Regional Clusters in Germany their Geography and their Relevance for Entrepreneurial Activities

Transcription

1 European Planning Studies, Vol. 12, No. 6, September 2004 Regional Clusters in Germany their Geography and their Relevance for Entrepreneurial Activities ROLF STERNBERG and TIMO LITZENBERGER [Paper first received, November 2003; in final form, January 2004] ABSTRACT The aim of this paper is to identify regions with industrial clusters in Germany and to analyse their entrepreneurial environment. A new index employing industry data supplied from the German Federal Labour Office is used to elaborate on spatial clusters in the most concentrated German industries. A second data set collected as part of the Global Entrepreneurship Monitor (GEM), serves to assess the relationship between regional clusters and entrepreneurial activities and attitudes. The paper tests the rather popular but hitherto seldomly empirically-proven hypothesis that the existence of one or several industrial cluster(s) in a region has a positive impact on the number of start-ups and attitudes in the very same region. The implications of the findings with respect to regional policies encouraging industrial clusters are discussed briefly in the concluding section. 1. Introduction Clusters and entrepreneurship have become very popular subjects in economics, regional science, and economic geography. In the last decade, the seminal works by Michael Porter (1990) and Paul Krugman (1991) have motivated a growing number of scholars to investigate the empirical evidence for clusters, their definition, and their implications for economic policy. It would not be an exaggeration to say that the concept of clusters has become en vogue in the aforementioned academic disciplines, as well as in many applications of local economic development policy. The same is true of entrepreneurship and new firm formation processes. It is the mark of the new economy boom, as indicated by economic policy and widely in the academic world (see. e.g., Reynolds et al., 2002; Wennekers & Thurik, 1999), that entrepreneurial activities are seen as an essential impetus for both national and regional growth. Given this, the more surprising it is to the researcher that there exist only few analyses to date of the relationship between cluster attributes of a region and entrepreneurship activities in the same region. Though there is literature to be found on a theoretical level, there is no coherent theoretical framework explaining firm start-up and cluster development from a Corresponding author: Prof. Dr. Rolf Sternberg, University of Cologne, Department of Economic and Social Geography, Albertus-Magnus-Platz, D Köln. sternberg@wiso.uni-koeln.de Timo Litzenberger, University of Cologne, Department of Economic and Social Geography, Albertus- Magnus-Platz, D Köln. litzenberger@wiso.uni-koeln.de ISSN print/issn online/04/ DOI: / Taylor & Francis Ltd

2 768 Rolf Sternberg and Timo Litzenberger regional perspective (Fornahl & Menzel, 2002). Hence, the empirical research gap is even more sizeable. The objective of this paper is two-fold. Firstly it aims at narrowing an obvious empiricalmethodological research gap: empirical evidence for industrial clusters within Germany and its regions. Despite the fact that several scholars from abroad have elaborated on German regions (especially studies on industrial districts, see, e.g., Piore & Sabel, 1984) and, more rarely, on clusters within Germany (van der Linde, 2002), such studies were not able to use the very recent data for the year 2002 on establishment and employment in German regions. Due to the strict data protection law, research in Germany is mainly characterised by case studies (Glassmann & Voelzkow, 2001, p. 85). There are few studies that deal generally with the local concentration or clustering of industries. Most of them (e.g. Brenner, 2003; Glassmann & Voelzkow, 2001; or Helmstädter, 1996) draw on measurements of industrial specialisation and neglect the spatial size of the regions investigated. We try to bridge this gap in section 4. Secondly, we analyse a part of the relationship between clusters and entrepreneurship that has not been adequately investigated empirically or theoretically to date. German regions will serve to exemplify this relationship concretely, using new data not previously applied to this purpose. Section 5 of this paper aims at filling in this gap, before policy implications of our findings will be discussed. 2. On Clusters and Entrepreneurship 2.1 Definition of clusters There are various concepts of cluster definitions to be found (see Enright, 2003; or Martin & Sunley, 2002 for an overview of definitions). In this paper clusters are seen as a hierarchy of three concepts (European Commission, 2002, p. 14). The hierarchical first or lowest degree is the spatial industrial or regional cluster. The regional cluster is defined as an industrial cluster, in which member firms are in close proximity to each other (see Enright, 1996, p. 191), whereas an industrial cluster is seen as a set of industries related through buyer-supplier and supplier-buyer relationships, or by common technologies, common buyers or distribution channels, or by common labour pools (see Porter, 1990). As Figure 1 shows, local entrepreneurial attitudes and entrepreneurial activities can be the starting-point for the evolution of a regional cluster. Entrepreneurial attitudes and activities interact and their characteristics are normally bound to the region (see Bergmann, 2002). Sufficient start-up activity leads to a spatial concentration of firms the main precondition for a regional cluster. Such a cluster is just a first step in a hierarchy of cluster evolution. As the basis for other concepts, regional clusters also include Marshallian industrial districts of SMEs, production systems containing big hub firms, the flexible production systems of Scott (1988) and the innovative milieu of Maillat and Lecoq (1992) (see Enright, 1996). According to Porter s (1998, p. 78) view of clusters as geographic concentrations of interconnected companies and institutions in a particular field, linkages and local actors play an important role. A regional cluster can be upgraded through more organised co-operation (agreement) between firms, stimulated by trust, norms and conventions (European Commission, 2002, p. 14) which can improve the firm s innovation activity. Consequently, it becomes a regional innovation network (see Camagni, 1991). In this network, the partly organised co-operation is encouraged by trust, norms, and conventions which can foster innovation activities in firms. The third and hierarchically highest concept is the regional

