see Fig. 13.3). Figure 13.3 shows that points with the highest SQ values in KIBS employment in 1995 have overall reduced their SQ value over this period. At the same time, regions with a low level of SQ were able to increase their level of specialization during the period of economic expansion, moving from a low level of specialization to a higher specialization profile. A reduction in regional differences has been the result of widening the share of employment in KIBS in relation to total employment in regions with a low SQ value at the beginning. The majority of regions in Cohesion Countries and other Objective 1 regions have increased their employment in KIBS at higher rates in general, thus elevating their SQ (see Map 13.2 registering variation in SQ in 2000–2007). These regions have benefited most from the process of expansion and this may be a result of cohesion policies during the period 1994–2006.

Map 13.2

Variation in specialization quotient in KIBS employment 2000–2007

Enlargements in 2004 and 2007 to New Member States meant that regional disparities in KIBS employment increased for statistical reasons because the SQ values of regions in New Member States were lower.

Capital city regions of New Member Countries benefited from the enlargement, increasing their employment in KIBS at higher rates than other regions. During the period 2004–2007, many regions of New Member Countries also increased their SQ in KIBS.

A tendency towards a reduction in geographical polarization in KIBS employment seems to have been broken during the recent crisis. Taking into account the aforementioned need for caution, if one analyses the specialization profile, one can draw the conclusion that the economic cycle has changed the evolution of the specialization profile in KIBS. Regions of early Cohesion Countries have lost employment in KIBS and have reduced their SQ value in KIBS in general (see Fig. 13.4). Nevertheless, there has been a process of convergence within these regions (except for capital cities) leading to a convergence club during the period 2008–2010.

However, regional differences in KIBS do not seem to have increased because on the other hand, regions of New Member Countries have increased employment in KIBS and SQ values in general. Polish, Romanian and Bulgarian regions benefited most during this period. Thus, the results of the convergence in KIBS employment are not clear in this current period of crisis.

3.3 Regional Disparities Within KIBS

If we now look separately at the categories of KIBS, one can observe important differences. Market KIBS employment has been the main category in KIBS employment (more than two out of three of jobs in KIBS have been created in this activity). Thus, the main regional differences are located in High-Tech KIBS and Financial KIBS. The greatest concentration is found in High-Tech KIBS and Financial KIBS (Fig. 13.6) as the Gini index shows.

Polarization is higher in High-Tech KIBS and Financial KIBS than in Market KIBS. This can be appreciated in the shape of their respective distributions (see Fig. 13.7) and the values of the confidence intervals.⁷ The highest values of specialization are achieved by Financial KIS, where Luxemburg had over four times the mean level of the EU-27 in 2010 and London three times that mean, thus proving to be the most specialized locations in Europe. Maps 13.3, 13.4, and 13.5 represent the levels of specialization in every category of KIBS in 2010.

Map 13.3

Specialization quotient in market KIBS employment in 2010

Map 13.4

Specialization quotient in high tech KIBS employment in 2010

Map 13.5

Specialization quotient in financial KIBS employment in 2010

There are few differences between Maps 13.1 and 13.3. Consequently, the overview for Market KIBS is very similar to KIBS in general.

The differences are most significant in the cases of High Tech KIBS and Financial KIBS. High Tech KIBS are concentrated in capital cities in general. Moreover, employment in this kind of KIBS is particularly relevant in Northern regions (Sweden, Denmark, Holland, Germany, Belgium, Luxembourg and the United Kingdom) in general. In these regions, the level of technology is very high, as some indicators reveal (I + D/PIB, patent applications, etc.). Furthermore, manufacturing firms in these areas may demand a great amount of this kind of KIBS due to the process of services externalization.

Financial KIBS are also located in capital cities. Other regions with a high share of employment in Financial KIS in 2010 are located in Belgium, Luxembourg, Germany and the United Kingdom. Map 13.5 registers the main financial sites of Europe.

