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Howsub‐nationalconditionsaffectregionalinnovationsystems:ThecaseofthetwoGermanys

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How sub-national conditions affect regional innovationsystems: The case of the two Germanys

Michael Fritsch1, Holger Graf1

1 Department of Economics, Friedrich Schiller University Jena, Carl-Zeiss-Str. 3, Jena D-07743, Germany(e-mail: m.fritsch@uni-jena.de, holger.graf@uni-jena.de)

Received: 18 July 2010 / Accepted: 9 March 2011

Abstract. We compare two leading regional innovation systems (RIS) in East Germany withtwo RIS in West Germany of about the same size and degree of agglomeration. Our analysesshow that differences in the performance between the regions cannot easily be related to thestructural properties of the respective innovation networks because distinct challenges andmacroeconomic conditions in the two parts of the country, as well as differences in integrationof the regions into their neighbouring spatial environment, play an important role. We concludethat an analysis of RIS should account for the (sub-) national economic conditions as well as forthe region’s position in its spatial environment; merely focusing on region alone is not sufficient.

JEL classification: O31, Z13, R11

Key words: Regional innovation systems, national innovation systems, innovator networks,gatekeeper, social network analysis

1 The embeddedness of regional innovation systems

The concept of national innovation systems (NIS) is a comprehensive approach to analysinginnovation processes. It particularly stresses the division of innovative labour between actorsand the embeddedness of innovation processes in an institutional and macroeconomic environ-ment (Lundvall 1992, 2007; Nelson 1993; Edquist 1997). Following introduction of the NISconcept, many authors proposed focusing on the systemic view of innovation processes inregions, sectors, or technologies, leading to the regional innovation systems (RIS) (Cooke 2004;Asheim and Gertler 2006), sectoral innovation systems (Malerba 2002), and the technologicalsystems (Carlsson 1994) approaches. The RIS approach has become especially popular inempirical research. However, it is still unclear to what degree the RIS approach is appropriate forempirical analyses or, particularly, as a basis for policy decisions. Lundvall (2007), for example,argues that although this concept, like others such as sectoral and technological systems, maylead to important insights, the NIS approach should be favoured because the nation-state is themost important level of policy making. In a recent paper, Shearmur (2010) voiced the criticism

doi:10.1111/j.1435-5957.2011.00364.x

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that many analyses of RIS treat regions as ‘islands’ with characteristics such as local network-ing, institutions, tacit knowledge, and clusters “that have little or no effect on another’s”(Shearmur 2010, p. 5). He calls for a more continuous conceptualization of space in innovationstudies that will account for the surrounding space and particularly for the fact that differentkinds of innovation may require different intensities of face-to-face contact and different degreesof geographic proximity (for another such an argument, see also McCann 2007).

This study investigates the effects of internal interaction and the wider geographic context,as well as influences that may result from historical developments, on the performance of RIS.Specifically, we are interested in the relationship between structural properties of regionalinnovator networks and system performance, taking into account the importance of sub-nationalconditions. By comparing regions in East and West Germany, we can analyse how the differenthistories and resulting macroeconomic conditions of the two parts of the country have shapedregional innovation activity. During the time period we study, East and West Germany have hadmore or less identical formal institutions, but vary considerably as to their soft institutions,economic structures, and the behavior of their residents (Fritsch 2004; Kronthaler 2005). We arethus able to perform a kind of international comparison within one country.

We selected two of the leading RIS of East Germany, Dresden and Jena, and matched themwith two well-performing RIS in West Germany that are of comparable size and degree ofagglomeration, Aachen and Karlsruhe. We use patent statistics to construct networks of inno-vators in the four regions under study (Cantner and Graf 2006; Graf and Henning 2009). Ouranalysis shows important differences with regard to the structural properties of innovatornetworks between the two East German and the corresponding West German RIS that clearlyindicate different modes of innovation that may be traced back to different historical develop-ment and macroeconomic conditions. We find that the two East German RIS show relatively lowlevels of innovation performance but considerably higher degrees of R&D co-operation thantheir more efficient West German counterparts, which is in contrast to what would be predictedby the systems of innovation approach (Cooke 2004; Asheim and Gertler 2006). This mayindicate an effect of differences in the organization of innovation activities as well as of differentmacroeconomic conditions in the two parts of the country. Another factor that could contributeto explaining this phenomenon is that the two East German RIS (Dresden and Jena) are spatiallyisolated ‘hot spots’ in the East German innovation landscape, whereas the two West German RIS(Aachen and Karlsruhe) are embedded in relatively prosperous regional neighbourhoods. Thissuggests that the performance of RIS depends to a considerable degree on their wider spatialenvironment and that sub-national conditions have a strong effect on the performance of RISthat should not be neglected in either empirical analyses or in policy design. The results can beregarded as some indication of the importance of NIS to the performance of the embedded RISand may shed some light on the question of which approach - NIS or RIS - is most appropriate.Moreover, since East Germany experienced a turbulent change from a socialist system to amarket economy, our study also contributes to research on transformation processes.

We introduce the regional innovation systems in the next section (Section 2). Section 3contains a brief overview of the historical background and innovation performance in EastGermany compared to the western part of the country in general, and Section 4 providesinformation on characteristics of and performance in the four case study regions. The compara-tive analysis of the four regions is reported in Section 5. Section 6 discusses development of theregions in the subsequent period. Section 7 concludes.

2 Literature: Regional innovation systems and the network perspective

The systemic view of innovation processes (e.g., Lundvall 1992; Nelson 1993; Edquist 1997),emphasizes the importance of a division of innovative labour and knowledge transfer between

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innovative actors. The RIS approach particularly stresses that the conditions for innovationactivities may differ substantially between regions and that knowledge flows tend to be region-ally bounded (Jaffe et al. 1993; Cooke 2004; Asheim and Gertler 2006). The main argument forthe spatial dependence of knowledge flows is that knowledge has tacit components that can betransferred only via personal relationships that may be facilitated by geographical proximity(Boschma 2005; Breschi and Lissoni 2009). These ideas are put to an empirical test by Fleminget al. (2007), who show that differences in regional innovative performance can be traced backto the connectedness of the respective inventor networks.

