Accounting for core and extra-core relationships in technological systems: a methodological proposal

Research Policy 34 (2005) 83–100

Accounting for core and extra-core relationships in technological systems: a methodological proposal R. Leoncinia , S. Montresorb,∗ b

a Department of Economics, University of Bologna and CERIS/DSE-CNR, Milan, Italy Department of Economics, University of Bologna, Strada Maggiore 45, Bologna 40125, Italy

Received 1 January 2003; received in revised form 1 November 2004; accepted 11 November 2004 Available online 30 December 2004

Abstract The paper proposes a new intersectoral methodology to measure the core relationships of a technological system – that is the relationships of its private business core (PBC) – and to integrate them with a consistent analysis of its extra-core relationships – that is between the PBC and the public and the foreign sub-system. Although more demanding in terms of data, referring to sectoral R&D financing rather than to sectoral R&D expenditure, and differentiating the diffusion channels which approximate these relationships accordingly, turns out to be more accurate in methodological terms and more powerful in terms of applications, in particular as far as network analysis indicators are concerned. An illustrative application for 5 TS is provided with respect to 1985 and important insights are obtained about how to compare the structure of the different TS sub-systems. Although to be further explored in more extended and updated applications, these insights have to be considered as pros along with the cons the methodology we propose inevitably entails. © 2004 Elsevier B.V. All rights reserved. JEL classification: O33; 038; 039 Keywords: Technological system; R&D expenditure; R&D financing; Embodied and disembodied innovation diffusion

1. Introduction System analysis is becoming more and more an established methodology of analysis, given the evidence ∗ Corresponding author. Tel.: +39 051 2092651; fax: +39 051 237002. E-mail addresses: [email protected] (R. Leoncini), [email protected] (S. Montresor).

0048-7333/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.respol.2004.11.001

on the interactive and context-specific nature of the innovative process. Indeed, ‘systems of innovation’ and ‘technological systems’ are increasingly used as units of analysis of both positive (e.g. Edquist, 1997) and normative kinds of studies (e.g. OECD, 2001) on innovation. Among these, a powerful approach is for us represented by an intersectoral kind of analysis of the technological system (TS) which: (i) moves from a broad (techno-economic) notion of the TS; (ii)

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organises its constituent relationships into interactive sub-systems; (iii) measures them through suitable intersectoral innovation flows; (iv) identifies and compares the structure of the TS by means of network analysis indicators, structural linkages coefficients, pervasiveness/dependency ratios, just to mention a few (Leoncini and Montresor, 2003). This intersectoral approach to the TS has several interesting implications. The degree of synthesis of the results obtained from its application is for sure one of the most relevant. Indeed, different TS can be consistently compared and analysed over time, simply by looking at indicators which reflect several and complex co-evolution processes, simultaneously at work among the sub-systems of each TS. The ‘macro-stability’ of the TS which is caught by these indicators is, in fact, the result of the high ‘micro-turbulence’ which characterises its constituent sub-systems (Leoncini and Montresor, 2003). Although this kind of analysis is extremely relevant, identifying and comparing the synthetic structure of different TS is as important as ‘unfolding’ them, that is exploring the different processes and relationships they are made up of. Therefore, on the basis of this argument, it has been proposed (Leoncini et al., 1996) to integrate the analysis of ‘embodied’ intersectoral flows of R&D expenditure – taken as a proxy of the relationships which establish within ‘the private business core’ (PBC) of a TS – with: (i) technological balances of payments disaggregated by economic sector – taken as a proxy of the relationships occurring between the PBC and the foreign market; (ii) public R&D expenditure disaggregated by socio-economic objectives – taken as a proxy of the relationships occurring between the PBC and the institutional set-up of a TS.1 While 1 Following Leoncini (1998), we in fact consider the TS as the ‘host’ of variety generation and selection mechanisms which occur through the interactions of four sub-systems: the scientific core, where knowledge is created or possibly recombined; the technical systems constellation, where this knowledge gets translated into actual techniques; the market sub-system, which transforms such techniques into products and services then selected by both competition and final demand; the institutional set-up, meant as both a sub-system and as an interface allowing the relationships among the previous ones to occur. In such a framework, the PBC is a higher degree sub-system, identified by the technoeconomic flows which occur among the innovative core, the pro-

