Business services as actors of regional innovation: a spatial approach. I
Mercedes RODRIGUEZi, José A. CAMACHOi and Jorge CHICAii Department of International and Spanish Economics and Institute of Regional Development ii Department of Quantitative Methods for Economics and Business University of Granada E-mail:
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Abstract The objective of this paper is to carry out an analysis of the role played by business services in regional innovation by adopting a novel perspective, namely, spatial econometrics. In particular we start from the so-called modification of Griliches knowledge production function (KPF) carried out by Jaffe in 1989. In the first section we review the evolution in the analysis of business services, paying special attention to the problems in defining these activities. The recent literature on geographical concentration and regional clustering is revised in the second section, pointing out the ascending participation of the new economic geography and more concretely of the knowledge production function approach. Next we comment on the functions developed by knowledge-intensive business services (KIBS) in regional innovation. In the third section we examine the spatial distribution of innovation activity in 83 regions from 10 European countries: Austria, Belgium, the Czech Republic, Italy, Poland, Portugal, Slovenia, Slovakia, Spain and the United Kingdom. In contrast to most of previous studies that use patent applications our proxy is product and process innovation as measured in the Fourth Community Innovation Survey (CIS 2004) that is, the percentage of firms introducing product or process innovation in each region over the period 2002-2004. The “traditional” KPF relates innovation activity in one region to R&D inputs in the same region. We modify this simple specification by including, among other factors, the presence of business services in neighbouring regions. Our extended model examines two aspects. Firstly, the spatial dependence at the level of innovation performance or, in other words, the fact that the innovations produced in one region might spillover and contribute to innovation activity of firms in neighbouring regions. Secondly, the fact that, among other factors, the availability of business services helps to explain why some regions are more innovative than others. The empirical results obtained confirmed both the existence of knowledge spatial spillovers the relevance of business services as regional innovation drivers. Keywords: business services, spillovers, knowledge production function.
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1. Introduction. Over the last decades a radical change in the analysis of business services has taken place. The recognition of their key role as drivers of production, employment and growth has resulted in the publication of an increasing number of studies that emphasise the knowledge diffuser role of business services, and more concretely of those business services classified as knowledge-intensive (KIBS). Firms are the key protagonists of the innovation process in all its dimensions: they transform basic research into applied research, create new products and services and, in short, carry out the bulk of innovative activities. Given this fact, the special relationship that links KIBS with their client firms and their increasing knowledge and innovation intensity have combined within the current knowledge-demanding context to make innovation performance almost co-dependent on the quality of the business provided. In addition to their “own” innovative activity, “the most important feature of KIBS innovation is that is typically linked to satisfying client-specific needs” (Boden and Miles, 2000, p. 17). Thus, in their exhaustive revision of the study of KIBS, Muller and Doloreux (2007) differentiate between the knowledge dimension and the innovation dimension. In the case of innovation, they conclude that a shift in the vision of KIBS has taken place from supporting entities aimed at introducing new technologies to truly agents for developing innovations. This conclusion is in accordance with the progressive evolution from technological innovation, described in the paper by Barras (1986), to non-technological innovation or knowledge creation. Thus, knowledge creation and diffusion becomes the major distinctive feature of the innovation activity carried out by KIBS. From a statistical point of view, the recent revision of the International Standard Industrial Classification of All Economic Activities (ISIC Rev. 4) allows a more detailed classification of KIBS than the previous classifications. Drawing on the identification established by Nählinder (2002) using the ISIC rev. 3.1., eight different divisions containing a total of eighteen KIBS classes can be identified (Table 1). A distinction is established between technologically-based KIBS (t-KIBS), which comprise three out of the eight divisions: 62 computer and programming, consultancy and related activities; 71 architectural and engineering activities, technical testing and analysis and 72 scientific research and development; and traditional professional KIBS (p-KIBS), which contain the rest of divisions: 69 legal and accounting services; 70 activities of head offices, management consultancy activities; 73 advertising and market research; 74 other professional, scientific and technical activities and 78 employment activities. In this paper we try to employ a definition of business services as similar as possible to this detailed classification of KIBS, but given the scarce level of detail of the statistical information currently available, we centre on three industries of the NACE rev 1.1.: 72 computer and related activities, 73 research and development and 74 other business activities. As some other activities (not only KIBS) are included in this classification we refer in our analysis to the role of business services and not the impact of KIBS. We mentioned above that KIBS are innovative in their own right, but unlike the majority of highly innovative manufacturing activities, they also facilitate innovation in other industries. Antonelli (2000, p.171) describes the way they operate as follows: they function as “holders of proprietary quasi-generic knowledge from interactions with customers and the scientific community, and operate as an interface between such knowledge and its tacit counterpart, located within the daily practices of the firm”. That is to say, they act as bridges for knowledge (Czarnitzki and Spielkamp 2000) or as Den Hertog and Bilderbeek (1998) put it, as a second knowledge infrastructure, even substituting for functions traditionally attributed to the public sector. Specifically, KIBS have three main ways of contributing to the knowledge base (Kox 2002): developing original innovations (technological and non-technological), diffusing knowledge (combining their own knowledge with the client firm’s knowledge) and surpassing the