However, if there is ‘too much proximity’ (Boschma 2005) or, as Uzzi (1997) terms it,‘overembeddedness’, we can expect adverse impacts on learning and innovation as dense localstructures can pose a barrier to inflows of novel information created external to the system.Taking a perspective different from the view that focuses solely on the benefits of knowledgespillovers within local networks, several authors discuss the importance of interaction withactors external to the local system (Gertler 1997; Bathelt et al. 2004; Bathelt 2005). As such, thebenefits of a dense local network arise as these transmission channels foster the diffusion ofexternal knowledge into the system thereby reducing the risks of lock-in effects (Grabher 1993;Keeble et al. 1999).

Thus, the literature is in agreement about the necessity of external connections for awell-functioning RIS, but it is not very specific about which configurations of RIS in terms ofinternal density and external openness are the most conducive to sustainable regional develop-ment. Similar to multinational firms (with R&D facilities at different locations), that face atradeoff between internal (within the firm) and external proximity (Blanc and Sierra 1999), wemight expect RIS to perform best when comprised of a certain specific mix of internal interac-tion and external relations.1 In addition to these relational factors, the literature discusses anumber of region-specific factors that may influence RIS performance, such as location withregard to other regions (Andersson and Karlsson 2004), size of the region and its degree ofagglomeration, qualification of the regional workforce, endowment with universities and otherpublic research organizations, the innovative milieu, regional industry specialization, etc.(clustering; Fritsch and Slavtchev 2010).

To summarize the argument, the innovation performance of a RIS may be particularlyshaped by region-specific factors such as the level and structure of regional interaction, as wellas by relationships with external actors, which may indicate the openness of a region (Graf2011). In a nutshell, the systemic view may be boiled down to the hypothesis that the level andquality of the division of innovative labour have an important positive effect on the level andsuccess of innovation activity and, therefore, on the performance of RIS. Hence, one may expectthat tightly knit regional networks and the integration of local actors into global knowledgeflows will create an excellent environment for an effective RIS.

There are several empirical studies on the region-specific determinants of RIS performance,but the effects of the general framework conditions, as well as the importance of the surroundingspatial environment, are more or less unexplored territory. We thus know nearly nothing aboutthe relative importance of such environmental factors for the performance of RIS and the role

1 This proposition may be regarded as in line with the lively Burt-Coleman debate in organization research. Coleman(1988) argues that the benefits of dense networks arise through increasing trust, whereas Burt (1992, 2004) emphasizesthe importance of structural holes for the acquisition of diverse external knowledge. The validity of these argumentsseems to depend on the direction of firms’ search processes, with structural holes being more important in R&D aimedat new technologies for radical innovation (exploration) and density more important in the context of R&D based on agiven technology aiming at incremental innovation (exploitation; Rowley, Behrens, and Krackhardt 2000). Provided thatRIS are comprised of actors that follow quite heterogeneous search paths, it is plausible to assume that both types ofactivity will be present within a region. Since we have no information on the degree to which one of the two types ofinnovative activity prevails in the RIS under inspection, we are unable to account for these aspects in our empiricalanalysis.

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they play in the effectiveness of region-specific determinants.2 For example, how do specificinstitutional settings affect knowledge transfer from universities and other public researchorganizations to the private sector? How does general macroeconomic prosperity affect theperformance of RIS as compared to either a decline in prosperity or a high level of turbulenceas occurred in East Germany during the 1990s?

In this study, we compare a number of key characteristics of systemic innovation processesbetween four case study regions. We focus on the relations between innovative actors (firms,public research institutions and individuals), the resulting regional innovation networks, and onlinks to actors external to the respective region. This information allows us to assess the systemicproperties of the four RIS and to derive expectations about their relative performance. Com-paring these results with the actual level of regional innovation activity leads to conclusionsabout the relative importance of a region’s characteristics and its more macroeconomicenvironment.

3 Historical background: The two German innovation systems

There are two reasons why the environment for innovation may differ between East and WestGermany. First, for more than 40 years, East Germany was a socialist regime characterized bya substantially different institutional framework, different macroeconomic conditions, and, mostparticularly, a different organization of innovation processes compared to West Germany. This‘natural experiment’ left a substantial imprint on the East German RIS. Second, beginning in1990, East Germany experienced a turbulent transformation process to a market economicsystem (Brezinski and Fritsch 1995), the effects of which are still clearly noticeable 20 yearslater and that created an environment for innovation activity quite distinct from the one prevail-ing in the West (Fritsch 2004; Kronthaler 2005). Hence, one may expect to find that thesedifferent general conditions will have a considerable impact on the performance of RIS in bothparts of the country.

Until 1945, the end of the Second World War, the national framework conditions in what istoday’s Germany were identical. Right after the end of the war, the country was divided into fourzones, each governed by one of the allied powers. In 1949, the Soviet zone became the GermanDemocratic Republic (GDR), commonly referred to as East Germany; the other occupationzones became the Federal Republic of Germany (FRG), or West Germany. The FRG was set upas a capitalistic market economy and soon experienced vigorous economic recovery. In contrast,the GDR, East Germany, became a socialist-type centrally planned economy with an innovationsystem much like the Soviet model. Specifically, the innovation system in East Germany wascharacterized by a close orientation towards the linear model of the innovation process andpronounced bureaucratic steering (Fritsch and Werker 1999; Hanson and Pavitt 1987).

In 1961, the East German government instituted a border regime that more or less com-pletely separated East Germany from the West and made any uncontrolled transfer of people,goods and resources nearly impossible. In the course of these developments, innovation activi-ties in East Germany were largely cut off from those in the West. The East German governmentonly rarely allowed Eastern scientists to travel to the West or communicate with Westerncolleagues. Innovation in the East was also hampered by the fact that the Western bloc imposedan embargo on certain goods (e.g., modern machinery, software), into East Germany (Kogut andZander 2000). In 1990, the socialist regime in East Germany ended rather abruptly and both

2 A number of studies investigate the effect of different characteristics of national innovation systems on theirperformance (Hall and Soskice 2001; Dosi et al. 2006), but largely ignore regional conditions below the level of thenation-state.

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parts of the country were once again united, with a common currency and the rapid creation ofan identical framework of formal institutions. Adjustment to these new conditions, however, wasnot nearly so rapid and is by no means complete even today, 20 years later.