data availability makes this extended analysis relatively straightforward to be implemented on a casestudy comparative basis, when systematic analysis is carried out, crossing sectoral data for an appreciable number of TS drastically reduces the resulting dataset. This and other problems, which will be briefly recalled in the next section, can be somehow accommodated when the number of TS to be analysed is relatively low,2 as international statistics can be complemented by country-specific information to make them comparable. With a higher number of TS, and possibly over more periods, the cons instead grows and somehow obscure the pros. This fact motivates us to propose a new methodology to deal more systematically with core – i.e. within the PBC – and extra-core – i.e. between the PBC, the institutional set-up and the foreign market – relationships of a TS. In extreme synthesis, our proposal is that of ‘proxing’ the techno-economic relationships of a TS by referring to different kinds of R&D financing intersectoral flows, rather than to an ‘extended’ analysis of embodied R&D expenditure intersectoral flows. Although it is not free from limitations, this new methodology allows us to deal with more homogenous data and with a higher level of disaggregation, thus increasing the interpretative power of the indicators applied to investigate the structure of the TS (Section 3). On the other hand, because of the (still?) limited availability of sectoral data on R&D by source of financing, the chances to apply it in comparing different TS turn out to be quite narrow, both as far as the cross-sectional and the temporal coverage of the comparison are concerned. The ‘limited’ application that, because of data availability, we have been able to implement in this paper (Section 4), however, can serve to illustrate the interpretative power of our methodology. Obviously, should better data be made available in the future, the relevance in interpreting the recent development of globalisation and institutional change could be extremely important.

ductive/technical sub-system, and the internal market sub-system. The foreign market and the institutional set-up thus complete the picture. 2 For instance, in Leoncini et al. (1996), the application actually consists of the comparison between the Italian and the German TS, with respect to 1 year only (1988).

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2. The ‘extended’ analysis of R&D expenditure (embodied) intersectoral flows: pros and cons As argued elsewhere (Leoncini and Montresor, 2003), intersectoral innovation flows can be the starting point for a consistent empirical investigation of the main relationships which constitute a TS. More precisely, with some caveats, these relationships can be proxied by the flows of the R&D expenditure made by each economic sector of a TS and diffused to the others through the exchanges of intermediate commodities in which its innovative outcome is embodied. The distinction between embodied and disembodied technological flows is, in fact, one of the key issue in studying the innovative diffusion process (see, for example, OECD, 1992, p. 48). In synthesis, while the latter are exclusively due to the ‘public’ nature of (at least part of) the innovative knowledge which is produced by a TS, and to the related ‘pure-knowledge’ spillovers, embodied flows are more the expression of both its structure and functioning: mainly, but not exclusively, in terms of ‘rent-spillovers’, affected by the market structure of the TS sectors and by their technoeconomic distance, in addition to their innovative efforts (Leoncini and Montresor, 2003, pp. 49–52). On the basis of this argument, embodied innovation flows seem to us a more comprehensive proxy of the techno-economic, rather than of the purely technological, relationships of a TS. In which diffusion means innovation gets embodied and transmitted thus becomes the crucial issue to be addressed. Briefly, assuming that those innovations which are produced by a certain sector get embodied in the capital goods (typically machinery and plants) it produces, and hence transferred along with them to their acquiring sectors, appears the most relevant hypothesis.3 However, the construction of capital stock matrices to be used in implementing this hypothesis is still affected by serious problems which hamper its application in comparative kinds of studies.4 3 The standard ‘text-book’ example of an embodied intersectoral innovation flow is that of the innovation one firm, for example of the textile sector, acquires by buying from another firm, for example of the mechanical sector, an innovated machinery, for example, a more efficient mule. 4 For a detailed analysis of this problems, only part of which has been successfully solved, see Montresor (1992). For a review of the scanty temporal series of these capital stock matrices see also Costa and Marangoni (1995).