3 Regional Clusters in Germany 769 Figure 1. Three hierarchical cluster concepts innovation system (see also Braczyk et al., 1998). Besides co-operation between firms only, it exhibits co-operation between firms and organisations, which helps to develop and diffuse knowledge. The development or upgrading from a cluster to an innovation system can improve the competitiveness and innovation capability of the firms located in the cluster (see European Commission, 2002; or Capello, 1999, who stresses the positive effects of regional collective learning and cumulative knowledge creation). Furthermore, the regional-bound cluster attributes increase the propensity of the respective local population to start new firms and thus, to enhance regional growth. Whether an identified regional cluster is also an innovation system or an innovation network will not be analysed in this paper. In Germany, there is no data available that gives indications of inter-firm co-operation for a complete set of subregions smaller than federal states ( Bundesländer ). Also firms in clusters do not necessarily have close contacts or buyer-supplier linkages with each other (see Bathelt & Glückler, 2002). Research interested in inter-linkages between firms and even institutions or in regional culture has therefore to explore in each region whether or not the requirements for the hierarchically higher concepts are met. Since such an sophisticated approach would clearly exceeded our resources we focus on regional clusters in our empirical analysis. 2.2 Entrepreneurship as a regional event, too Alternative theories and approaches explaining entrepreneurial activities can be found in the literature. Theoretical analyses of neo-classicist provenance, still operate from a quasi spaceless wonderland, abstracting completely from spatial factors, a good 40 years after the appearance of spatial analysis (Isard, 1956). Recently, however, the reasons for a more serious consideration of geographical influences on start-up decisions have been accumulating: the issue of spatial proximity. ( Geographical proximity following the French school led by Torre and Gilly, 2000) has become very modern in particularly those areas of economics whose protagonists consider themselves to be a part of the new economic geography. This

4 770 Rolf Sternberg and Timo Litzenberger is indicative of the fact that the economic sciences no longer explain the performance of companies solely or primarily on the basis of determinants related to the firm itself or to the entrepreneur. The consideration of environmental factors in a broad sense, including spatial proximity and features of the regional environment, is becoming evermore prevalent and popular. According to this increasingly influential school of thought, entrepreneurship is a generically social, a collective phenomenon (Johannisson, 2000, p. 306) that cannot be explained solely through the attributes of individual persons. Rather, entrepreneurship must be explained with recourse to the entrepreneurial social infrastructure (Butler, Flora & Flora, 1993, p. 48) regarding the social capital concept and the actor networks related to it, which create positive externalities. This makes the relationship between entrepreneurship and regional development processes obvious (see Bolton & Westlund, 2000). Such a regional environment does not have clear-cut boundaries and cannot be equated with the borders of a region as defined by administrative authorities. Most empirical studies on entrepreneurship, to date, refer to the national level as a standard scale, ignoring the interregional differences within a particular nation. With respect to Germany, we assume that the dimensions of a regional environment lie clearly below the level of the nation and below the level of the federal states and also clearly above the level of the living or work space of a potential entrepreneur. This demarcation of a region is in concordance with the understanding of a regional environment, in which the majority of potential entrepreneurs ponder and execute start-up plans. The high degree of spatial immobility of entrepreneurs has been documented in numerous empirical studies (see, e.g., the comprehensive analysis of start-ups in German innovation centres, Sternberg et al., 1997) and is evidence for the influence of the regional environment (which also includes the majority of personal affiliations) on start-ups. The arguments for the hypothesis that existing clusters have positive impacts on entrepreneurial attitudes and entrepreneurial activities (see Sternberg, 2003, 2000) are outlined on the following: All things being equal, the probability that a person will start a firm within a certain region increases as a function of the number and size of incubator organisations within the region whose fertility is sufficient for the emergence of start-ups. The development of already existing start-ups also profits from a positive regional environment, which, in addition to the incubators (availability and attitude to spin-offs), hinges necessarily on an equally positive entrepreneurial climate. Within the scope of a self-augmenting process, e.g. via role model effects of successful start-ups, and their interregional networking (see Fornahl, 2003), regional clusters of start-ups may form regions, in which the creation and development of start-ups is economically more favourable than outside these clusters. Stuart and Sorensen (2003), Sorensen and Audia (2000) as well as Sternberg and Bergmann (2003) distinguish between impacts on the creation and the development of start-ups. But in general, the favourable climate on start-ups is a result of agglomeration economies and other positive external effects associated with spatial proximity and can lead to a regionally-caused self-propelling cumulative process. A primarily demand- or environmental-oriented approach, whereupon an individual s decision to start a firm is the result of influences from the macro- and the microsocial environment, which are perceived differently depending on person-related factors is depicted in Figure 2. The macrosocial environmental factors include both factors that develop primarily on a supraregional level (i.e. in other territories belonging to the nation or outside the nation) and factors that develop within the regions of interest. The importance of the supraregional and regional determinants varies from factor to factor. A multitude of determinants can be found in the relevant literature in tables listing environmental factors. Most important among these are the cultural, social, political, and financial conditions of a region, as well as the system of