4 Regional innovation systems compared: Dresden and Jena vs. Aachen and Karlsruhe

4.1 Selection of case study regions

Dresden and Jena are two East German regions that perform relatively well despite the chal-lenges posed by the East German innovation system described above. At the turn of themillennium, 10 years after the transformation process began; they were the two East Germanlighthouses of innovation in terms of level of innovation activity as well as efficiency of theirinnovation systems (Fritsch and Slavtchev 2011). However, they still considerably laggedbehind the West German level of innovation. For our analysis, we employ a matched-pairsapproach and compare Dresden and Jena with two regions of about similar size and populationdensity in the West that are characterized by relatively high efficiency of their RIS.3 Comparablesize and population density are required because innovation theory, as well as empiricalresearch, stresses the importance of agglomeration economies for innovation activity (Feldmanand Audretsch 1999). The regions also had to have a renowned research university as well as anumber of other public research organizations, such as institutes of the Fraunhofer and the MaxPlanck Society. According to these criteria, we chose Aachen and Karlsruhe for the comparison.

All four case study regions are defined as German planning regions (Raumordnungsre-gionen). To represent functional entities, planning regions normally comprise several NUTS3-level districts,4 namely, a core city and its surrounding area. While districts are administrativeregions, planning regions are more often used for spatial analysis and policy development,particularly regarding public infrastructure. Planning regions tend to be somewhat larger thanlabour market regions or travel-to-work areas. We consider planning regions as more suitablethan districts for an analysis of RIS for two reasons. First, a single district, particularly a corecity, is probably too small to include the most important sites of innovation-related localinteraction. The second reason is of a methodological nature: since patents are assigned to theresidence of the inventor, taking just a core-city as a region would lead to an underestimation ofpatenting activity since many inventors have their private residence in surrounding districts.5

Figure 1 shows the location of the four case study regions. Aachen and Karlsruhe are situatedclose to other regions with a high level of innovation activity (e.g., Bonn and Cologne in the caseof Aachen; Stuttgart and Mannheim in the case of Karlsruhe), but the two East German regionsare more isolated in this respect.6 This is particularly true of Jena, which represents a ‘cathedralin the desert’ even within its planning region (Graf 2006).

3 Wanting to achieve comparability with regard to size and population density means that the two West German RISwith the highest levels of innovation efficiency, Munich and Stuttgart, were not selected because they are much largerthan the two East German regions.

4 NUTS is an abbreviation of Nomenclature des Unités Territoriales Statistiques. This regional definition wasestablished by Eurostat more than 30 years ago to provide a single uniform breakdown of territorial units for theproduction of regional statistics for the European Union; see URL: http://europa.eu.int/comm/eurostat/ramon/nuts/introduction regions en.html.

5 A number of studies chose larger spatial units such as whole German Federal States for their analysis of RIS (see,e.g., the contributions in Cooke et al. 2004). Given the considerable differences in the efficiency of RIS (Fritsch andSlavtchev 2011), we believe that planning regions are more appropriate for such an analysis. This does, however, in noway mean that the wider spatial environment of a planning region is deemed to be irrelevant for innovation performanceof planning regions. In fact, our analysis leads us to the conclusion that this wider spatial environment may have aconsiderable effect.

6 The cities in Figure 1 are represented by the respective city regions, which reflect their geographic size.

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4.2 Characteristics and general performance of case study regions

The size of the four case study regions ranges from nearly 800,000 inhabitants in the Jena regionto about 1,250,000 inhabitants in the Aachen region (Table 1). All four regions have a longtradition in manufacturing industries: electronics and mechanical engineering in Dresden; opticsand precision mechanics in Jena; electronics and electrical engineering in Aachen;7 and elec-trical engineering, mechanical engineering, and vehicles construction in Karlsruhe. The fact thatthe two East German regions have a much smaller establishment size in the manufacturingsector is probably a result of the transformation process, during which large entities weresplit-up, often followed by further employment decline due to unfavourable economic perfor-

7 The Aachen region has experienced a considerable shift from coal mining to more manufacturing industries sincethe 1970s. In the period of our analysis, the mining sector no longer played an important role in the region’s economy.The economies of the other three case study regions have not undergone such a dramatic change in their sector structure.

Fig. 1. The case study regions

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mance. Moreover, many East German establishments are small because they are relativelyyoung, having been set up only after German Reunification. The higher start-up rates in the twoEast German regions reflect an adjustment to the level of entrepreneurship in the West Germanpart. The share of R&D employees8 is considerably lower in the Eastern regions but both regionsshow a relatively high share of employees with a tertiary degree.

The amount of third-party funds per professor may indicate several things. First, sinceexternal funds are predominantly allocated by means of highly competitive procedures, theamount of third-party funds per professor can be regarded as an indicator of research quality.This is particularly true of funds from the German Science Foundation (DFG), which aredesignated for basic research. Funds from private firms signify university-industry linkages thatmay result in significant knowledge spillovers.9 An important difference between the twoEastern and the two Western regions is the lower level of third-party funds per professor in theEast. Since departments of engineering and natural sciences tend to have the highest levels ofexternal funding, we restrict this indicator to these departments only. Aachen is the clear leaderwith respect to this indicator, Karlsruhe and Dresden take the middle position, and Jena lagsbehind, with less than 30 percent of Aachen’s level of funding.

The two East German regions are well equipped with non-university public research insti-tutes of the Max Planck Society, which focus on basic research, and of the Fraunhofer Society,which have the mandate of transferring results of basic research to private-sector innovators.

8 Employees are classified as working in R&D if they have a tertiary degree and work as engineers or naturalscientists.

9 Although we have no information about the location of the respective private firms, we know from other studies(e.g., Fritsch and Schwirten 1999) that industry-university co-operation tends to be concentrated in the university’svicinity.