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Furthermore, resorting to investment flows matrices to proxy capital goods flows represents a ‘second-best’ solution which is no less problematic: sectoral investments are strongly affected by economic trends and cumulating data over time to make them less erratic is crucially dependent on the choice of the lag. Accordingly, embodiment in intersectoral flows of intermediate commodities become a more workable hypothesis. Given that these commodities enter in the production of final goods and services, the degree of abstraction is inevitably greater. However, it can be somehow reduced by considering innovation flows embodied in intermediate commodities referred to ‘vertically integrated’ rather than ‘pure’ economic sectors (Momigliano and Siniscalco, 1982): final demand, and thus also productive investments, are thus considered the enabler of both direct and indirect intersectoral innovation flows, so that the role of the capital formation process in driving the diffusion one is at lest indirectly retained (see Leoncini and Montresor, 2003, Chapter 3). With these caveats, R&D expenditure embodied intersectoral flows directly account for the relationships which constitute what could be considered the ‘private business core’ (PBC) of a TS. The PBC is actually made up of the firms which invest in innovation, obtain new products and processes, and spread their innovative results to other firms (mainly their suppliers and users) and to the final demand, both directly (in the form of spillovers) and indirectly (through some form of technology transfer). These flows account only indirectly for the relationships between the PBC and the other two building blocks of the TS: that is, the institutional set-up and the foreign market. Indeed, also public and foreign financing affect both the volume and the sectoral structure of embodied R&D expenditure. And this turns out to be quite important since making funds available to private innovation is one of the most important channels of interaction with the institutional set-up and the foreign market. Therefore, this analysis has the important advantage to allows us to refer to the TS as a whole: that is, as the result of the co-evolution of the different sub-systems it is made up of. Of course, besides considering the ‘aggregate’ TS, as it results from the co-evolution of its sub-systems, it is also important to disentangle their functioning and individual relationships. In so doing, first of all, the core and the peripheral partitions of a TS can be

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precisely identified, distinguishing those which more directly refer to the private business ‘sector’ from those which instead pertain to the public and/or to the foreign sub-system. Secondly, and accordingly, policy recommendations can be put forward more accurately. Unfortunately, sticking to the ‘embodied-R&Dexpenditure’ framework, and unfolding the TS with respect to its sub-systems, is not unproblematic. Indeed, adding sectoral-consistent proxies for the institutional set-up and the foreign sub-system suffers from some important methodological problems. This appears to be the case, for example, of an earlier application of this extended methodology (Leoncini et al., 1996), in which two additions have been made to the standard analysis of embodied R&D expenditure flows. On the one hand, the public R&D expenditure by ‘socio-economic’ objectives (SEO) has been taken into account to explain the role of the institutional set-up.5 On the other hand, sectorally disaggregated versions of the technological balance of payments (TBP) have been used to proxy the relationships with the foreign market. At the theoretical level, these two extensions appear quite sound, when one just thinks of the inner nature of the selected proxies.6 At the operative level, they are relatively immediate to implement, since the relative data are available, respectively, from the EUROSTAT (Statistics on Innovation) and the OECD (Main Science and Technology Indicators). To be sure, the same data are produced at a quite aggregated sectoral level of analysis.7 However, in a limited empirical application (in that case, just two countries for one year only) un-matching sectors can be rescued by resorting 5 Of course, the role of the institutional set-up is much more broader and consists of a set of activities, typically of regulatory nature, with respect to which quantitative measurements are hard to find. More than with respect to the PBC, therefore, the present analysis should be integrated with a more qualitative kinds of studies, such as, for instance, those contained in the national systems of innovation literature (e.g. Nelson, 1993). 6 As for the former, by investing money in pursuing the development of both economic and social fields, these sectors presumably benefit from public technological transfers and knowledge spillovers which typically have disembodied nature. As far as the latter extension is concerned, given that the TBP reflects a TS ability to sell its technology abroad along with its use of foreign technologies, its sectoral disaggregation actually informs about the worldwide technological diffusion process in which the sectors of a TS are involved. 7 This is particularly true for the R&D expenditure data by SEO, which can be only partially related to standard economic sectors.