5 Regional Clusters in Germany 771 Figure 2. Regional impacts on entrepreneurial activities education and research, the infrastructure, and the economic structure (see, e.g., Bruno & Tyebjee, 1982, p. 290f). An explicit component of the regional economic structure and the question that concerns us here is the existence and dominance of individual industries within the relevant regions, e.g. using the number of firms or employed persons as a measure. Empirical evidence for the high relevance of these macrosocial regional environment can be found for the US regions. Prevezer (1998) shows impressively that, the quantity and, even more so, the quality of academic R&D institutions in the places investigated were the most important factors for explaining the market entry of biotech start-ups (for further empirical evidence concerning US high-tech start-ups see Zucker et al., 1998 and Feldman, 2001). In Germany and for less technology-intensive industries Moßig (2000) identifies local spin-offs as the origin of clusters of the German packaging machine industry. The microsocial environmental factors include, in particular, the social and professional backgrounds and the egocentric networks of the potential entrepreneurs. These elements can also be shaped by primarily regional (e.g. private networks) or primarily supraregional (e.g. a large number professional networks) influences. Person-oriented factors include e.g. entrepreneurial motivation (push vs. pull factors), demographic factors (age and sex), as well as personality traits (e.g. efficiency and the willingness to take risks). Each individual filters the environmental signals he or she receives. The totality of individual entrepreneurial activities in a particular region determines the entrepreneurial activity of the region. As a matter of principle, macro- and microsocial as well as person-oriented factors are operative in all nations and in all regions of a nation. They have a clear regional dimension, however, because they come into play to varying degrees and thus operate differently in different regions. The high degree of spatial immobility of entrepreneurs in academia is correctly interpreted as evidence for the importance of the regional environment for start-ups. (see Wagner & Sternberg, forthcoming; Sternberg & Bergmann, 2003; and Bergmann 2002 for entrepreneurial differences in German regions derived from GEM and the Regional Entrepreneurship Monitor ). To be sure, not only intraregional environmental factors influence entrepreneurial activi-

6 772 Rolf Sternberg and Timo Litzenberger ties. It would be unwise to support a regional determinism. As is the case with regional development in general, monocausal explanations are out of place here. Thus, the view taken here argues for a combination of person- and environment-oriented approaches, which should be familiar to proponents of more person-oriented approaches, like the psychologically-oriented research interested in individual strategies and motivations. Analyses of this type show that the factors that determine the start-up decision of an individual (entrepreneurial activity) are not identical to those that determine the start-up success and that this success of start-ups is dependent also on the characteristics of its regionally bound determinants (see Feldman, 2001; or van Gelderen et al., 2000). 2.3 Why and how could cluster characteristics of a region affect entrepreneurship? As discussed in the previous section, the relevant entrepreneurship and new firm formation literature distinguishes between person-related and environment-related determinants if one searches for theoretical explanations for an individual s decision to start a new firm (see Preisendörfer, 2002). The environmental factors also include regional factors, a number of which are of particular interest for the purposes of this paper. Environment includes all determinants of the potential entrepreneur s decision that are external to the person. This includes regional factors, i.e. determinants, whose manner of influence varies from region to region (e.g. the so-called entrepreneurial climate ). Or as Quaid (2002, p. 916) makes clear, greater entrepreneurial behaviour can be the result of changes in entrepreneurial culture. The way the potential or actual entrepreneur perceives these factors affects his or her decision-making and the success of a possible start-up. We may distinguish between approaches of industrial economics, of organisational ecology, and, finally, between firm-related network approaches (see, Preisendörfer, 2002). Industrial economics argues primarily from the point of view of the industry that the start-up belongs to and defines factors such as capital intensity, competition, or concentration in the relevant industry as the main factors that influence the decision to start a new firm. The spatial level at which these factors are measured is not always made clear in empirical studies: the appearance of an industry can be completely different on a global level as compared to a national or regional level. These theoretical approaches also address the question of the nature of the influence of clusters, though much research remains to be done here. Two questions are of particular interest in this respect: does the number or proportion of employees in cluster industries in a region influence the entrepreneurial activity in the region? Is this influence industry-specific, i.e. are the cluster-related effects in the industries in the region belonging to the cluster stronger than in industries outside of the cluster? Organisational ecology works primarily with longitudinal data that makes it possible to track the changes in influence in environmental factors taken to be important. The data also makes it possible to assess the relationships between e.g. death rate and firm age or between number of firms and birth or death rate. But, here, too, the spatial level is not usually taken into account. Finally, the firm-related network approaches try to identify the degree of embeddedness of firms in organisational networks, claiming an important role of embeddedness for the success of firms. Although clusters can also decline economically and can then have a negative impact on the development of the region, in sum, positive impacts of existing spatial-sectoral clusters on regional growth and development seem to prevail. For the purpose of this paper it is important to know which role entrepreneurship plays in this relationship. Following Sternberg (2003) one might argue that a large number of start-ups in absolute as well as in relative numbers is, in general positive for regional development, Following Schumpeter s notion of creative destruction, this can also be interpreted with respect to regions. Reviewing the literature and existing theories on the formation processes of new firms, we