Table 1. Innovation and performance indicators for case study regions (ROR level)

East Germany West Germany

Dresden Jena Aachen Karlsruhe

Number of population 1,032,659 788,236 1,247,270 1,087,776Number of employees (private sector) 289,647 198,501 271,232 324,759Average establishment size (number of employees) overall 7.43 6.86 7.87 9.68Average establishment size (number of employees) in manufacturing 14.25 14.80 19.32 24.45Average establishment size (number of employees) in services 5.52 4.37 4.77 5.66Share of employees in manufacturing in total private-sector

employment25.91 30.13 37.57 42.46

Start-up rate private sector 7.17 7.78 7.05 5.50Share of R&D employees 3.16 2.44 3.69 3.98Share of employees with tertiary degree 12.65 10.83 7.83 8.07Third-party funds per professor (in 1,000 €)a 72.92 39.73 169.25 109.11Third-party funds from private firms per professor (in 1,000 €)a 14.49 5.56 169.25 35.18Third-party funds from German Science Foundation (DFG) per

professor (in 1,000 €)a

17.75 16.89 43.85 36.89

Third-party funds per professor (in 1,000 €) in departments ofengineering and natural sciences onlya

119.81 66.33 234.99 131.20

Number of Fraunhofer Institutesb 10 1 3 2Number of Max Planck Institutesb 3 3 0 0Patents of private firms per 1,000 employees 1995-2001 0.77 0.58 1.37 1.44Patents of private firms per 1,000 R&D employees 1995–2001 21.63 22.86 46.11 38.92Efficiency of the RIS 1995–2000 (Fritsch and Slavtchev 2011) 0.354 0.394 0.769 0.613

Notes: a Private universities and university hospitals excluded.b As of 2008.

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Dresden, in particular, is home to the remarkably high number of 10 institutes of the FraunhoferSociety. Using patents as a measure of innovation output, the two West German regions performmuch better than their Eastern counterparts. This becomes particularly clear if one takes thenumber of patents per R&D employee as an indicator, which can be viewed as a measure of theproductivity of R&D activity. Estimates of the efficiency of German RIS in the 1995-2000period by Fritsch and Slavtchev (2011) reveal a much better performance of Aachen (0.769) andKarlsruhe (0.613) compared to Dresden (0.354) and Jena (0.394).10

This first inspection of innovative resources and performance in the four case study regionsreveals the impact of a socialist heritage and the subsequent transformation process in the twoEast German regions. All four regions are similar with regard to the prerequisites for innovationactivity on the resource side, but the two West German regions clearly perform better. Followingthe arguments of the RIS approach (see Section 2), one should expect to observe a higherinteraction intensity in Aachen and Karlsruhe as compared to their East German counterparts. Inthe following section, we analyse the networks of inventors in the four regions in order to testthis proposition.

5 Regional networks of inventors

The innovation systems approach suggests that the division of innovative labour is of crucialimportance for innovation performance (Lundvall and Johnson 1994; Capello and Faggian2005; Malmberg and Maskell 2006). For our empirical study, we operationalize the concept ofdivision of innovative labour by looking at the structure of interaction within regional networksof innovators.

5.1 Method: Social network analysis and patent data

An empirical analysis of social networks requires relational data on the vast majority of theactors constituting the system. Patent data meet this requirement as they reveal informationabout the persons involved in the underlying innovative activity (the inventors), and the patentapplicants (firms, individuals, research organizations), who own the rights to exploit the inven-tion (the innovators11). The data are publicly accessible, consistent, and complete in the sensethat any innovative effort that was judged worth a patent application is included. Since a patentapplication tends to represent a certain minimum standard of newness of an invention and of therespective R&D, the quality of the links between actors that we identify on the basis of patentstatistics should be comparable across regions. Of course, patent data are not perfect and havesome widely acknowledged flaws. Most importantly, there are alternative mechanisms of appro-priating the returns to innovative activity (Cohen et al. 2000), and the propensity to patent variessubstantially between sectors (Arundel and Kabla 1998; Mairesse and Mohnen 2003). Weovercome this last problem by selecting regions that specialize in manufacturing industries inwhich patenting is presumed to be of similar importance.

10 These estimates are based on a knowledge production function with the number of patents as R&D output and thenumber of R&D employees as R&D input; for details, see Fritsch and Slavtchev (2011). A value of 0.769 in the case ofAachen means that this RIS reaches 76.9 percent of the value for the RIS with the highest R&D productivity. Dresdenand Jena reach only 35.4 percent and 39.4 percent, respectively, of that level.

11 We use the term ‘innovator’ instead of the more formal term ‘applicant’, since the main reason for patenting is toprevent copying of a successful innovation. Of course, not all patented inventions lead to marketable products or processinnovations.

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Methodically, patent networks can be constructed by relating patent applicants (innovators)to the respective inventors. Generally, it seems more plausible to assume knowledge flowsbetween individuals who know each other from joint research projects rather than betweenpatent applicants. Therefore, it is common practice in the analysis of innovation networks basedon patent data to link the inventors directly (Balconi et al. 2004; Singh 2005; Fleming et al.2007); however, these connections can also be used to identify channels of knowledge trans-mission between the innovators by linking them via common inventors (Cantner and Graf 2006;Graf and Henning 2009; Breschi and Lissoni 2009). We follow the latter approach because itseems more appropriate to assume organizations (innovators) as constituents of the regionalsystem.12

Our analysis of the networks of innovators is based on patent applications at the GermanPatent Office that were disclosed between 1995 and 2001. To assess inventive activity in aregion, the first best solution would be to use the address of the research lab where the R&D wasperformed. Unfortunately, patent statistics do not provide this information. Using the applicant’saddress does not help in this respect because a number of (particularly large) firms have severaland regionally dispersed subsidiaries in which the research may have been conducted, but applyfor patents centrally on behalf of headquarters. The common solution to this difficulty is to useinventor residence instead of that of the innovator, under the assumption that people normallylive fairly near where they work (Jaffe et al. 1993; Breschi and Lissoni 2009). Consequently, webase the regional networks on all patent applications with at least one inventor residing in therespective region. Innovators are defined as external to the region if they have applied for at leastone patent naming an inventor living in the focal region, but are themselves not located in thatregion.13

We assume two innovators to be related if at least one inventor has developed a patent forboth innovators. In other words, a relation is established between innovators A and B if we findthe same inventor named on a patent applied for by A and on a patent applied for by B. Thereare two ways this could occur:

1. First, the innovators jointly apply for a certain patent. In this case, we assume a previousresearch co-operation and there are as many linkages between all co-applying innovators asthere are inventors.