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problem of human capital indivisibilities (that is to say, facilitating the access to specialised knowledge to small firms). A highly educated workforce, in combination with strong efforts in innovation (not only R&D expenditures, but also training and acquisition of new technologies), allows these service industries to improve their client’s knowledge bases, and at the same time, their own knowledge bases. Moreover, as a consequence of the inherent co-production that takes place in their provision, KIBS act as “bridges” for innovation and knowledge in their client firms, and in general as key agents within the innovation systems. This co-production needs, in most of the cases, of physical (face to face) contact. As a consequence space becomes a key variable. Table 1. KIBS industries in ISIC Rev.4. Industry 62 Computer programming, consultancy and related activities 6201 Computer programming activities 6202 Computer consultancy and computer facilities management activities 6209 Other information technology and computer service activities 69 Legal and accounting activities 6910 Legal activities 6920 Accounting, bookkeeping and auditing activities; tax consultancy 70 Activities of head offices; management consultancy activities 7010 Activities of head offices 7020 Management consultancy activities 71 Architectural and engineering activities; technical testing and analysis 7110 Architectural and engineering activities and related technical consultancy 7120 Technical testing and analysis 72 Scientific research and development 7210 Research and experimental development on natural sciences and engineering 7220 Research and experimental development on social sciences and humanities 73 Advertising and market research 7310 Advertising 7320 Market research and public opinion polling 74 Other professional, scientific and technical activities 7410 Specialized design activities 7420 Photographic activities 7490 Other professional, scientific and technical activities n.e.c. 78 Employment activities 7810 Activities of employment placement agencies 7820 Temporary employment agency activities 7830 Other human resources provision Source: Own elaboration.
Type of KIBS t-KIBS t-KIBS t-KIBS p-KIBS p-KIBS p-KIBS p-KIBS t-KIBS t-KIBS t-KIBS t-KIBS p-KIBS p-KIBS p-KIBS non-KIBS p-KIBS p-KIBS p-KIBS p-KIBS
The objective of this paper is to focus on this aspect by analysing the impact of the geographical availability of business services on regional innovation performance. In the following section we review the evolution of those theories explaining the concentration (clustering and agglomeration) of activities. We highlight that in the distribution of innovation activity the new economic geography is one of the most flourishing lines of analysis. The key role played by business services is also depicted, starting from the pioneering work developed by Strambach more than a decade ago. In the empirical part of the paper we analyse the spatial distribution of both innovation activity and business services in our European regions under analysis (83 from 10 countries). Spatial dependence seems to be a common feature in both cases, so we estimate a modified knowledge production function (KPF) which among other factors takes into consideration both the existence of knowledge spillovers and the availability of business services. Finally some conclusions are extracted from the econometric analysis.
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2. Innovation, space and business services: some remarks. 2.1. Innovation and space: the knowledge production function. The relevance of spatial distribution in the analysis of how most variables evolve (production, employment, etc.) is widely-acknowledged. As a result, we can speak of the existence of clusters or agglomeration economies in many fields. The objective of this sub-section is to carry out a brief review of the origins and the evolution of the theories on geographical concentration and regional clustering, from traditional location theories to the most recent ones, among which we find the KPF approach. The origins of the interest on the location on productive activities is found in Von Thünen’s (1826) pioneering work on the location of food producers around markets: because of the trade-off between the profit obtained and the distance-related costs food producers located around the markets in order to maximise profits. Marshall (1890, 1919) elaborated the pillars of the main theories on the concentration of innovation (Becattini, 2002) like the industrial districts (Becattini, 1979), the cluster approach of Porter (1990) or the new economic geography of Krugman (1991a). The central idea of Marshall, more complex than traditional location theories, was based on the emergence of benefits coming, not from the proximity to consumers or markets, but from the co-location of firms. Starting from Marshall’s work, Krugman described the existence of three types of externalities (Krugman, 1991a): • Economies of specialisation: the presence of a high number of firms is reflected in the outsourcing of complementary activities and into closer cooperation. Firms obtain benefits by sharing resources and competences. These benefits are particular noticeable when they share innovation costs. • Economies of labour pooling: the availability of a high qualified labour force not only attracts more specialised labour and more firms but also generates two main types of advantages: o A high concentration of firms allows a higher mobility of labour depending on demand fluctuations. More concretely, the concentration of a great number of employers reduces the risk of unemployment which translates into lower wages (workers will accept lower wages due to the higher stability in their income). o Moreover, employees are more likely to invest time in training because many potential employers will value their efforts. • Technological externalities or knowledge spillovers: the concentration of firms facilitates the emergence of knowledge spillovers, because knowledge flows more easily locally than over long distances, especially tacit knowledge. Most of theoretical works centre on this latter type of externality. Following a chronological order we can differentiate several approaches. Firstly, we find the Italian and French theories on the industrial districts and the milieu innovateur, respectively. Secondly, in the nineties, the works by Porter and the New Economic Geography appeared. Finally, we can mention new contributions known as the new industrial spaces theories. In the case of the industrial districts theory, it appeared in the end of the seventies, when the success of some Italian cities and regions captured the attention of scholars like Becattini (1979). The industrial district is defined as a cluster or agglomeration of firms with a peculiar relationship and interaction among them. More concretely, following Brusco (1990) this relationship is the result of a balance between cooperation and competition: whereas competition takes place among firms that produce the same product or develop the same activity, cooperation, on the contrary, occurs among firms in different stages of the vertical product chain. These interactions are part of what is called “common cultural background” (Becattini, 1979), that is to say, not only interactions among firms are important, but also the existence of adequate institutional and market conditions. In this sense, the institutional environment, in combination with “informal” relationships, are key elements for firms´ success. Taking a similar 4