Every category of KIBS shows different paths in terms of the evolution of the Gini index, but it is only evident that geographical concentration has decreased in Market KIBS. The results of considering different periods in these categories of KIBS do not show any important changes with respect to those mentioned for KIBS in general.

The development of employment in each category of KIBS shows some important differences. The most dynamic has been Market KIBS, whose annual average rate of growth was higher than 4 % during the period 1995–2007 (EU-15) and positive during the period 2008–2010. On the contrary, the growth of employment in Financial KIS has been very low during the expansion cycle and negative during the current economic crisis.

The spatial allocation of employment in each category of KIBS changed in EU regions during the period 1995–2010. SQ in the period of economic expansion (1995–2007) shows some significant differences among categories of KIBS, mainly between Market KIBS and Financial KIS. In the first case, regional differences tend to clearly reduce due to a process of catching up followed by regions with the lowest level that have augmented their SQ level in general. Therefore, its confidence interval was lower in 2007 than in previous years, and the number of regions included in high and in low categories has diminished as well as their share of employment in KIBS. The evolution in Market KIBS has been very similar to that of Total KIBS. Furthermore, the conclusions mentioned above could also be applied here. Regions in Cohesion Countries have benefited most from the growth in employment in Market KIBS (see Tables 13.1 and 13.2). The annual average rate of growth in Market KIBS employment has been higher than 4.4 % in this area during the period 1995–2007 (UE-15). Employment in Market KIBS increased notably in several regions of Italy (9 out of 21), Germany (8 out of 33), France (6 out of 22) and Spain (7 out of 17) mainly.

Figure 13.8 shows that points with the highest SQ values in Market KIBS employment in 1995 have generally reduced their SQ value over the period 1995–2004. At the same time, points with the lowest SQ values increased their SQ values during that period. Therefore, geographical polarization in Market KIBS employment has reduced during the expansion cycle.

However, regions in Cohesion Countries have lost a great deal of employment during the crisis (the annual average rate of variation: −4 %). Spanish regions and Southern Italian regions have been the main actors of this unstable behaviour.

Regions in New Member Countries have also benefited from expansion in Market KIBS employment in general, mainly before 2007 (enlargement to EU-25 and to UE-27), but also during the period of crisis. Nevertheless, the highest level of generation of net employment in Market KIBS during the crisis has been located in regions of the United Kingdom (14 out of 36), Germany (9 out of 33), France (3 out of 22) and Poland (3 out of 16).

Something similar to Market KIBS has happened to High-Tech KIS, but not so clearly, since the number of regions in high and low categories of SQ has increased as has its confidence interval. The main results of regional evolution in SQ in HT KIS have been that a lot of Southern regions (e.g., Italy, Spain and Portugal) have increased their SQ values between 1995 and 2007. Moreover, some regions of Germany and Holland have experienced the same phenomenon.

Employment in regions of Cohesion Countries has been the most dynamic during the 1995–2007 period, and has performed positively during the recent economic crisis. The main actors have been Spanish regions (located mainly on the Mediterranean Coast and in Madrid), Southern Italian regions and the capital cities of Cohesion Countries and New Member States. These regions started from a very low position. Therefore, it seems to have been a process of catching up stimulated by the assistance of cohesion policy.

Those regions with the highest SQ values in HT KIBS employment at the beginning of the period have generally reduced their SQ value during the period 1995–2007. At the same time, regions with the lowest SQ values have increased their SQ values in that period. Therefore, geographical polarization in HT KIBS employment has decreased in general.

Nevertheless, during the current crisis, the process of reduction in regional disparities seems to have ceased. Old Objective 1 regions from the Cohesion Countries appear to have lost positions in these years in favour of some central regions and New Member Countries regions (Polish regions mainly).

The behaviour of Financial KIS has differed from Market KIBS considerably, showing a polarisation tendency towards the extreme categories throughout 1995–2007, although it has mainly benefitted regions with the lowest levels. Nonetheless, it is important to highlight the fact that this situation may be generated by the use of a less labour-intensive technology in regions with medium profile, which would therefore lead to a lower amount of employment in the category of regional specialization. During this same period, employment in Financial KIS has risen at higher rates in many regions of New Member Countries mainly (e.g., Latvia, Lithuania, Poland and Slovakia), and also in Ireland, Spain and Luxemburg.