7 Regional Clusters in Germany 773 can develop at least the following hypothesises which underscore a supportive role of cluster characteristics for entrepreneurial activities and entrepreneurial attitudes: Cluster characteristics may reduce the barriers of entry for new firms. These lower entry barriers make it easier at least in the perception of the individuals for potential founders to risk taking the difficult step from being a potential founder to being a real founder of a start-up (since some individuals equate lower entry barriers with better chances of success, which generally is a fallacy). Agglomeration economies referring to job-matching opportunities and service economies of scale and scope (Gordon & McCann, 2000) are also relevant for start-ups because in smaller, non-clustered regions they have several comparative disadvantages compared to established firms (wage level, availability of employees of different skills). Based upon the argument of the industrial complex model referring to explicit links of sales and purchases between firms (Gordon & McCann, 2000), we know that the local/regional market and customers are decisive for many start-ups during the first months and years. If the regional economy has cluster characteristics it is easier for start-ups to find relevant customers and suppliers, because there is more of a choice within the region. The social-network model, referring to the importance of social networks for the firm s success, can be transferred to start-ups or wanna-be entrepreneurs as well. Empirical studies reveal that the social environment (very often connected with the home region of the potential founder) and the respective networks can have a crucial impact on the decision to start a new firm (see Fornahl, 2003, for the role model hypothesis; see Brüderl & Preisendörfer, 1998, for an empirical test of the network argument). 3. Data description and variables The data used in this paper stems from two different data sets. The first set is provided by the German Federal Employment Services. For each of the 440 German districts ( Kreise ), it contains the number of establishments employing at least one person subject to the social security system and the number of employees itself. The data is industrially disaggregated down to the 3-digit level of the German Classification of Economic Activities, Edition 1993 (WZ93) of the German Federal Statistical Office ( Statistisches Bundesamt ) 1, with 125 groups assigned to manufacturing (chapters C-F) and 90 groups assigned to services and trade (chapters G-Q) (see Federal Statistical Office Germany, 2003). It includes the year 2002 and is the most current data obtainable at the moment. The original spatial level of Kreise has been aggregated to the 97 area-wide planning regions, which were originally created by the German Federal Office for Building and Regional Planning (BBR) considering spatial functional linkages (see BBR, 2003). In terms of entrepreneurial activities and attitudes this paper uses the databases of GEM, which offers data for Germany as a whole (as for 37 other countries in 2002) as well as, due to heavily expanded sample sizes in this country, for federal states and the 97 planning regions. The German GEM surveys , are combined making available a database of almost 28,000 cases (see Reynolds et al., 2002 and Sternberg & Bergmann, 2003 for the recent GEM global report and country report Germany). GEM intends to measure the level of entrepreneurial activities and to identify the individual factors and the entrepreneurial framework conditions that may explain differences in the levels between German regions and countries respectively. Within GEM three measures of entrepreneurial activity are distinguished and are calculated for each German planning region (on the basis of the 18 to 64-year old inhabitants): the share of nascent entrepreneurs, the share of young entrepreneurs (or new businesses) and the Total Entrepreneurial Activity rate (TEA) 2. In this paper we use