perspective, the French group GREMI elaborated in the eighties the milieu innovateur approach (Aydalot, 1986; Camagni, 1991; Ratti, 1992). This theory also highlights the relevance of the relationships among firms and especially between firms and their environment. The firm is analysed not as an isolated unit but as part of a mileu with a common innovative capacity. In late eighties, the Porter’s cluster approach emerges. After the publication of several studies on the national competitiveness of various industrialised countries, Porter published his famous book “The Comparative Advantage of Nations” in 1990. Although in this book he used the concept cluster, the geographical dimension will be introduced in 1998 in “On Competition”. In this book he affirms that competitive advantages are closely related to geography and more concretely to the institutions and the knowledge spillovers described by Marshall. As has been mentioned, Krugman will be the main architect of the New Economic Geography approach during the nineties (Krugman, 1991a, 1991b, 1998a, 1998b, 2000; Fujita et al., 1999). This theory was the result of the combination of the new international trade theory, developed in the eighties, whose main novelty was the incorporation of aspects like increasing returns or imperfect competition, and the traditional economic geography. Its main objective was to model agglomeration by simultaneously combining centripetal and centrifugal forces. That is, building on the core-periphery model, it explained how the interaction between increasing returns and transportation costs could lead to a particular geographical production structure. Currently, one of the main lines of analysis is the concentration of specific industries not included in the core-periphery model (Krugman and Venables, 1995; Venables, 1996). It is important to note that, in difference with Porter, Krugman (1991a) affirmed that although knowledge spillovers can be important in some activities, like hightechnology industries, they are not a key factor for explaining agglomeration. More recent works, known as the “new industrial spaces” (Storper, 1995; Storper and Scott, 1988, 2003) combine, in accordance with Moulaert and Sekia (2003), ideas from different theories: the industrial districts (Becattini, 1979), the flexible production systems (Piore and Sabel, 1984), the social regulation (Boyer, 1990) or the transaction costs (Williamson, 1975, 1985). The central point is that the interactions among firms, along with political, economic and cultural practices, are integrated within the social and institutional environment and determine the success (or failure) of regions. We can affirm, therefore, that the integration of economic geography in “mainstream economics” is quite recent, especially in the innovation domain. Nowadays we can distinguish three main approaches in regional innovation: the geography of innovation, the regional innovation systems and the learning regions. The geography of innovation embraces a group of works aimed at measuring knowledge spillovers starting from the knowledge production function (KPF) introduced by Griliches (1979) and modified by Jaffe (1989). Originally, Jaffe analysed the externalities produced by universities. Later on, some works have tried to examine the process of creation and diffusion of knowledge at the regional level using econometric models, assuming that as Fritsch (2002) states, the knowledge production function is a useful instrument to compare the quality of the regional innovation systems. Following Griliches (1979), innovation depends on the innovation efforts carried out in the region in combination with the characteristics of the region. As many aspects referred to the characteristics of the region cannot be measured (for example the innovation culture) they have to be incorporated using proxy variables. In the KPF the most commonly output indicator employed is patents, and more concretely patents applications. Among the main advantages attributed to patents are that they guarantee a minimum level of originality and that they are intimately linked to invention. The main problems of this indicator is that it does not take into account informal innovation activity and that, given the fact patent application is a time-consuming and costly process many innovation activities carried out by small and medium size firms are not patented but protected through other methods like secrecy. In this sense the European Patent Office (EPO)
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estimates that only 50 percent of innovations are patented (EPO, 1994). This is why in our analysis we employ the percentage of innovating firms as a proxy for regional innovation. 2.2. The role of business services in regional innovation: some insights. As was pointed out in the introduction, services have traditionally been ignored in innovation studies, given their assumed “non-innovative nature”. The absence of adequate statistics, unable to report the major part of services expenditures on innovation (that is, training and acquisition of new technologies and knowledge), in addition to the scarce use of patents, have caused services to be characterised as a sector with low innovation efforts and few innovation results. It was not until the second half of the nineties that the first in-depth studies on the potential innovative role of services appeared. These studies pointed out the important impact of services in innovation not only at the country or firm level, but especially at the regional level. Why to choose the regional level? If we accept the arguments exposed in recent theories on regional innovation (regional innovation systems and learning regions), knowledge, and in particular tacit knowledge, flows adequately at short distances. Moreover, user-provider interactions in services are carried at the local level (Wood, 2002). This supports the choice of the region as the main scenario for the analysis of the impact of services on innovation. In this sense Strambach, in her pioneering paper on the role of services on regional innovation performance (Strambach, 