This tendency seems to have dramatically changed during the current economic crisis (2008–2010). Taking into account the need for caution mentioned above, regional differences in Financial KIS have clearly increased. The effects of the financial crisis have mainly affected regions at the lowest levels and particularly regions of the Cohesion Countries, which is reflected in their number, their share of employment, and their confidence intervals (see Tables 13.1 and 13.2). The reallocation of Financial employment has been very important for many regions in Belgium, Bulgaria, Estonia, Finland, Holland, Latvia, Portugal, Spain and the United Kingdom towards other regions of New Member Countries (the capital cities of the Czech Republic, Poland, Romania, Slovakia and Slovenia mainly), Luxembourg, some regions in the area around London and in some German regions with high SQ. Therefore, the latter regions have increased their employment in Financial KIS and have benefited most during the crisis.

Figure 13.9 shows that regions of EU-4 (and Southern Italian regions) have generally lost employment in Financial KIBS employment during the current crisis. On the contrary, regions of New Member States have increased employment in this activity in general.

4 Factors Influencing the Location of KIBS: Empirical Investigation

Specifically, this section investigates the potential drivers that may influence the location of KIBS in European regions during the last years. In order to carry out the empirical analysis, we adopted the approach proposed by Miles (2005), according to which the term knowledge-intensive can be interpreted in terms of labour qualification (Miles, 2005). Thus, a knowledge-intensive firm refers to a firm that undertakes complex operations of an intellectual nature where human capital is the dominant factor (Alvesson, 1995).

From an empirical viewpoint, due to the methodological break applied by Eurostat from 2008 onwards in the measurement of KIBS, two periods are analysed separately. On the one hand, we conducted an analysis for the period 2000–2007 (i.e. economic expansion) and, on the other hand, we explored the period 2008–2010 (i.e. beginning of the current economic crisis).

The previous literature dealing with the main variables influencing the location of KIBS points to several variables related to accessibility to clients and to knowledge and information and agglomeration economies. Therefore, this section serves to build a model that helps to explain the SQ in total KIBS (dependent variable) in relation to other variables, such as: capital city (CC), employment in total manufacturing (ETM), employment in high-tech manufacturing (EHTM), human resources in science and technology (HRST), GDP/km² (density of production, labelled DP), patent applications (PA), and accessibility (A), in each temporal moment t for each region I, according to the following specification:

$$ S{Q_{it }}={\alpha_i}+{\beta_1}C{C_{it }}+{\beta_2}ET{M_{it }}+{\beta_3}EHT{M_{it }}+{\beta_4}HRS{T_{it }}+{\beta_5}D{P_{it }}+{\beta_6}P{A_{it }}+{\beta_7}{A_{it }}+{v_i}+{\varepsilon_{it }} $$

where $i=1,\ldots,N$ are the regions under study (N = 204), $t=1,\ldots,L$ is the temporal length under analysis, ${v_i}$ is the random component, and ${\varepsilon_{it }}$ is the residual of the model.

In relation to the period 2000–2007, Table 13.3 (see Annex) reports the results obtained. We can observe that capital city status, employment in high-tech manufacturing, availability of human resources in science and technology, wealth concentration (measured by the variable GDP/km²), patent applications and accessibility positively and significantly influence specialization in KIBS at a regional level in Europe. On the contrary, employment in total manufacturing exerts a negative influence on such specialization.

The second period under study covers the years 2008–2010. In this case, Table 13.4 (see Annex) includes the information. There are three main differences in comparison to the other period. Firstly, wealth concentration is not a relevant driver of regional specialization during the period 2008–2010, whilst in the previous years this variable played a relevant role in explaining the levels of regional specialization. Secondly, the variable “capital city” has a positive and stronger influence than in the previous years, which represents the polarisation of KIBS from peripheral areas towards more traditional regions (i.e. capital cities) from 2008–2010. Thirdly, the negative influence of the variable “employment” on total manufacturing is considerably stronger during this second period under analysis.