to national statistics, whose degree of comparability can be accurately evaluated. At the methodological level, instead, the same two extensions suffer from some relevant problems and limitations. First of all, the set of appropriate network analysis indicators considerably shrinks to encompass only oriented graph analysis. Once appropriate cut-off values have been selected,8 the foreign market and the institutional set-up can, in fact, be just added as two extra nodes to the sectoral nodes of the PBC, and relationships from the former to the latter mapped by drawing those edges which correspond to flows bigger than the threshold level selected (Leoncini et al., 1996, Fig. 6, p. 427). Although this is an extremely important piece of analysis, other more ‘quantitative’ indicators (such as density, or centralisation) cannot instead be applied. Second, and even more important, innovation flows are captured by quite heterogeneous proxies (such as, respectively, patents and public efforts on socially valuable innovations), thus possibly mixing-up the different biases which affect each of them. In front of the trade-off between methodological accuracy and data availability, this extended methodology has thus privileged the latter, mainly for two reasons. First of all, the focus of that application was mainly on the analysis of PBC interrelationships, while the PBC relationships with the foreign market and the institutional set-up were presented just as a tentative extension of the former. Second, the application was limited and its methodological problems could be thus somehow accommodated. Evidently, in trying to compare an appreciable number of TS with respect to more than one period, supplementing international statistics with national data becomes burdensome. Furthermore, by focusing on extracore relationships, rather than on within the core ones, the methodological restrictions pointed out above become unsustainable and a more rigorous and systematic methodology has to be envisaged.

3. The analysis of R&D financing intersectoral flows: a new methodology Looking for a more consistent approach to deal with the core and extra-core relationships of a TS, in this 8

A delicate issue on which we will come back in the next section.

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paper we put forward a new methodology. Rather than referring to R&D expenditure flows, we suggest to look at R&D financing flows in addressing the technoeconomic relationships within the PBC and between the PBC and both the public and the foreign subsystems. In so doing, we have to deal with two kinds of hypothesis, with respect to (i) the ‘technological outcome’ of the firms of the PBC and (ii) the ‘means’ through which innovation diffuses. As for first hypothesis, we assume that the private financing of R&D could be consider as a proxy of the ‘technological outcome’ which is obtained and diffused by the firms of the PBC. Apparently, by resorting to R&D financing, rather than R&D expenditure, we make our innovation proxy more ‘proxied’. Indeed, the part of a certain financial project that is transformed into actual innovations is presumably less than the correspondent actual expenditure: in a sense, the input nature of the proxy has been raised to the square. On the other hand, it seems to us reasonable to assume that the incentives of the firms to make their innovative projects more efficient in terms of outcome is the higher, the more the required resources are privately financed, not to say self-financed: in this sense, although still a proxy, private R&D financing is not necessarily less reliable than R&D expenditure. As far as the diffusion hypothesis is concerned, an important distinction is introduced with respect to the mechanism through which it takes place within the PBC and between the PBC and both the public and the foreign sub-system.9 Although both embodied and disembodied diffusion processes are at work with respect to both core and extra-core relationships, as an approximation we assume that the outcome of privately funded R&D expenditure circulates within the PBC exclusively in an embodied way. The rational that supports this hypothesis is that of a close functional linkage between the private financing of R&D projects and their implementation (and thus embodiment) in improved products and services, obtained with improved intermediate commodities. We instead assume that the 9 Although a diffusion process can be envisaged also from the PBC towards the public and the foreign sub-system, such an outward kind of PBC flows will not be considered for reasons we will clarify in the next section.

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innovative relationships between the public and the foreign sub-systems, on the one hand, and the economic sectors of the PBC, on the other hand, are exclusively of disembodied nature. The peculiar role of public financing to R&D, mainly consisting of the support to a basic kind of research, and the geographical divide which renders foreign financing to R&D typically ‘long-arm’, make this assumption not entirely arbitrary. The two kinds of hypotheses we have just discussed translate quite immediately in workable matrices to which, following our previous analysis (Leoncini and Montresor, 2003), some interesting network analysis indicators can be straightforwardly applied. 3.1. R&D financing and intersectoral matrices Following the literature on innovation diffusion with respect to R&D expenditure or/and employment (Marengo and Sterlacchini, 1990), and the previous hypotheses with respect to the private (Pr) R&D financing, a matrix RPr (n × n) can be accordingly defined as: ˆ RPr = rˆ Pr [(ˆx)−1 Bd]