1998), employs the learning regions theory to describe the two major types of effects (direct and indirect) that KIBS carry out in innovation. The direct effects refer to the development of own innovations (product, process or organisational). Nevertheless, the effects specific to KIBS are the indirect ones, which are divided into four types: • Knowledge transfer. KIBS diffuse knowledge in the form of expert technological knowledge and management know-how. As a result of the increase in the amount of information and knowledge and the vertical disintegration in firms, KIBS are closely linked, not only to knowledge diffusion, but more generally to the modernisation and rationalisation of production, management and sale methods. • Integration of different stocks of knowledge and competences. The problems associated with innovation processes require in many occasions the combination of knowledge of different functional areas. This explains why formal and informal networks and cooperation play a key role in KIBS performance: because they are able to integrate very different types of specialised knowledge. • Adaptation of existing knowledge to the specific needs of their clients. KIBS maintain long-term relationships with their clients which allow them to acquire both tacit and explicit knowledge about their client firms. This knowledge is used to adapt solutions for innovating problems to the specific structure and culture of client firms. • Production of new knowledge. During the development of their activity KIBS collect, reorganise and create new knowledge, especially of a tacit type. Taking arguments from evolutionary and institutional theories, Simmie and Strambach (2006) justify how KIBS are at the heart of interactive learning processes. In particular, they point out that concentration of KIBS in metropolitan regions offers important advantages in terms of knowledge diffusion and the generation of knowledge spillovers. Nevertheless, in spite of the considerable importance of these functions, there are very few empirical studies about the role of KIBS in regional innovation performance. We can highlight, because of its pioneering nature, the one developed by the KISINN network (Knowledge-Intensive Services and Innovation) during the years 1995 and 1996. Research centres from nine European countries participated in this project: Belgium, France, Germany, Greece, Italy, the Netherlands, Spain, Portugal and the United Kingdom. Its conclusions, although tentative because of the scarce availability 6
of statistics, emphasised the increasing relevance of KIBS at the regional level as facilitators, carriers and sources of innovation, as well as the growing demand for these services (Wood, 2001). The existence of a certain “north-south” location pattern was also stressed: whereas in northern countries the distribution of KIBS was strong, varied and flexible, in southern countries there was a high concentration of these services, as a result of the dominant influence of multinational investors, transnational companies and the government. This supports the existence of a potential relationship between poor innovation regional performance and scarce presence of KIBS, which would call for the action of the public sector. In this sense Cooke (2001) takes a step further and highlight the need for public policies aimed at solving this “gap” or “market failure” in the provision of KIBS in order to contribute to the maturation of the regional innovation system. Along with the work carried out by this network, we can cite some empirical studies on the role of those KIBS provided to business (KIBS) in regional innovation. These can be classified into three groups, depending of their main interest. In the first group we find those works aimed at relating regional innovation performance and use of KIBS. This is the case of the works by Makun and MacPherson (1997) for electrical equipment industry in the three main regions of New York, Muller and Zenker (2001) for five regions in France and Germany or Aslesen and Isaksen for Oslo (2007). The paper by Makun and MacPherson (1997) shows how innovation rates are significantly higher in those regions with a high supply of advanced production services. They affirm that despite technological advances like the Internet help to cut off deficiencies in peripheral regions, in most of the cases interregional trade of advanced services is impossible to develop because of the need for face to face contact to adequately transmit knowledge. In this line, Muller and Zenker (2001) conclude that knowledge intensive services are not only innovators but also contribute to innovation in other firms. In particular, those SMSEs that use KIBS tend to spend more on R&D and have closer relationships with universities and research centres. In other words, KIBS create a “virtuous circle” in which they learn from their clients, codify this knowledge and act as bridges between the generic knowledge and the specific needs of the firms. The analysis of the sectors of software and consultancy in Oslo carried out by Aslesen and Isaksen (2007) reveals that they act as a “motor of competence” and stimulate innovation. A second group of works centre on the analysis of the cooperation patterns of KIBS firms, underlying the importance of location. Examples are the papers by Koschatzky (1999) for thirteen German regions, Drejer and Vinding (2005) for five Danish urban areas, and Doloreux and Mattson (2008) for the Ottawa region. Koschatzky (1999), after applying probit models to data from a German regional innovation survey, concluded that horizontal networks of service firms located in central regions are characterised by interregional cooperation, which could help to improve interregional innovation. Drejer and Vinding (2005) defend the hypothesis that geographical proximity influences on collaboration. By controlling for size, industrial affiliation and collaboration patterns, they found that those firms located in great urban areas have almost the double probability of collaborating with KIBS firms than those firms located in peripheral areas. As for the Ottawa region, Doloreux and Mattson (2008) point out the need for local proximity given the greater propensity to collaborate with local partners shown by KIBS. Finally, Koch and Stahlecker (2006) and Andersson and Hellerstedt (2009) adopt a different perspective: instead of analysing how KIBS affect regional innovation they study how regional characteristics affect the foundation of KIBS firms. In their study of Bremen, Munich and Stuttgart, Koch and Stahlecker (2006) find that in early stages, geographical proximity to suppliers and clients play a key role in KIBS development. Andersson and Hellerstedt (2009), using data from Swedish municipalities, show that the qualification of the workforce and the size of the regional market have a positive influence on the development of KIBS firms.