5 Conclusions

KIBS constitute a relevant source of employment, production, investment, and knowledge dissemination, especially for industrialised areas. Thus, they may have a relevant role in the spatial allocation of economic activities and regional development. Whilst previous studies have shown that KIBS were not equally distributed across European regions, is this description still valid nowadays? This chapter seeks to explore whether the patterns of regional concentration and specialization in KIBS have changed during the last 15 years. In order to achieve this aim, this study has investigated the evolution of both geographical distribution as well as regional specialization in KIBS (i.e., Total KIBS, High-Tech KIBS, Market KIBS, and Financial KIBS) in European regions during the last years. It is important to note that the period under study was not randomly chosen, as it involves both the enlargement of the EU undertaken in 2004 and in 2007 as well as the beginning of the current economic crisis.

The data analysed reveal that in 2010 KIBS was the most geographically concentrated activity. Within the context of KIBS, the greatest geographical concentration has been found in High-Tech KIS and Financial KIS.

The second topic addressed in this study is the regional specialization of European regions. By calculating the specialization quotient, European regions are classified into different profiles of regional specialization. There are significant differences among the groups of regions. Those regions with the highest values of SQ concentrated KIBS employment. Thus, KIBS were concentrated in capital city regions, Central and Northern regions of EU-27. The share of KIBS employment as compared to total employment in those areas was very high due to the agglomeration of economic activity and to a greater accessibility to markets and knowledge resources in these regions. On the contrary, Objective 1 regions during the programming period 2000–2006, and regions of New Member Countries (except capital cities regions) have obtained the lowest levels of SQ values in KIBS.

Nevertheless, our results concerning the evolution of spatial concentration, during the period 1995–2007, show a spatial diffusion process in KIBS location. Traditional regions where KIBS were agglomerated have lost participation in favour of peripheral regions as well as regions where KIBS were not very important in the past (e.g., in France, Germany, Sweden and the United Kingdom). Therefore, it seems that there has been a process of catching up and geographical diffusion during the economic expansion period, which reflects some of the effects of cohesion policy. Nonetheless, this process of spatial dispersion may have shifted during 2008–2010 due to the economic crisis, which has affected regions in Cohesion Countries in a more intense way.

Many regions in Cohesion Countries managed to increase employment in general and KIBS employment in particular during the period prior to the current crisis, thereby approaching the levels of regions in developed countries. However, through the period 2008–2010 many of them lost employment and have also lagged behind in the catching up process. On the contrary, regions in New Member Countries have generally made progress during the two periods.

Moreover, there has been a catching up process and a reduction in regional disparities in KIBS employment, but with different actors in each stage. At the same time, there has been a spatial reallocation of KIBS employment from the traditional sites towards Southern regions in the first stage and later towards Eastern regions. Within the regions that have most increased their position in the ranking of level in KIBS employment one can find the capital cities of Cohesion Countries (EU-4) and New Member Countries and a few other regions.

Reduction of regional disparities in KIBS has not been observed for Financial KIBS, which seem to have experienced a process of geographical polarisation. However, employment in Financial KIBS has risen at higher rates in many regions in New Member States, Ireland and Portugal.

Furthermore, our exploration of factors influencing location of KIBS confirms the main conclusions of previous relevant literature. The results show that the variables representing accessibility to clients and resources of knowledge and economies of agglomeration, such as patent applications, human resources, transport accessibility and density of GDP serve to explain the regional distribution in KIBS of European regions.