8 774 Rolf Sternberg and Timo Litzenberger three questions from GEM that focus on the entrepreneurial attitudes, e.g. the perceptions of the individuals in the respective region. Thus, to operationalise the regional entrepreneurial attitudes in this paper three dichotomised yes-no variables derived from the GEM questionnaire of are used: Fear of failure would not prevent you from starting a business. In the next six months there will be good opportunities for starting a business in the area where you live. You have the knowledge, skill and experience required to start a new business. The share of yes answers correlates positively with a positive entrepreneurial attitude of the population within the region. 4. Regional clusters in Germany 4.1 Measuring concentration There is no agreed method for identifying and mapping clusters, either in terms of the key variables that should be measured or the procedures by which the geographical boundaries of clusters should be determined (Martin & Sunley, 2002, p. 29). Amongst the variation of results and methods to identify clusters there are, in general, two different approaches: the top-down, and the bottom-up or case study approach (for an assessment of the two approaches see Martin & Sunley, 2002, p. 29ff). Using the top-down approach, we have first to investigate to which degree an industry is spatially concentrated at all before we try to localise regional clusters. A non-uniform spatial distribution of an industry and hence a certain spatial concentration of firms is the precondition for the formation of clusters (see Figure 1). In literature (Amiti, 1998, 1997; Helmstädter, 1996; Kim, 1995; or Krugman, 1991) concentration is often described by measurements which specify the degree of spatial division of labour or more simply: industrial specialisation. For example Alecke et al. (2003, p. 2) define concentration relative to total employment by applying the measurement of Ellison and Glaeser (1997) for German industries. However, the most common tool is the coefficient of localisation which is based on the location quotient (Kim, 1995, p. 883). The location quotient was first defined by Hoover (1936) and depicts the degree of specialisation of a region in a certain industry. It is used to construct the locational Gini coefficient in order to measure the distribution of an industry over the subregions of the space analysed (for a detailed description of how to calculate the locational Gini or coefficient of localisation see Kim, 1995, p 883). The location quotient and therefore the locational Gini measure regional specialisation as a deviation of the distribution of the total industrial employment, as they take the overall employment as the referring variable. But if we look at our definition of clusters where clusters are seen as firms in close proximity to each other the spatial dimension is neglected by measurements of specialisation. There is no value that gives any indication of the size of the analysed region or the magnitude of proximity between firms. The reference value should therefore be the area and not the employment or the inhabitants of a region. If there is a reference value other than the area of the region, an equal spatial distribution of the industry is theoretically not expected (see Roos, 2002). Using the above measures of specialisation as measures of industrial concentration implies that the location of industries depends on the distribution of the total employment and that the location of industry and the location of employment and of the inhabitants are not interdependent. The Gini coefficient (G std ) is an appropriate tool to

9 Regional Clusters in Germany 775 measure concentration (for alternative measurements of concentration see Devereux, et al., 1999), but it has to be weighted with the size of the regions. Hence the G std, weighted with the area of the region (G a ) is proposed as a measurement of spatial industrial concentration. 4.2 Most concentrated industries For the identification and mapping of clusters in the following sections, the ten most concentrated industries in both manufacturing and service, respectively, were chosen. To avoid the effect of a higher concentration due to a lower number of firms compared to the number of regions, industries with less then 97 firms are excluded from our analyses. Industries, whose location was obviously chosen on the basis of first-nature advantages, e.g. the existence of natural resources, of coal, iron-ore, peat, or coasts, were also excluded from the focus of further research. The ten industries chosen in manufacturing (indicated in grey in Table 1) are manufacturers of watches and clocks (DL335); aircraft and spacecraft (DM353); weapons and ammunition (DK296); man-made fibres (DG247); motor vehicles (DM341); reproduction of recorded media (DE223); accumulators, primary cells and primary batteries (DL314); textile weaving (DB172); motorcycles and bicycles (DM354); and knitted and crocheted articles and fabrics (DB176 and DB177). The latter industries have been aggregated, because of the proximity of their products and the relative smallness of DB177. The results do confirm the findings of Keilbach (2002) and Haaland et al. (1999). Most of the highly concentrated industries can be found in their surveys, as well. Highly specialised industries are concentrated, whereas industries supplying daily demands, e.g. bakeries, do not feature in the table of most concentrated industries. The ten most concentrated industries in services (see table 2) are other financial intermediation (JA652); other supporting transport activities (IA632); radio and television activities (OA922); extra-territorial organisations and bodies (QA990); activities auxiliary to financial intermediation, except insurance and pension funding (JA671); news agency activities (OA924); research and experimental development on social sciences and humanities (KA732); other computer related activities (KA726); motion picture and video activities (OA921); and scheduled and non-scheduled air transport (IA621 and IA622). Again, the latter industries were aggregated due to the proximity of their products and the relative smallness of one of the industries (IA622). In manufacturing and services, the unweighted means of the G a (G a manu 0.695; G a serv 0.565) are higher than the means weighted with employment (G a man w 0.546; G a serv w 0.442). This indicates that in manufacturing and services small industries do concentrate geographically more than bigger industries (compare also the number of employment and number of establishments of the most concentrated industries with the 3-digit mean in both Table 1 and Table 2, respectively). This effect does not totally stem from industries with less than 97 firms, for in services, there are only three industries space transport (IA623); transport via pipelines (IA603); and databases (KA724) with less than 97 firms, compared to 18 industries in manufacturing. Another interesting issue is that on the 3-digit level manufacturing is higher concentrated than services (G a manu w 0.546; G a serv w 0,442), whereas on the scale of total sectors manufacturing is spatially more dispersed than services (G a manu total 0.364; G a serv total 0.399). In other words: on an industrially disaggregated level, manufacturing clusters more than services, but it also avoids the proximity of other manufacturing industries more strongly, in a way that aggregated manufacturing is less concentrated than aggregated services. This result indicates that the location of the analysed manufacturing industries relies much more on localisation economics then on urbanisation economics like in services.