2. Second, the same inventor is named on two distinct patent applications submitted by differentinnovators. In this case, we assume mobility of the inventor between the innovators.

Both types of linkages are based on the concept of knowledge transfer via personal relationships(e.g., Almeida and Kogut 1999). The main idea is that organizations, namely, firms or researchinstitutes, interact via scientists who know each other either from working on joint projects(co-operation) or as they move from one organization to another (mobility). Of course, mobilitydoes not only encompass individuals changing jobs between existing organizations but alsocaptures spin-off processes in which new entities are formed by employees of incumbents.

During the period under analysis, German patent law allowed university professors to patentunder their own names instead of under the name of the university, and thus the number ofuniversity patent applications is underestimated in our data. The number of patent applicationsfrom public research is further underestimated because universities may trade intellectualproperty rights for financial support in university-industry co-operation projects, that is, a

12 Hence, relationships between inventors within the same institution are not explicitly accounted for, but a prelimi-nary study showed a close correspondence of network structures between the two approaches.

13 To correct for the misleading effects of headquarter patenting, the list of these (presumed) external actors wascarefully checked to allocate them correctly whenever they could be identified as a local (and therefore internal)subsidiary.

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private firm sponsors the research carried out in the university’s lab in exchange for the exclusiveright to patent the invention. This means that not only will public research patent activity beunderestimated but, even more importantly, a number of university-industry co-operationsleading to patent output will not be identified as co-operative activity.

5.2 Overall structure of inventor networks

Looking at the number of patent applications in each region reveals that actors in Aachen andKarlsruhe filed many more patents than actors in Jena or Dresden, with the number of patentapplications in Karlsruhe being almost triple that of Jena (Table 2). In terms of the number ofapplicants (the network actors), the differences are not quite as pronounced, with Aachen andKarlsruhe having roughly twice as many applicants as Jena. The number of patent applicationsper actor (Dresden 3.21 patents per applicant, Jena 3.02, Aachen 3.46, Karlsruhe 4.16), isprobably a result of the smaller average size and corresponding lower levels of R&D of actorsin the two East German regions. Another significant difference between the East German andWest German regions is the relative importance of patent applications by public researchinstitutions. In Dresden and Jena, roughly every fourth patent was filed by a university or otherpublic research institute; this share is only 9 percent in Karlsruhe and 15 percent in Aachen.14

In Table 2 we also present variables that measure the structure and intensity of interactionwithin the regional networks.15 Under the assumption that knowledge can only ‘flow’ betweenactors who are linked either directly or indirectly, the largest connected part, the networks maincomponent,16 should indicate the share of actors that have (potential) access to the bulk of localknowledge. It is remarkable, and in sharp contrast to our initial proposition, that the inventornetworks in the two East German regions are much more integrated than those in the two WestGerman regions. In Dresden, as well as in Jena, the share of actors in the main component ismuch higher and the share of isolated actors, that is, those who have no connections with otheractors, is much lower than in Aachen and Karlsruhe (Table 2). Since the actors in the two EastGerman networks have a larger average number of links to other actors (mean degree17), thenetwork density, namely, the share of realized links over all possible links, is also higher in theEastern regions.18 The considerably greater number of relationships in the two East German RISholds for both types of links, those based on co-operation and those related to inventor mobility.While a higher level of mobility links in the East German RIS could have been expected as aresult of the turbulent transformation process during which relatively many persons had tochange employers,19 the higher number of co-operative links is surprising, given the disruptiveeffects of the transformation process on personal ties and networks (Albach 1994).

14 Public research entities include universities and technical colleges (Fachhochschulen), as well as non-universitypublicly funded scientific institutes. The latter are in most cases members of one of large German scientific institutions:the Max Planck Society, the Leibniz Association, or the Fraunhofer Society.

15 For details on the calculation of network statistics, see Wassermann and Faust (1994).16 A network component is defined as a subset of all network nodes that are directly or indirectly connected.17 Mean degree is reported for valued and binary versions of the networks. The valued network accounts for the

intensity of relations in terms of the number of common inventors, while the latter is solely based on the number ofconnections the actors have.

18 If g is the size of the network as measured by the number of actors and di is the degree, i.e., the number of connectionsof actor i (i = 1, . . . , g), then the density d of the network is defined as the number of all active linkages divided by thenumber of possible linkages within the network D d g gi

gi= ∑ −( )=1

2 . The density measure is somewhat problematicwhen comparing networks of different size as the number of possible links increases geometrically while the actualnumber of links usually does not since inventors are constrained in their capacity to have contact with other actors.

19 The share of mobility links over all links ranges from 49.2 percent in Karlsruhe to 37.4 percent in Dresden(Table 2). The figures do not indicate a more pronounced role for mobility in the East as might have been expected asa result of the East German transformation process.

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Table 2. Characteristics of the inventor networks in case study regions

East Germany West Germany

Dresden Jena Aachen Karlsruhe

Number of patents 3,720 2,094 5,508 6,072by type of applicant (%)- individual 19.2 18.8 25.5 19.5- public 23.7 25.7 14.8 8.7- firm 57.2 55.5 59.7 71.8by location of applicant (%)- same region 70.3 75.0 65.8 66.8- same Federal State 7.2 3.2 21.9 18.8- rest of Germany 21.4 21.2 10.2 11.9- abroad 1.0 0.7 2.1 2.4Number of actors 1,158 694 1,591 1,460by type (%)- individual 40.2 38.3 50.0 46.7- public 5.6 8.6 1.8 2.2- firm 54.2 53.0 48.2 51.1by location (%)- same region 51.8 56.2 61.2 60.2- same Federal State 11.1 6.3 17.9 16.8- rest of Germany 35.1 35.9 16.4 19.0- abroad 2.0 1.6 4.5 4.0Total Number of linkagesa 4,106 3,614 4,036 3,754- share internal (%) 30.8 42.4 49.3 25.6- share external (%) 69.2 57.6 50.7 74.4Number of co-operation linkagesa 2,570 2,100 2,374 1,906- share internal (%) 31.0 41.7 50.5 26.7- share external (%) 69.0 58.3 49.5 73.3Number of mobility linkagesa 1,536 1,514 1,662 1,848- share internal (%) 30.5 43.3 47.7 24.5- share external (%) 69.5 56.7 52.3 75.5Share of mobility linkages (%) 37.4 41.9 41.2 49.2Network measuresNumber of components 549 309 910 875Size of main component 359 259 254 344Share in main component (%) 31.0 37.3 16.0 23.6Share of isolates (%) 35.1 32.6 42.5 48.3Centralizationb 0.094 0.115 0.022 0.046Density (valued) (%) 0.44 0.92 0.21 0.25Density (binary) (%) 0.19 0.39 0.10 0.11Mean degree (valued) 5.069 6.383 3.382 3.627Mean degree (binary) 2.225 2.689 1.612 1.604Average distance within main component 3.374 3.103 4.423 4.032Co-operationMean degree (valued) 3.021 3.839 1.898 1.879Mean degree (binary) 0.805 0.896 0.569 0.518MobilityMean degree (valued) 2.048 2.545 1.483 1.748Mean degree (binary) 1.435 1.830 1.046 1.101