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3. Business services and regional innovation performance: a spatial approach. In the preceding section we have shown how there are strong theoretical arguments that support a positive contribution of KIBS to regional innovation performance. Nevertheless, what seems to be “clear” from a theoretical viewpoint is more difficult to empirically evaluate because of the absence of statistics with an adequate level of detail. As an example we can mention the definition of knowledge-intensive services employed by Eurostat which comprises the NACE rev 1.1. codes 61, 62, 64-67, 70-74, 80, 85, 92, that is, water transport; air transport; post and telecommunications; financial intermediation; real estate, renting and business activities; education; health and social work and recreational, cultural and sporting activities. We pointed out in the first section that we try to employ an accurate definition of business services (mainly including KIBS) so we take three industries of the NACE rev 1.1.: 72 computer and related activities, 73 research and development and 74 other business activities. We analyse 83 European regions from ten countries: Austria, Belgium, the Czech Republic, Italy, Poland, Portugal, Slovenia, Slovakia, Spain and the United Kingdom. The selection of countries and regions is conditioned by the availability of statistical information. Our proxy for innovation activity is the percentage of product and process innovating firms over the period 2002-2004 as defined in the Fourth Community Innovation Survey (CIS-4)1. In comparison with patent applications, this indicator better captures the innovation activity carried out by small and medium size firms. Moreover, as recent analyses have demonstrated (Buesa et al. 2010) the presence of innovating firms in a region is one of the most important explanatory factors for patenting activity. In addition to business services, other variables are taken into consideration when explaining the spatial distribution of innovation, namely, R&D expenditures, and the participation of the manufacturing sector in employment and the population density. In all the cases each variable is an average of four years data (2000-2004) to take into account temporary effects and approximate long-run values. 3.1. Global spatial analysis. As a previous step before estimating our modified knowledge production function (KPF) we carry out a descriptive spatial analysis of our key variables of interest: innovation and business services. For so doing we employ both quantile maps, which give us a clear picture of how homogeneous/heterogeneous the distribution at the regional level is, and a statistic of spatial association. The Map 1 shows us the regional distribution of firms’ innovation across our regions under analysis. We can observe a strong innovation activity in countries like the UK or Austria or some areas like the north of Italy, whereas in Eastern countries (Poland or Slovakia) innovation levels are more modest. If we look at business services (Map 2), the so-called trend of business services to locate in capital regions is confirmed: London, Brussels, Madrid, Île de France, Praha and South East are all included in the first group. On the other side we find regions in Poland, Slovakia and Portugal, with very low participations of business services. Some coincidences are observed in the leading regions in terms of innovation and presence of business services, the clearest example being the UK. Therefore, we can affirm that spatial distribution is far from homogeneous and that there is higher variation in the distribution of business services that in the distribution of innovation in terms of coefficients of variation. In order to better characterise the spatial distribution of these two variables we calculate a statistic of global autocorrelation: the Moran’s I. The presence of spatial autocorrelation means that the presence of high 1
A product innovation is defined as the introduction of a new good or service or significantly improved good or service with respect to its capabilities, such as improved software, user friendliness, components or sub-systems. A process innovation is defined as the implementation of a new or significantly improved production process, distribution method or support activity for goods or services. The innovations must be new to the surveyed firm but they do not need to be new to the sector or market. It does not matter if the innovations were originally developed by the surveyed firm or by other firms.
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innovation levels (or business services) in one region is not only explained by other variables, but also by the location innovating firms or business services in neighbouring regions. Map 1. Regional distribution of innovation, 2002-04.