Finally, we conducted an empirical analysis in order to address the factors that may help explain the location of KIBS across European regions for the periods 2000–2007 and 2008–2010 separately. The data show that capital city status, employment in high-tech manufacturing, availability of human resources in science and technology, patent applications and accessibility positively and significantly influence the specialization in KIBS at a regional level in Europe. On the contrary, employment in total manufacturing exerts a negative influence on such specialization. The main difference between both periods is that, whilst the density of production (measured by the variable GDP/km²) exerts a positive and statistically significant influence during the period of economic expansion, this influence does not hold for the period of current economic crisis (i.e. 2008–2010).

In summary, employment in KIBS is concentrated geographically in EU regions in capital cities, Central and Northern regions due to the agglomeration of economic activity and to the greater accessibility to markets and knowledge resources in these regions. However, a process of spatial dispersion has occurred during the economic expansion period (1995–2007), coinciding with the process of enlargement (2004) and with cohesion policy (1994–1999 and 2000–2006). Areas where employment in KIBS has increased at higher rates have been regions in Cohesion Countries (EU-4), and Objective 1 in 1995–2007 and in New Member Countries in 2004–2007. This process seems to have been decelerated during the economic crisis, because KIBS employment has reduced in regions of Cohesion Countries and have increased in Mew Member Countries regions. Thus, while the application of cohesion policy for the period 2007–2013 is expected to benefit the latter regions, the data for 2008–2010 point to such a conclusion.

Respect to policy implications, in a context characterized by the global competition in the KIBS sectors, policy makers are seeking to build competitive advantage for their nations or regions. In this sense, nations that participate in the production and delivery of knowledge intensive business services should recognize certain strategic issues that may give them a sustainable competitive advantage. These issues involve policy implications.

First, investment in infrastructures and ICTs. KIBS count on suppliers who can deliver services with seamless efficiency. Physical infrastructures may be less critical for certain types of knowledge services (e.g., medical transcriptions), but they are vital for the delivery of other services that require regular face-to-face interactions (e.g., medical services; educational programs). Furthermore, cross-border connectivity is clearly facilitated and mediated by ICT through teleconferencing, e-mail, video-conferencing and virtual networks (Jones, 2005). ICTs have led to often rapid and unexpected changes in competitive positions of firms and countries (Kautonen et al., 2009). Therefore, focused investment on infrastructures and ICTs must be warranted so that business can be conducted (Daniels, 2004).

Table 13.1 Share in employment by level of specialization quotient (%)

Table 13.2 Number of regions by level of specialization quotient

Second, the strategic role of small and medium-sized enterprises (SME) (Bryson & Rusten, 2005). SMEs, constituting as much as 90 % of enterprises in many countries around the globe, have been identified as the driving force behind innovations and entrepreneurial investments, job creation, international trade, and new product and service developments. However, SMEs are usually confronted with enormous challenges. SMEs are usually more vulnerable to managerial, financial and technological challenges as compared to bigger companies. Thus, it is imperative that nations pay special attention to supporting SMEs.

Third, research and development (R&D). This variable represents knowledge services at their most intense level. While there have been huge improvements in R&D investments, enhancing greater university-industry collaborations should be promoted.

6 Methodological Notes

• Source of data: Eurostat and Espon.

• Territorial units: We have considered regions at NUTS 2 level. It is important to note that there are lacks of data for these regions at some years. Due to this situation, we have eliminated in the analyses those regions with missing data. However, the final sample provides more than 90% of representativity of the total population.

• Time series: There is a methodological break since 2008. However, the descriptive analyses have considered four periods from a transversal perspective, and we have taken into account establishing comparisons among categories of regional specialization according to the specialization quotient rather than comparing among absolute numbers, which could have led to problems of comparability among data.

• Sectors: We have taken knowledge-intensive sectors, and mainly with private. Other public knowledge-intensive sectors have not been included into the analyses explicitly.

• Classification of KIBS: Despite the intense controversy about the classifications of KIBS, we have used the traditional classification to avoid the presence of breaks in the available data series.

Annex of Tables

Table 13.3 Estimates^a,b for the drivers of regional specialization (2000–2007)

Table 13.4 Estimates^a,b for the drivers of regional specialization (2008–2010)