Notes: a Only relations with at least one internal actor involved.b Based on degree centrality.

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The higher average number of co-operative links observed in the two East German RIS maybe an artifact of the more open attitude toward R&D co-operation under its socialist regime.Obviously, many of the relationships established under the past system proved stable enough tosurvive the radical reorganization of the East German RIS caused by the transition.20 If wedistinguish between the two types of linkages, co-operation and mobility, we do not observe muchof a difference with respect to the shares of internal and external relations. In addition to the higherlevel of interaction in the two Eastern regions, there is another notable structural differencebetween the two parts of the country: the networks in Dresden and Jena are far more centralized21

than those in the West, that is, linkages are more concentrated between few key actors.The higher degree of integration in the two East German regions holds not only for the

networks as a whole but also for their main components (Figure 2). The networks in EastGermany seem to be more tightly knit than their West German counterparts. Accordingly, theaverage distance between actors within the main component22 is also smaller in the two EastGerman regions. Especially when comparing Jena and Aachen, we observe a dense pattern ofrelationships with a large number of central actors in Jena, whereas in Aachen, the networkappears less dense, with no easily identifiable centre. The main component of the inventornetwork in Dresden is clearly dominated by two public research actors (the Technical Universityof Dresden and the Fraunhofer Society), that have by far the highest number of linkages. In theother regions, public research actors do not play such a dominant role.

5.3 Internal vs. external relations

According to the RIS approach, the functioning of a regional innovation system depends oninteraction within the region and on the connections regional actors have with the ‘outer world’of external knowledge sources (Bathelt et al. 2004; Graf 2011). As such, we could expect to finddifferences in the openness and geographic reach of regional networks that may explain thedifferences in performance. We use a number of measures to analyse the importance of extra-local linkages (Table 2). Since the regional networks are defined based on patents naming atleast one inventor located in the respective region, applicants might be from different locations.This could be due to commuting inventors, inventors working for firms that have their head-quarters in a different region, or co-applications by actors from different localities. Analysingthe number of patents by location of the patent applicant, we find that between two-thirds andthree-quarters of all patents are from local applicants, with the highest share in Jena and thelowest share in Aachen. Counting only actors not their patents, the share of locals is lower in allregions, which is not surprising since we account for all patents by locals, but only for thatfraction of patents by external actors that are invented in collaboration with local inventors.

A rather interesting observation from both measures (patents and applicants), is the differentdegree of integration of the regions into their surrounding space. The two West German regionshave a much larger share of links to actors located within the same Federal State,23 indicating

20 Another explanation could be that the higher share of East German firms with R&D co-operation is motivated bygreater scarcity of resources, which may result from their average relatively poor economic performance. There havebeen numerous policy programmes aimed at promoting R&D co-operation among East German firms but most of thesewere not implemented until the late 1990s, at the end of our period of analysis, and, therefore, cannot have resulted inpatent applications.

21 Network centralization is given by CC i C i

gig

D D=∑ ( ) −( )

−( )=1

2

max, where Cd(i) is the normalized degree centrality.

22 The average distance is the mean distance in terms of number of actors between any two actors in a component.23 Federal States are an important level of administration and policy in Germany. We do not investigate relationships

with actors in the surrounding planning region because this would mean including regions in other countries (for thecases of Aachen, Dresden and Karlsruhe), for which comparable information is missing, or regions in West Germany

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that they have a considerably higher level of interaction with neighbouring regions than do theirEast German counterparts, which appear to be almost isolated spots in their surrounding spatialenvironment. The two West German regions also seem better integrated into internationalknowledge flows, having much higher shares of actors that are located abroad.

Having classified actors as either internal or external, we now analyse the relations betweenactors according to their location. Internal linkages are linkages between actors located within

(for the case of Jena). The results for actors in the same Federal State may be distorted by the fact that the two EastGerman states of Saxony (Dresden) and Thuringia (Jena) are much smaller than Baden-Württemberg (Karlsruhe) andNorth-Rhine-Westphalia (Aachen) in terms of population and economic activity so that opportunities to relate to otheractors in the same state are smaller in the East.

Fig. 2. Main components of regional networksNote: Actors located within the region (headquarter or subsidiary) are marked in grey/red (electronic version), externalactors are in black. Squares indicate a private actor; public research organizations are circles. The size of a node isproportional to the number of patents filed.

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the same region. External linkages are those between an internal and an external actor.24 Theoutward orientation, as measured by the share of external relationships, is highest in Karlsruheand lowest in Aachen, which is surprising given the large share of external actors in the Aachennetwork.

Analysing the number of external linkages of local actors gives a first impression of aregion’s integration into interregional knowledge flows. A large number of interregional link-ages, however, does not necessarily mean that the system can effectively integrate externalknowledge because it says nothing about how this knowledge is disseminated in the region.25

Therefore, we take a more micro perspective and identify those actors who play the role ofgatekeeper for the RIS by absorbing external knowledge and passing it on to local actors.

5.4 Gatekeeper

‘Gatekeepers’, that is, actors who are well integrated into global knowledge flows as well asrelated to regional actors, play a key role in connecting the RIS to the ‘outer world’ (Giuliani2005; Giuliani and Bell 2005; Graf 2011). Gatekeepers serve two functions in a regionalinnovation system: external knowledge sourcing and diffusion of knowledge inside the localsystem, thereby acting as knowledge brokers (Giuliani 2005; Wink 2008).