(1)

where rˆ Pr (n × n)is the diagonal matrix of privately ˆ × funded sectoral R&D expenditure, xˆ (n × n) and d(n n) the sectoral diagonal matrices of, respectively, total intermediate inputs and final demand, B the Leontief inverse, and, finally, n the number of economic sectors. Each cell, RPr ij , of this matrix measures the amount of R&D expenditure financed by sector i which is embodied in the final demand for the commodity produced by sector j. RPr can thus be taken as a proxy of the core relationships of a TS, that is, of the relationships which occur within the PBC among its n constitutive sectors. In order to map the whole set of (both core and extra-core) flows among all the sub-systems of a TS, we should in principle extend RPr by adding two consistent rows and columns: one row (r Pub (1 × (n + 2))) and one column (rPub ((n + 2) × 1)) for the public sub-system, and one row (r For (1 × (n + 2))) and one column (rFor ((n + 2) × 1)) for the foreign sub-system. In so doing, we could obtain a matrix

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RPr,Pub,For ((n + 2) × (n + 2)) such as the following: RPr,Pub,For  Pr R11  ..  .  =  RPr  n1  rPub,1 rFor,1

··· .. .

RPr 1n .. .

· · · RPr nn · · · rPub,n · · · rFor,n

r1,Pub .. . rn,Pub rPub,Pub rFor,Pub

 r1,For ..  .   (2) rn,For   rPub,For  rFor,For

Evidently, RPr,Pub,For represent the constitutive relationships of the TS as a whole. Inward extra-core relationships would, in fact, be captured by the two extra rows, rPub (1 × (n + 2)) and r For (1 × (n + 2)), along the first n columns, while outward extra-core ones would be measured by the first n rows of the two extra columns, rPub ((n + 2) × 1) and rFor ((n + 2) × 1). Relationships occurring within the public and the foreign sub-system themselves can be found along the main diagonal, and described by, respectively, rPub,Pub and rFor,For . Finally, relationships between extra-core sub-systems can be visualised as well, both those originating from the public (rPub,For ) and from the foreign sub-system (rFor,Pub ). While the (n × n) core partition of RPr,Pub,For can be proxied by resorting to private R&D financing, that is through Eq. (1), how to measure its extra-core relationships remains problematic. Referring to R&D financing as a general innovative proxy, and modelling the diffusion process according to the hypotheses introduced above (Section 3) can however be helpful in filling it up consistently. More precisely, the first n elements of the two extra-rows of Eq. (2) can be proxied by the shares of R&D expenditure made by each of the n sectors of the PBC which are financed by, respectively, public and foreign sources. As we will see in the next section, although limited, the data to fill this partition of RPr,Pub,For are available, while data availability hampers the fulfilment of the other extra-core elements of it. However, the relevance of these extracore cells of RPr,Pub,For for the internal configuration of a TS is not so crucial. On the one hand, the first n elements of rPub can be considered a proxy of a private form of co-financing to public innovative efforts, which is typically less important than the public financing to private innovative efforts. On the other hand, the first n elements of rFor are more relevant in shaping the configuration of a foreign TS-target than that of the

domestic TS-source. Finally, the (Pub, For) partition of RPr,Pub,For refers to financial relationships with no direct bearing on the innovative activities of the firm. Accordingly, as a first approximation, in the following we will refer to a ‘reduced’ form of RPr,Pub,For such as the following:  Pr  R11 · · · RPr 0 0 1n  .. .. .. .. ..   . . . . .   RPr,Pub,For =  RPr · · · RPr 0 0  (3) nn  n1   rPub,1 · · · rPub,n 0 0  rFor,1 · · · rFor,n 0 0 Although the structure of a TS appears now more explicitly than from the sole analysis of R&D expenditure, some further transformations have to be made in order to apply network analysis. First of all, each intersectoral and inter sub-system R&D financial flow has to be related to its total sectoral R&D financing, i.e. to the sum of the private R&D financing that a certain sector acquires indirectly from the other sectors of the PBC, and of the public and foreign R&D financing acquired directly from the other two sub-systems. We thus refer to a relative version of RPr,Pub,For normalised by column: −1 RRel Pr,Pub,For = RPr,Pub,For (ˆs)

(4)

where sˆ is the diagonal matrix of total sectoral R&D financing acquisitions. Dividing the R&D financial acquisition of each sector by the total amount of its acquisitions, we eliminate undesired scale effects. Furthermore, and above all, RPr,Pub,For becomes an internally consistent matrix rather than a matrix in which the core matrix RPr and the edging vectors (rPub and rFor ) are simply ‘juxtaposed’ side by side. In this way each cell refers to a system kind of variable: the total of R&D financing acquired by each sector of the PBC. It must however be stressed that, in spite of this transformation, the flows of RRel Pr,Pub,For are still of a different nature. Indeed, while the inter-industrial partition measures the incidence of private R&D financing indirect acquisitions on total acquisitions of each sector, the last two row vectors measure the incidence on the total of direct public and foreign R&D financing acquisitions. As we will see, this fact will prevent us from using the whole set of network