10 776 Rolf Sternberg and Timo Litzenberger Table 1. Most concentrated manufacturing industries 3-digit economic activities: Manufacturing (measured by employment) Classification WZ93/ISIC Ga Employment Establishments No. of Clusters Mining of uranium and thorium ores CA120/ Manufacture of coke oven products DF231/ ,130 4 Mining of iron ores CB131/ Mining and agglomeration of hard coal CA101/ , Processing of nuclear fuel DF233/ Service activities incidental to oil and gas extraction, excluding surveying CA112/ , Manufacture of pesticides and other agro-chemical products DG242/ , Mining and agglomeration of lignite CA102/ , Production of salt CB144/ , Mining of non-ferrous metal ores, except uranium and thorium ores CB132/ , Manufacture of watches and clocks DL335/ , Extraction of crude petroleum and natural gas CA111/ , Mining of chemical and fertiliser minerals CB143/ , Extraction and agglomeration of peat CA103/ , Manufacture of weapons and ammunition DK296/ , Manufacture of ceramic tiles and flags DI263/ , Manufacture of tobacco products DA160/ , Tanning and dressing of leather DC191/ , Manufacture of aircraft and spacecraft DM353/ , Manufacture of man-made fibres DG247/ , Manufacture of motor vehicles DM341/ , Reproduction of recorded media DE223/ , Processing and preserving of fish and fish products DA152/ , Manufacture of refined petroleum products DF232/ , Manufacture of vegetable and animal oils and fats DA154/ , Manufacture of accumulators, primary cells and primary batteries DL314/ , Building and repairing of ships and boats DM351/ , Manufacture of knitted and crocheted articles DB177/ , Manufacture of knitted and crocheted fabrics DB176/ , Manufacture of basic iron and steel and of ferro-alloys (ECSCII) DJ271/ , Textile weaving DB172/ , Manufacture of motorcycles and bicycles DM354/ , Mean for all 125 manufacturing industries Mean, weighted with employment ,367 3,

11 Regional Clusters in Germany 777 Table 2. Most concentrated industries in services Classification No. of 3-digit economic activities: Services (measured by employment) WZ93/ISIC Ga Employment Establishments Clusters Space transport IA623/ Scheduled air transport IA621/ , Sea and coastal water transport IA611/ ,176 1,124 Transport via pipelines IA603/ Other financial intermediation JA652/ ,343 1,402 9 Database activities KA724/ , Other supporting transport activities IA632/ ,029 1,365 7 Non-scheduled air transport IA622/ , see IA Radio and television activities OA922/ , Extra-territorial organisations and bodies QA990/ ,840 1,149 7 Activities auxiliary to financial intermediation, except insurance and pension JA671/ ,883 3,254 8 News agency activities OA924/ ,134 1,313 8 Research and experimental development on social sciences a. humanities KA732/ , Other computer related activities KA726/ , Motion picture and video activities OA921/ ,841 3,428 5 Mean for all 90 service industries ,951 17,586 Mean, weighted with employment 0.442

12 778 Rolf Sternberg and Timo Litzenberger Figure 3. Concentration and specialisation Figures 5 and 6 support these insights. The individually highly concentrated 3-digit manufacturing sectors are more evenly distributed over all of the planning regions than is the case for services. The latter concentrate with different service industries in just a few planning regions, preferably in planning regions including major cities. 4.3 Measuring regional clusters Using the top-down approach, we have to take into account that regional clusters have to be regarded in their context. As a concentration of economic activities, clusters must be seen relative to the sum of the regions. A measurement often used for this purpose is the location quotient (see, for example, Dümmler & Thierstein, 2003; or Lublinski, 2003). In our paper we use the industrial stock of the respective planning region relative to the whole country. In contrast to the location quotient, the relative industrial stock uses the number of inhabitants instead of total employment, specifically the employment of the next higher industrial scale, as the referring value. But the location quotient and the relative industrial stock are both measurements for the relative industrial specialisation of a region, rather than measurements of industrial concentration. Thus they are not sufficient to identify clusters. The spatial proximity of workers and firms has to be considered, as well. It is measured with the industrial density of one planning region relative to the whole country. Figure 3 shows the relation between specialisation and spatial proximity (concentration). If all people in a very scarcely populated region work in the analysed industry, for example, the region is not necessarily a cluster. Because of the small number of people, it could be, that there is no spatial concentration of economic activity relative to the overall region. Quadrant I shows this case. It is characterised by a high relative industrial stock but a below-average relative industrial density. Quadrant II is the industrial periphery, which can be a city or a scarcely populated region with no or very few workers or firms in the analysed industry. As a consequence, the relative industrial stock and the relative industrial density is below average. A high relative industrial density alone is not sufficient either. Martin and Sunley (2002,