To identify regional gatekeepers, we plot the actors in each network according to the numberof their internal and external relations (Figure 3). The four parts of Figure 3 are scaled identi-cally to make them comparable. Private actors (firms and individuals) are represented bysquares; public research institutes and universities are shown as circles. The size of the symbolreflects the number of patent applications submitted by that actor. For most actors, internal andexternal contacts seem to go hand in hand, but with different intensities. Actors in Jena appearmore inward oriented than those in Dresden, Aachen, and especially, Karlsruhe. According toour gatekeeper index, defined as a brokering position between internal and external actors(Gould and Fernandez 1989), gate-keeping activity is strongly concentrated in a few innovators(see Table A1 in the Appendix). For example, in Dresden, the two actors with the highestnumbers of relationships, the Technical University of Dresden and the Institutes of the Fraun-hofer Society, score 30 and 10 times higher, respectively, than the third actor on the list. InKarlsruhe, the top gatekeeper (Forschungszentrum Karlsruhe) has a score three times higherthan that of the runner-up (Bosch) and 10 times higher than the local university (TechnicalUniversity of Karlsruhe). In Aachen, the same distributional characteristics are seen, but at alower level, while in Jena, the gatekeeper function seems to be performed by a variety of actors.

Common to all regions is that the local university and large research institutes, such as theFraunhofer Institutes, are at the top of the gatekeeper index (Table A1 in the Appendix). Certainprivate firms, such as Carl-Zeiss in Jena and Bosch in Karlsruhe, also act as gatekeepers.

5.5 Dynamic perspectives

The analysis in the previous sections covered a seven-year period. The advantage of such a longtime period is that the network structures are very visible; a disadvantage is that changes in thisstructure over time cannot be analysed. To investigate the dynamics of regional innovationnetworks, we now divide the observation period into three overlapping sub-periods, 1995–1997,

24 Links between external actors are not considered here as they have little to do with the regional network.25 It might well be the case that some actors hold the bulk of external relations but are not sufficiently integrated into

the RIS to transfer the external knowledge to other actors in the system.

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1997–1999 and 1999–2001. Networks constructed for shorter time periods tend to be muchsmaller than networks for longer time periods because of the smaller number of patent appli-cations filed and the consequently fewer links.

Table A2 in the Appendix presents network statistics for the four regions in each sub-period.Figure 4 shows development of the network components. Selected network indicators areillustrated in Figure 5. First, all networks increase in size and with regard to the number of actorsin the main component. This increase is not only in absolute terms but also with regard to theshare of actors within the main component. At the same time, the share of isolates is decreasing,which is in line with the general tendency in science toward increasing collaboration and largerteams (Wuchty et al. 2007). There are differences between the regions with regard to the averagenumber of links per actor (mean degree) and the level of network centralization. For example,there is a sharp increase of the mean degree in Jena and, to a lesser extent, also in Dresden, butthe values for the two West German regions, Aachen and Karlsruhe, remain more or lessconstant. Both East German regions also show a strong tendency toward increasing centraliza-tion of the network, which is far less pronounced in Aachen and Karlsruhe. Jena is the only casestudy region where the average distance within the main component is decreasing, indicating an

1 2 5 10 20 50 100 200

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Fig. 3. Gatekeepers in the regional networks of innovators

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increasing degree of integration of this part of the network. While no clear trend in this respectcan be found for Dresden and Aachen, we see an increase of the average distance within themain component of the Karlsruhe region, indicating disintegration.

In all four regions there is a considerable increase in the number of relations to externalactors (Figure 6). The highest level of dynamics in this respect occurs in the two East German

95−97 97−99 99−01 95−97 97−99 99−01 95−97 97−99 99−01 95−97 97−99 99−01

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Fig. 4. Component distribution in the four networks in three subperiods

Fig. 5. Dynamics of the regional networks

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regions: in Jena, the number of external relations almost doubled from 520 to 956 and inDresden it increased by about 50 percent, whereas the increase in both Aachen and Karlsruhewas about 25 percent. Development in the share of external relations is most pronounced inDresden, where the share of external relations rose from 60 percent in the mid 1990s to 75percent by the turn of the millennium. There is a slight decrease in the share of external relationsin Jena, and a small increase in Aachen and Karlsruhe.

6 The subsequent performance of the RIS

It is plausible to assume that main effects of the quality of a RIS on its performance do notbecome immediately apparent, but occur with a considerable time-lag. It is therefore of interestto compare the performance of the four RIS in the subsequent period. We have already shownthat during the years 1995–2001, the two East German RIS had a much lower efficiency thantheir West German counterparts (see Table 1, based on Fritsch and Slavtchev 2011). In terms ofpatents of private firms per 10,000 employees or per 1,000 R&D employees, neither EastGerman region reached much more than about half the values of Aachen and Karlsruhe(Table 3). Between this first period and the subsequent period of 2002–2005, we find muchhigher growth rates in the number of patents per employee or per R&D employee in the two EastGerman regions, indicating considerable convergence in the levels of regional patent produc-tivity. Notwithstanding these relatively high growth rates, in many respects the two East Germanregions had not yet attained the levels achieved by the West German regions. An exemption isthe number of patents of private firms per 1,000 employees, where Dresden reached the level ofthe Aachen region in the 2002–2005 period.

It would be well in line with the systemic view of innovation processes to assume that at leasta part of the relatively high growth rates in patent productivity in the two East German RISduring the 2002–2005 period resulted from the rather advantageous characteristics of theirnetworks, that is, the higher intensity of interaction and corresponding knowledge flows. Evenif we cannot rule out that other factors may also have played an important role in this respect,

95−9797−9999−01

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0.45 0.50 0.55 0.60 0.65 0.70 0.75

Share of external relations

8428981250

520762956

694736864

9568621194

Abs.

Fig. 6. Development of external orientation

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the characteristics of the two East German RIS that we found suggest further strong improve-ments in the performance of the two East German RIS.

7 Discussion

Our comparative analysis has clearly shown that RIS are in no way islands but that regionalinnovation processes are to a considerable degree dependent on their wider spatial environmentand the governing macroeconomic conditions. We found that even though the two leading RISin the eastern part of Germany showed a much larger degree of interaction than two roughlycomparable RIS in West Germany, the latter were clearly more efficient in terms of patentingand innovation.