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analysis indicators used in other analyses of the TS (Leoncini and Montresor, 2003). 3.2. R&D financing matrices and network analysis As we have already shown (Leoncini and Montresor, 2003), standard network analysis indicators can be generally applied to intersectoral innovation flows matrices. The density of the matrices, for example, worked out by measuring the incidence of ‘appreciable’ cells (i.e. higher than a certain value) on their total number, can be taken as an indicator of the connectivity degree of the TS to which such a matrix refers: the higher the density, the more connected the TS. Although with some notable restrictions, network analysis indicators can also be applied to matrices such as RRel Pr,Pub,For . Indeed, the inherent heterogeneity of such a matrix just prevents a direct application of system-wide density and centralisation indicators, while the density and the centralisation of the sub-systems, along with their centrality and the correspondent oriented graphs can be worked out straightforwardly. As far as density is concerned, it is evident that, being heterogeneous, those cells of RRel Pr,Pub,For which are higher than a certain cut-off value k cannot be added up to have a proxy of the connectivity of the TS as a whole. However, some considerations about the connectivity degree of the TS can still be made by looking at the density of those clusters of flows which are homogeneous, that is of the different sub-systems. More precisely, we could calculate the density of the PBC, the institutional set-up and the foreign market sub-system, by working out, as usual, the ratio between the number, mk , of the correspondent edges which are greater than a selected threshold, k, and the total number of edges which could potentially constitute the relevant sub-system. Formally, we could refer to the following sub-system density indicators: k DPBC =

mkPr ; n × (n − 1)

k DPub =

mkPub ; n

mkFor (5) n As usual, the greater is the relevant D value, in turn defined between 0 and 1, and the more internally conk DFor =

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nected is the correspondent sub-system. Results would of course crucially depend on the selected threshold, k. However, ad hocness can be attenuated by building up density distributions referring, rather than to one cut-off only, to the array of all the possible cut-offs of the compared systems (or sub-systems). Patterns of lower or higher density, rather than punctual density relationships, can thus be identified by retaining innovative flows of smaller or larger size (Leoncini and Montresor, 2003). It is important to stress that the density indicators of Eqs. (5) cannot be directly compared with the density of the whole TS, as they refer to the connectivity of different kinds of flows. We can however look at the correlation degree between the ranking of the various density indicators. The reference to rankings, rather than to the actual values of the indicators, in fact, allows us to overcome the problems entailed by flows heterogeneity. Furthermore, in so doing we can get at least some hints about the mutual relationships between the connectivity degree of the different sub-systems, and between their connectivity and that of the TS as a whole. For example, by means of the Spearman correlation index,10 we can check to which extent a higher position in terms of density of the PBC gets reflected in a higher position in terms of density of the whole TS. In such a way, we could, although indirectly, investigate to what degree the PBC is the main determinant of the system coherence. The correlation between the invesk , Dk k tigated TS ranked according to DPBC Pub and DFor , instead suggests how far higher positions in terms of density of the PBC are complemented by (in the case of a positive correlation) or substituted for (in the case of a negative correlation) higher positions in the density of the institutional set-up and of the foreign sub-system. Indeed, depending on the sign and the intensity of the correlation, we might infer that a certain TS compensates the low connectivity of its PBC with highly connected public and foreign sub-systems, or vice versa (for high negative values of the correlation index). Alternatively (for high positive values of the correlation index), we might infer that the low (high) connectiv10 This is a correlation index between two rankings which is defined over the domain [−1; +1]. The closer the index is to +1 (−1), the more the two rankings are similar (dissimilar), that is the first position of the first ranking is closer to the first (last) position of the second ranking, and so on.