Percentage of innovating firms 20,00 - 34,00 34,01 - 47,00 47,01 - 62,00 62,01 - 78,00 78,01 - 100,00
Source: Eurostat.
Map 2. Regional distribution of business services, 2000-04. Participation in employment 1,96 - 4,51 4,52 - 6,83 6,84 - 9,93 9,94 - 18,57 18,58 - 29,93
Source: Eurostat.
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The Moran’s I (Moran, 1948) defines the similarity as the cross-product of the differences between individual values and the mean of the values under study that is to say:
cij = ( xi − x )( x j − x ) where xi is the value of a variable for region variable under study.
i
[1]
and x is the mean of the values of the
The Moran’s I is constructed as: N
∑ w ( x − x )( x N ij
I=
i
j
− x)
ij
S0
N
∑ (x − x )
2
where
S0 = ∑∑ wij i
[2]
j
i
ij
To test the significance of the statistics we compare the theoretical distribution and the empirical distribution. If the standardised value is positive and significant, this indicates the existence of positive autocorrelation. In our analysis we will use two types of matrices: contiguity and inverse distance matrices. In a binary contiguity matrix wij=1 if regions i and j share a border and 0 otherwise. In the inverse distance matrices, weights are defined as the inverse distance and the inverse squared distance between the centroids of regions i and j . Table 2. Moran’s I for innovation and business services. Innovation Business services Weight Matrix Z-value Prob. Z-Value Prob. Wbin 7.097 0.000 3.050 0.000 Wdis 7.035 0.000 3.028 0.000 Wdis2 6.688 0.000 2.894 0.000 Source: Own elaboration.
The Moran index (Table 2) confirms the existence of a strong positive autocorrelation process in the cases of both innovation and business services. We have corroborated the existence of some similarities between the spatial distribution of innovating firms and the concentration of business services as well as the presence of positive spatial autocorrelation at the regional level. The aim now is twofold: firstly, to evaluate the presence of knowledge spillovers and, secondly, to evaluate the role played by business services in regional innovation performance. In order to examine these two aspects we start from the theoretical framework of the knowledge production function (KPF). 3.2. Innovation spillovers and the role of business services. In its more basic form, the KPF relates innovation to R&D expenditures. Starting from this, we specify the following function:
Ii = Riδ1 Z1δi2 ei
[3]
Where I is the proxy for innovation, R represents R&D efforts, Z1 is a vector of variables for institutional and economic factors and e is a random independent and identically distributed error term which captures unobservable elements.
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Firstly we modify this general specification by introducing the existence of positive spatial autocorrelation in innovation, or, in other words, by recognising that innovation (and more in general knowledge) can spillover among regions ( Z 2 ). δ
I i = Riδ1 Z1δi2 Z 2i3 ei
[4]
We suppose that the innovation activity carried out by firms ( I ) is influenced by the R&D efforts ( R ) in combination with some internal factors (population density, presence of manufacturing) ( Z1 ) and with external factors (innovation activity carried out in neighbouring regions) ( Z 2 ). By taking logarithms we obtain the following equation: 10
ln I i = β1 ln Ri + β 2 ln Di + β3 ln M i + β 4Wi ln Ii + ∑ δ c Nic + ε i
[5]
i =1
where β1 , β 2 and β3 are the elasticities of the increase in innovating firms due to changes in the respective variables. The endogenous variable I is proxied by the percentage of firms developing product and process innovations over the period 20022004. In the case of the explanatory variables, R&D efforts, R , are proxied by R&D expenditures in percentage of GPD. The internal factors are the density of population, D , and the participation of manufacturing in employment, M . As established in the literature, these variables capture agglomeration economies (Ciccone, 2002)2. The variable W ln I is the spatial lag for innovation activity, that is, a weighted measure of the innovation activity in those regions with which region i has contacts. These contacts are reported in the weight matrix W which defines linkages across regions. As was indicated before, this variable tries to capture the interregional knowledge spillovers. A set of national dummies is included so as to capture national differences in institutional and social terms. Thus, equation 5 provides a general framework for estimating the existence of knowledge spillovers at the regional level. In the second section we described how business services, and more concretely KIBS, are at the heart of innovation process both in theoretical and in empirical terms. Our objective now is to evaluate the importance of the activities carried out by our group of business services. Most empirical studies on KIBS conclude that proximity is important because of the need for face to face contacts to adequately transmit tacit knowledge. In our global spatial analysis the Moran’s I has confirmed that, not only the availability of business services in a specific region is important, but also the presence of business services in neighbouring regions. Therefore, before modifying equation 5 so as to include business services, we evaluate whether business services or spatially lagged business services is a more adequate indicator. For so doing we carry out simple regressions between innovation and both business services and spatially lagged business services. The results are shown in Table 3. As can be noticed, both indicators are closely related with our proxy for regional innovation, being the coefficients significant. Nevertheless, the spatially lagged indicator is more intimately related to innovation as the higher values for the coefficient and the R2-adjusted shown. In short, it seems that the availability of business services has to be understood not only in terms of location in the same region but in a wider
2
Other indicators employed with the same purpose in other papers are high-tech employment or GPD per capital.