13 Regional Clusters in Germany 779 p. 13) ask: How economically specialised does a local concentration of firms have to be to constitute a cluster? If we take a very big city, the fact that many people living in a relatively small area makes it very probable that the value for the relative industrial density will be higher than the average of the sum of the regions (see quadrant III). But it makes no sense to identify every big city as a regional cluster. The prerequisites for a cluster (quadrant IV) are fulfilled only if the relative industrial stock and the relative industrial density are above average. A third point that has to be considered is the number of firms in the region. Even if the relative industrial density and the relative industrial stock are above average, it could be that the region is dominated by just one or two firms, which goes against our definition and understanding of clusters, e.g. the geographically proximity of many firms. Our top-down approach therefore employs three components for the identification of spatial industrial clusters: the relative industrial density (ID), the relative industrial stock (IS), and the relative size of the establishment (SB). The possible values of ID, IS, and SB range from zero to infinite. If a subregion (planning region) does not differ from the overall region (Germany), the respective values of the three components are equal to 1. Thus, we propose a cluster-index (CI) as a measurement to identify regions as industrial clusters. It is defined as the product of the ID, the IS and the reciprocal of SB: Formula 1. Cluster-Index CI ij ID ij IS ij 1 SB ij e ij n e ij i 1 i i n i i i 1 b ij n b ij i 1 a i n a i i 1 The indices denote the respective industry (j) and the respective subregion (i). The magnitude of the CI is proportional to the number of people employed (e ij ), the number of firms (b ij ), the reciprocal of the size of the area (a i ) and the reciprocal of the number of inhabitants (i i ) of the region regarded. Because of the multiplicative connection of its three components (ID, IS and SB), the possible values of the CI range like the values of the components from zero to infinite, with one as the average which represents the total region. As already said, clusters are relative to the economic activity in the overall region. This means that the value of CI has to be bigger than one, minimally in order to identify a cluster. Since the ID, the IS, and the reciprocal of SB are related by multiplication, it is also possible that a value below 1 of one of the three components can be compensated by a very positive value of the other two. This means that specialised rural regions, as characterised in Figure 3, quadrant I, or an agglomeration or city, quadrant III, can still be recognised as a cluster if the threshold value is exceeded by the CI. The results of the CI suggest clusters on the lowest hierarchical step on the path of cluster evolution (see Figure 1). To provide further insights into the origins or the stage of development on-site studies are necessary, but are not intended in this paper. Advantages of the CI lay in its flexibility, the simplicity of calculation and the availability of the data needed. There might be problems involving clusters cutting across sector classifications, clusters being concealed within several classifications, or clusters crossing spatial boundaries. But these are data problems and can not be solved by the CI.

14 780 Rolf Sternberg and Timo Litzenberger 4.4 Localisation of regional clusters in Germany Since clusters are relative, there is no agreement which magnitude of spatial concentration in a subregion, relative to the overall region constitutes a cluster. An exact threshold does not exist. The critical value depends on the scale of the region, the level of the industrial aggregation (see Krätke & Scheuplein, 2001, p. 6), and above all, on the number of clusters intended to be identified. For our data a CI bigger than 4.00 was employed to constitute a planning region as at least a potential cluster. The value of 4.00 is reached if two of the three components of the cluster index are twice as high for the sub-region as the average of the total region (for the third component being the average, i.e. one). It is also possible to adapt the threshold value for every single industry (see Brenner, 2003), but we wanted to identify clusters with comparable features relative to the overall region. We acknowledge that the value is somewhat arbitrary, but it gives us a suitable mean of about ten clusters for every industry in the 97 planning regions. For all ten analysed industries in the manufacturing sector, a CI bigger than 4.00 identified 115 clusters. In the ten services industries, the same threshold revealed a total of 87 clusters. Figure 4 shows the exemplar case of the manufacture of motor vehicles (DM341). The classification comprises the manufacture of passenger cars, motor lorries, and their resp. engines. A CI bigger than four times the average (CI 4.00) identified 14 planning regions as at least potential clusters. Of these Bremen (code 11, CI 52.00), Stuttgart (72, 46.50), Starkenburg (52, 25.08), Brunswick (22, 21.67), and Ingolstadt (89, 25.08) are the clusters with the strongest value of the CI. The leading regions, Bremen and Stuttgart, are both important sites of production of the DaimlerChryslerAG it is headquartered in Stuttgart and show a very large number of component suppliers. Furthermore there are 19 planning regions in which there are no employees at all in this industry. Thus the CI of the resp. planning regions equals zero. Fifty Five planning regions have some employees but the CI is below the German average of 1. In 9 planning regions, the manufacture of motor vehicles is somewhat more concentrated, nevertheless, the CI does not exceed the threshold value of Apart from Bremen, Brunswick, Düsseldorf and Cologne, all clusters in motor vehicles are found in the south of Germany. In Eastern Germany (New Laender), the only planning regions with a CI above average are South Thuringia (code 55) and South West Saxony (code 61). But the values of CI 1.33 and CI 4.27 make only the latter region as a cluster. For the ten aggregated industries in manufacturing we have identified 115 clusters, significantly more than in the ten service industries (87 clusters). This result corresponds to the higher mean of the G a of the 3-digit industries in the manufacturing sector (see Tables 1 and 2). As a comparison between Figures 4 and 5 shows, the distribution of clusters in manufacturing (measured by G std and the standard deviation (SD) 1.169) are much more evenly distributed over the 97 planning regions than the clusters in services (G std 0.866, SD 2.245). Figure 5 reveals that the highest number of manufacturing clusters located in one planning region is five (South West Saxony, code 61). Four Clusters can be found in Düsseldorf (42), Middle Upper Rhine (70) and Donau-Iller (74). Manufacturing clusters are frequent in the federal states of Baden-Wuerttemberg (27), Bavaria (20), and North Rhine-Westphalia (28). In the New Laender most clusters are situated in the industrialised regions of Saxony (9 clusters) and Thuringia (7 clusters). If we look at the number of manufacturing clusters per 1 million inhabitants, the Land Thuringia is with 2.9 clusters per 1 million inhabitants ahead of Baden-Württemberg with 2.5, Saxony with 2.1 and Bayern with 1.6 clusters per 1 million inhabitants (without consideration of the three city states). For the whole of Germany, a decline from the south to the north and from west to east can be observed. As figure 6 shows, clusters in services can predominantly be found in the major cities of