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ity of the PBC of a certain TS negatively (positively) affects the connectivity of the other two sub-systems. The analysis of the connectivity degree of the TS can also be carried out by looking at the centrality of the different nodes, and the direction of the edges of the correspondent oriented graphs. In this case the analysis needs not be amended: in fact, embodied and disembodied flows which overcome a certain cut-off value are not added up, but rather mapped and examined separately. More precisely, the relative weight that the institutional set-up and the foreign sub-system have within a TS can be estimated by working out the out-degree centrality of the correspondent nodes, in the same way we have done for all the other sectors of the PBC,11 that is:  OutPUB = iPub out ;  OutFOR = iFor (7) out with 0 < Out(·) < n For where iPub out and iout indicate one of the relevant edges (that is greater than a certain cut-off) which comes out from, respectively, the public and the foreign sector. Intuitively, the greater is the relevant outdegree indicator, the more central are the two sub-systems. Conversely, to evaluate the degree of dependence of each economic sector of the PBC, to a standard indegree centrality indicator (InPBC) we add two dual indegree indicators capturing its dependence on (values equal to 1) or independence from (values equal to 0), respectively, the public (InPUB) and the foreign sub-system (InFOR). As usual, on the basis of the previous PBC centrality indicators, we can work out a system-wide connectivity indicator, that is centralisation:  (OutPBC∗ − OutPBCi ) Centr = (8) (n)(n − 1)

where OutPBCi is the outdegree centrality of sector i out of the n of the PBC, while OutPBC* is the outdegree centrality of the most central node. Intuitively, the lower the centralisation, the less asymmetric (and thus the more systemic) is the PBC with respect to its most central node. The reference to outdegree rather than indegree centrality is due to the fact that, according to the methodology we have followed in building up 11 For sake of distinction, the outdegree centrality of each sector of the PBC with respect to the others will be here denoted as OutPBC.

our matrices, the indegree centralities of the sectors are structurally more homogeneous.

4. Setting the new methodology at work: an illustrative application Even after having ‘reduced’ RPr,Pub,For to its most relevant elements, the methodology we propose remains quite demanding in terms of data. Input–output tables are still its basic ingredient, with the well-know problems which affect the promptness of their delivery. Further data requirements are however imposed by the choice of shifting from R&D expenditure to R&D financing. Indeed, to the standard disaggregation of R&D expenditure by economic sector it here adds the need of sectoral data disaggregated by source of financing, a need that different kinds of reasons makes it difficult to fulfil consistently for an appreciable number of TS: the country tables of the OECD Main Science and Technology Indicators (MSTI) which refer to these data are actually populated by many missing values. Finally, these already scanty data have to be crossed with those on input–output tables, with further chances of sectoral drops from aggregation. Following the present technique, therefore, the trade-off between data availability and methodological accuracy would be solved in favour of the latter rather than of the former, as in the extended analysis of R&D expenditure flows. Accordingly, in front of quite severe data availability problems, the best we have been able to do to set the new methodology at work has been to build up a very limited application for it, whose value is thus at most illustrative. By crossing the OECD input–output dataset with the OECD MSTI, it has in fact, been possible to keep a significant number of sectors (16 of the manufacturing partition, see Appendix A) with respect to 5 TS (France, Germany, Italy, Japan, and UK) and 1 year (1985).12

12 Referring to more periods of time would have further reduced the number of viable sectors, so that we have also been forced to make our application purely cross-sectional and admittedly not updated. On the other hand, the reference to 1985 has allowed us to resort to national statistics for the Italian TS: both input–output tables and sectoral data on R&D expenditure are largely available from ISTAT (just) for that year so that, although by possibly introducing some heterogeneity in crossing the OECD and the ISTAT datasets, the application has been