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spatial perspective: the presence of business services in neighbouring regions permits an easy access to the provision of this type of services. Table 3. Innovation and spatial distribution of business services. OLS estimation 3.178 2.812 Intercept (0.000) (0.000) 0.327 Business services (0.000) 0.515 Spatially lagged BS (0.000) R2-adjusted 0.179 0.216 18.86 23.62 F-statistic (0.000) (0.000) Notes: OLS (Ordinary Least Squares). N=83. P-values are shown in parentheses.
So, once confirmed the relevance of the presence of business services in neighbouring regions we introduce this variable ( LBS ) in our modified knowledge production depicted in equation 5: 10
ln I i = β1 ln Ri + β 2 ln Di + β3 ln M i + β 4Wi ln I i + + β5 LBSi + ∑ δ c Nic + ε i
[6]
i =1
As noted before, we assume a time lag between innovation efforts and results by referring the explanatory variables to the period 2000-2004. The presence of spatial autocorrelation is assessed by using different Lagrange multiplier tests which evaluate the form of spatial dependence: a lag or an error model. The latter is an alternative form of incorporating spatial autocorrelation by specifying a spatial process for the disturbance term. The spatial error model for our equation 6 would be as follows: 10
ln I i = β1 ln Ri + β 2 ln Di + β3 ln M i + β 4 LBSi + ∑ δ c Nic + ε i i =1
[7]
ε i = λW ε i + µi Where µ is asymptotically distributed as N (0, δ 2 ) , ε follows a first-order Markov process and λ is the spatial autoregressive coefficient for the error lag. For estimating equations 6 and 7 we must employ maximum likelihood (ML) as ordinary least squares (OLS) estimations are biased in the case of the spatial error model and inconsistent and biased in the case of the spatial lag model. The econometric results are summarised in Table 4. Starting with the OLS results the explanatory variables are positive and significant, excepting the variable referred to R&D expenditures which is not significant at the 10% level. Nevertheless, when estimating the spatial models, this variable becomes (as expected) clearly significant. We have to note, however, the lower value obtained in comparison with those studies that employed patents as dependent variable. For instance, in the European case, this elasticity goes from 0.4 to 0.8 in the papers by Botazzi and Peri (2003) and Greunz (2003) but descends to 0.2 in the analysis of Moreno et al. (2005). The variables related to local agglomeration factors are positive and significant in all cases, with elasticities of 0.17 for population density and 0.43 for the participation of manufacturing in employment. Concerning our indicator of availability of business services, the coefficient is positive and significant, and similar to that obtained for manufacturing, 12
thereby corroborating the key role of business services. In the case of the institutional differences among countries, all dummies are positive and significant, with the exception of Slovakia.
R
Table 4. Estimation of regional innovation activity. ML estimation OLS Variable estimation Wbin Wdis 0.094 0.110 0.106 (0.119) (0.022) (0.025)
Wdis2 0.103 (0.027)
D
0.167 (0.000)
0.097 (0.000)
0.099 (0.000)
0.104 (0.000)
M
0.428 (0.000)
0.278 (0.000)
0.269 (0.000)
0.269 (0.000)
0.358 (0.000)
0.378 (0.000)
0.373 (0.000)
0.447 (0.000)
0.215 (0.051)
0.211 (0.051)
0.228 (0.031)
yes 0.722 14.143 17.592 (0.000)
yes 0.777 -2.834
yes 0.784 -4.688
yes 0.786 -4.836
LM-ERR
5.534 (0.018)
0.583 (0.445)
0.392 (0.531)
0.329 (0.567)
RLM-LAG
12.113 (0.000)
RLM-ERR
0.054 (0.8151) 18.977 (0.000)
20.831 (0.000)
20.980 (0.000)
WLn(I) LBS Dummies R2 AIC LM-LAG
Likelihood ratio test
Notes: OLS (Ordinary Least Squares), ML (Maximum Likelihood). N=83. P-values are shown in parentheses.