15 Regional Clusters in Germany 781 Figure 4. Clusters in manufacturing of motor vehicles Germany. The planning regions of Hamburg (code 6) and Berlin (30) accommodate ten clusters out of the ten analysed service sectors. There are nine service clusters in Munich (93), eight clusters in Rhein-Main/ Frankfurt (51), and seven in Bremen (11), Düsseldorf (42), as well as Cologne (44).

16 782 Rolf Sternberg and Timo Litzenberger Figure 5. Clusters in manufacturing 5. Empirical evidence on regional clusters and entrepreneurship The question is whether levels of entrepreneurial activities and the related attitudes among the population in the 97 German planning regions differ if alternative cluster intensities are given.

17 Regional Clusters in Germany 783 First, we will analyse whether the number of clusters per region has an impact on the three variables of entrepreneurial activities (TEA, Nascents and New firms) and on the three variables of entrepreneurial attitudes (Good opportunity, No fear of failure and Knowledge). In a first attempt, we test whether there are significant differences with respect to the six entrepreneurship variables when the 97 regions are separated into two subgroups: one contains regions without any clusters in the defined sense (totally, manufacturing only, services only); the other group contains the rest of the regions which have at least one cluster. We expect to find significant differences between both subgroups and to find higher values for the entrepreneurship variables of the subgroup with at least one cluster. Table 3 shows T-test results for all 20 industries analysed (i.e. ten each for manufacturing and for services). For most of the entrepreneurship variables there are no significant differences between clustered and non-clustered regions. The good opportunity variable is the exception to the rule that entrepreneurial activities and entrepreneurial attitudes of the region are not stronger in regions with clusters than in those without clusters. However, the t-values are almost always positive, as expected. No fundamental differences exist in this respect between variables of entrepreneurial activity and variables of entrepreneurial attitude. Given the fact that several of the 97 planning regions have a relative specialisation in some of the manufacturing industries or some of the service industries, it could be helpful to differentiate between manufacturing and services, considering the ten highly concentrated 3-digits for both subgroups. If we test for manufacturing industries results are very similar to the ones reported in Table 3, which is not surprising because there are more manufacturing clusters than service ones (73 out of 97 planning regions have no cluster according to our definition, but this is true only for 46 regions if manufacturing is considered). Differences between clustered and non-clustered regions are much more evident, if the 10 most highly concentrated service industries are considered (see Table 4). For each of the six variables the signs are as expected, i.e. regions with at least one cluster are stronger in terms of entrepreneurial activities and are more favourable with respect to entrepreneurial attitudes. These differences are statistically significant, the two-tail significance is at least below 0.05%. As Table 4 clearly reveals, differences between clustered and non-clustered regions are stronger for the attitudes variables than for the activities variables. One conclusion is that the relationship between the existence of clusters and its impact on entrepreneurial attitudes and (to a lesser degree) on entrepreneurial activities is stronger for services. As the comparison between Tables 1 and 2 makes clear, service industries, in general, are, at least in Germany, less concentrated spatially than manufacturing industries. However, the highly concentrated service industries (see Table 2) are much more clustered in larger urban agglomerations than is the case for manufacturing industries (see Figures 5 and 6) and for entrepreneurial activities, in general. One reason is that most of the entrepreneurial activities (either as nascents or as already existing, but very young firms) occur within the service sector of the regional economies. Thick labour markets and localised knowledge spillovers may serve as an explanation for relative high new firm formation rates in such service-based urban agglomerations, consistent with results from US regions (see Armington & Acs, 2002). In the next step we focus on only those regions that have at least one cluster. Table 5 shows the differences in the mean values of the six independent variables for all regions with clusters, ordered by the total number of clusters. In accordance with our hypothesis, TEA increases with a growing number of clusters. However, this tendency is not as clear for the two components that comprise TEA taken separately. For the attitude variables, we find a good fit for the question of whether there are good start-up opportunities in the region: the more clusters the region has the better the opportunities are assessed.