At least at first sight, this result is in sharp contrast to the typical argumentation of RISstudies that proclaim unequivocal benefits from networking. We have three possible explana-tions for our observations that should add to the understanding of RIS. A first explanation for therelatively poor performance of the East German RIS is based in the ongoing transformation ofthe East German economy during the period of analysis. Obviously, innovation processes inDresden and Jena have - to a considerable degree - been hampered by economic problems andthe resulting reorganization of the East German economy. This clearly indicates that macroeco-nomic conditions at the sub-national level can play an important role in the performance of RIS.A second explanation may be provided by the different degrees of embeddedness of the RIS intheir proximate geographic environment. The two East German RIS, Dresden and, particularly,Jena, are ‘cathedrals in the desert’, whereas the two RIS in the West, Aachen and Karlsruhe, aremuch more embedded in their surrounding spatial environment and have a higher share ofrelationships to actors located abroad. Third, it may be argued that it is not the level, but thequality of co-operation that is decisive and that our findings may be biased by the fact that thequality of the co-operative links of actors in the two East German regions is systematically lowerthan the quality of the links enjoyed by their West German counterparts. One might particularlysuspect that in the East German case study regions, social aspects could have supersededeconomic imperatives (Uzzi 1997, p. 59), so that social proximity outweighs economic reason-

Table 3. Indicators for the development of patent productivity in the fourRIS under study

East Germany West Germany

Dresden Jena Aachen Karlsruhe

Patents of private firms per1,000 employees1995–2001

0.77 0.58 1.37 1.44

Patents of private firms per1,000 employees2002–2005

1.47 0.87 1.48 1.87

Change (%) 90.91 50.0 8.03 29.86Patents of private firms per

1,000 R&D employees1995–2001

21.63 22.86 46.11 38.92

Patents of private firms per1,000 R&D employees2002–2005

33.85 32.49 45.76 44.61

Change (%) 56.50 42.13 -0.07 14.62

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ing, leading to economically suboptimal collaboration decisions. Although we cannot com-pletely exclude such influences, we have no indication that this is the case, especially since it hasbeen found that these two regions outperform comparable regions in East Germany because oftheir intensity of interaction (Graf and Henning 2009). Moreover, because our network analysisis based on patent statistics, these links are all productive in that they have led to at least onepatent application. Nevertheless, it would be quite important to learn more about the quality ofcollaborative linkages and to assess its influence on the performance of the respective RIS.

We conclude that focusing on a single region without accounting for geographic embed-dedness and general macroeconomic conditions is not a sufficient approach to explaining RISperformance. This is particularly true when comparing RIS in different countries. There is nodoubt that regions do differ with regard to their innovation performance and that regionalconditions play an important role in explaining such differences, but this insight should notresult in ignoring the effect that the wider spatial environment, particularly the national inno-vation system in which the regions are embedded, has on their performance. We therefore agreewith Lundvall’s (2007, p. 100) claim that investigations of regional, sectoral, and globalinnovation systems “have important contributions to make to the general understanding ofinnovation” but “are not alternatives to the analysis of national systems”.

To the degree that innovation activity benefits from co-operative links, division of innovativelabour, and networking, the two East German regions in our sample are on the right track andhave good prospects to perform equally well or even better than their West German counterpartsin the future. This can be particularly expected after the phasing-out of the East Germantransformation process and a general economic recovery in this part of the country. Our analysesalso show, however, that such a process will take a considerable amount of time, possiblydecades, before the effect of conducive regional conditions has fully developed.

An important limitation of our comparative analysis is the small number of cases investi-gated. Future research involving more cases would therefore be very helpful in painting a morecomprehensive picture of RIS. Another limitation is our reliance on patent data, consideringtheir well-known shortcomings. Work based on other kinds of information about regionalinnovation networks, such as non-patenting-related interaction, must ensure that the informationemployed is comparable across regions. In this respect, patent data have the advantage that theyreflect the same minimum standard of newness of an invention across all regions. Although ourcases were selected to match in various dimensions, we cannot completely rule out the possi-bility that our interregional comparison is to some degree affected by a technology bias in thesense that interregional differences in the level of co-operation may be due to the fact thatinteraction is more important in some technological fields than in others.

An important policy conclusion that can be drawn from our analysis is that R&Dco-operation and networks per se may not be sufficient to make RIS productive. The identifi-cation of other important factors that contribute to a well-functioning RIS is left for furtherresearch. Some of these factors, such as larger firm sizes in the West German regions that couldresult in scale effects in the production of knowledge, may also be found at the regional level.However, our analysis strongly indicates that these other factors will not be solely regionspecific, but should be looked for in the wider geographic environment and general economicconditions on a national as well as on a sub-national level.

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351Two systems

Papers in Regional Science, Volume 90 Number 2 June 2011.

App

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352 M. Fritsch, H. Graf

Papers in Regional Science, Volume 90 Number 2 June 2011.

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353Two systems

Papers in Regional Science, Volume 90 Number 2 June 2011.

Resumen. Comparamos dos sistemas de innovación regional (SIR) líderes en Alemania orientalcon dos SIR en Alemania occidental de aproximadamente el mismo tamaño y grado de aglom-eración. Nuestros análisis muestran que las diferencias entre regiones en cuant a su desempeñono pueden relacionarse fácilmente con las propiedades estructurales de las respectivas redes deinnovación debido al importante papel que juegan tanto los diferentes retos y condicionesmacroeconómicas de ambas partes del país, como las diferencias en integración de las regionesdentro del ambiente del espacio que las rodea. Nuestra conclusión es que cualquier análisis deSIR debería tener en cuenta no solo las condiciones económicas (sub) nacionales sino tambiénla posición de la región dentro del ambiente del espacio que la rodea: no es suficiente conestudiar únicamente la región.

doi:10.1111/j.1435-5957.2011.00364.x

© 2011 the author(s). Papers in Regional Science © 2011 RSAI. Published by Blackwell Publishing, 9600 Garsington Road,Oxford OX4 2DQ, UK and 350 Main Street, Malden MA 02148, USA.

Papers in Regional Science, Volume 90 Number 2 June 2011.