As for the spatial autocorrelation, the traditional tests: LM-LAG, Lagrange multiplier test for a missing spatially lagged dependent variable, and LM-ERR, Lagrange multiplier test for error dependence, were computed. In all of the cases the matrices try to reflect the fact that physical proximity matters in knowledge diffusion. A robust version of both tests (RLM-LAG, RLM-ERR) was also included. The value obtained for the LM-LAG test is higher than the value for the LM-ERR test so the spatial lag model is the most suitable form for incorporating spatial dependence. If we take the robust version of the tests, the RLM-LAG clearly rejects the absence of spatial autocorrelation at the 1% significance level, whereas the RLM-ERR does not reject the null hypothesis. The adequacy of the spatial lag model in comparison with the OLS model is corroborated by the increase in the explanatory power of the regression: the Akaike Information Criterion (AIC) descends from 14.14 to negative values (-2.83, -4.69 and 4.84) and the R2 increases from 0.72 to 0.78 in the case of the spatial models. The values of the likelihood ratio tests are also significant. We use three different weight matrices in the estimation in order to take into account different spatial structures of dependence. The Wbin is a physical contiguity matrix with elements equal to 1 in the case of two neighbouring regions and 0 otherwise. This matrix is binary and symmetric. The two other matrices, Wdis and Wdis2 are the inverse of the distance and the squared inverse of the distance, respectively. The inverse distance implies tat
13
spillovers diminishes with distance, or, in other words, that the shorter the distance between two regions, the higher the intensity of the spillover is. Concerning the coefficients obtained, we can note how similar results are obtained by applying different weight matrices. The elasticity of innovation with respect to R&D expenditures is positive and significant, ranging from 0.10 to 0.11 for the three weight matrices. The rest of “control” variables maintain their signs, although their explanatory power descends. For instance, in the case of population density the elasticity descends to 0.10. In the case of manufacturing a similar fall is observed, with elasticities ranging from 0.27 to 0.28. The elasticity of innovation in one region with respect to innovation in neighbouring regions ranges from 0.36 in the case of binary contiguity to 0.38 and 0.37 when distance matrices are applied. Therefore, the existence of interregional knowledge spillovers is confirmed in our European regions analysed. The higher values for the spillovers obtained with the distance matrices is explained by the fact that these are full matrices which take into account the whole range of spillovers in regions. In the case of the variables related to business services in neighbouring regions its positive explanatory power maintains its significance, ranging from 0.21 to 0.23. Again, we find values comparable to the impact of manufacturing which leads us to affirm that the availability of business services is a relevant driving factor for regional innovation. 4. Conclusions. Although quite recent, the recognition of the key role of the tertiary sector in advanced economies is nowadays widely extended. The objective of this paper was to analyse the contribution of business services to regional innovation. In difference with the majority of studies, we proxy innovation as the percentage of firms developing product and process innovations. This indicator was provided in the European Regional Innovation Scoreboard and offers a clear advantage in comparison with patents: it better captures the innovation carried by small and medium size firms which tend to use patents in a lesser degree than big companies. As for business services, we included within our group of interest three industries of the NACE rev 1.1.: 72 computer and related activities, 73 research and development and 74 other business activities. All activities commonly classified as KIBS are integrated in this definition, but also other service activities which cannot be described as knowledge-intensive. This justifies why in this paper we employ the term “business services” instead of the term “KIBS”. More concretely, the presence of business services is proxied by their participation in regional employment. Our point of departure was the knowledge production function introduced by Griliches (1979) and pioneering applied to the analysis of knowledge spillovers by Jaffe in 1989. The traditional KPF relates innovation activity to R&D efforts. In our version, in addition to R&D expenditures, we introduce two key variables to capture agglomeration economies, namely, the density of population and the share of manufacturing in employment. In the case of the density of population it is clear that a higher population implies a higher potential demand in a region. Concerning the participation of manufacturing, a well-developed industrial structure generates benefits not only in terms of higher possibilities of cooperation (among other aspects in the innovation domain) but also affects the propensity to innovate (at least in a technological sense, given the fact that “hard” innovation activity is more intense in manufacturing than in services). Institutional factors have much to say in the innovation performance of firms, and there are differences among countries, so, to capture this fact, we introduce a set of national dummies in our KPF. Our spatial model starts from this modified KPF and incorporates two aspects: firstly, the existence of knowledge spatial spillovers (introducing a spatially lagged innovation variable) and, secondly, the impact of the availability of business services. Curiously, the regression analyses carried out shown a closer relationship between the presence of business services in neighbouring regions and regional innovation than between the presence of business services and the innovation activity carried out in the same
14
region. So, in the final form of our KPF we include a variable for the spatially lagged participation of business services. The results obtained in our spatial model are accordance with previous analyses: the elasticities of R&D expenditures, population density and manufacturing are positive and significant. The national dummies (excepting Slovakia) are also significant. In terms of the two “novel” aspects examined, both knowledge spillovers and the availability of business services exert an important effect on regional innovation performance. Thus, the innovation activity carried out in neighbouring regions shows a positive impact on innovation that exceeds the effect of the rest of variables analysed. As for the effect of the location of business services in neighbouring regions, its value is quite high and comparable to the impact of manufacturing. We can conclude, therefore, that the regional distribution of business services plays a key role in explaining regional innovation performance. Moreover, this impact is not only referred to the own region but also to neighbouring regions. This finding implies a need for coordination in the design of those regional policies aimed at affecting the development of business services.
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