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As far as the structure of the empirical application is concerned, after having built up the relevant RRel Pr,Pub,For for the 5 TS, we will apply to them the network analysis indicators presented in Section 3.2. In particular, the analysis will be carried out with respect to two cut-off values: k = 0.005 and 0.05. Although affected by some arbitrariness, this choice has been made on the basis of the same heuristic methodology we have followed in our previous applications (Leoncini and Montresor, 2003). Density distributions have been built up for each and every TS for all the values (that is, 256 = 16 × 16) of a reference matrix. Two cut-offs have then been selected searching for dichotomised matrices which were neither insignificant (that is, elementary as with very few 1s) nor unobservable (that is, very complex as with a high number of 1s). Furthermore, relatively more robust cut-offs have been selected by checking for density stability in their neighbourhoods. Let us observe that while one cut-off value (k = 0.05) is the same of our previous application on a larger number of TS (Leoncini and Montresor, 2000b), to which we will therefore refer,13 the other (k = 0.005) has been added in order to compare the structure of the investigated TS for both small and large innovative flows. 4.1. Comparing density analyses A first exercise it might be interesting to carry out on the basis of the new methodology, is that of comparing the density of the PBC, in terms of private R&D financing flows, and of the TS as a whole, in terms of R&D expenditure flows (as from Section 2). In the present case, for example (Table 1), it seems to emerge that the connectivity of the core exerts a sort of wide ‘systemeffect’ which makes the correspondent TS relatively more connected: Japan is an illustrative example. In spite of remarkable reshuffling, the result appears robust (the Spearman correlation index for k = 0.05 decreases but remains positive) and should therefore be tested in wider applications. A second exercise concerns the parallel between the density of the PBC and of, respectively, the public and run with respect to a country which was missing in other previous applications of ours (Leoncini and Montresor, 2003). 13 In that application, however, focused on the structural change of the investigated TS over the 80s, Italy was not included because of the lack of comparable data for periods of time before and after 1985.

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the foreign sub-system. As for the former, a substitutability relationship, according to which a weakly (highly) connected PBC calls for (do not require) a highly connected public interface, appears sustainable, especially when both small and large flows are considered (for k = 0.05 the Spearman correlation index actually decreases to 0.3 in absolute value). As for the latter, there appears to be a substitutability relationship for both small and large flows: on the one hand, the foreign market works as a compensation channel for the low connectivity of the PBC (such as in the case of the UK) while, on the other hand, highly connected PBC make the dependence of the TS on the foreign market less stringent (such as in the case of Japan and Germany). On the basis of these results, therefore, more recent and systematic applications should carefully distinguish the size of the innovative flows with respect to which density relationships are investigated. 4.2. Centrality analysis and oriented graphs As density is a system-wide indicator, it might hide further differences across the TS in terms of centrality of its constituent sectors.14 Therefore, the resort to centrality and oriented graph analysis appears extremely important to make the comparison among the TS more subtle. 4.2.1. Small and large innovative flows: k = 0.005 At the outset, let us observe that for flows higher than 0.5% of the total innovative acquisitions the average centrality of the 16 sectors of the PBC is still quite high and homogeneous across the investigated TS (Table 2): in each of them, the ‘mean-sector’ innovates and gets innovated by nearly half of the others (around seven sectors). In spite of this homogeneity, some important differences among the investigated PBC emerge by 14 This is particularly true for the BPC, which might be, for example, equally connected in different TS, but with different underlying sectoral structures. As far as the other sub-systems (public and foreign) are concerned, instead, given the way they are proxied, their density is nothing but a relative measure of their absolute outdegree centrality. However, in spite of this equivalence, while their density (or relative centrality) has been, in the previous section, just related to that of the PBC and of the TS as a whole, their absolute centrality will be here analysed separately in order to evaluate their weight within different TS.

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Table 1 Sub-system density and TS rankings Cut-off

k = 0.005

k = 0.05

Density

Rank

Density

Rank

TS*

Germany France UK Japan

0.27 0.30 0.14 0.46

3 2 4 1

0.10 0.11 0.07 0.11

3 1 4 1

PBC

Germany France UK Japan Italy

0.41 0.39 0.40 0.45 0.37

2 4 3 1 5

0.08 0.08 0.10 0.11 0.10

4 4 2 1 2

Public sub-system

Germany France UK Japan Italy

0.94 0.94 1.00 0.50 0.94

2 2 1 5 2

0.69 0.44 0.56 0.06 0.50

1 4 2 5 3

Foreign sub-system

Germany France UK Japan Italy

0.25 0.63 1.00 0.06 0.44

4 2 1 5 3

0.06 0.31 0.63 0.00 0.25

4 2 1 5 3

Spearman correlation

TS

PBC

Public sub-system

Foreign sub-system

TS

PBC

Public sub-system

Foreign sub-system

TS PBC Public sub-system Foreign sub-system

– 0.4 −0.9 −0.8

0.4 – −0.65 −0.6

−0.9 −0.65 – 0.75

−0.8 −0.6 0.75 –

– 0.2 −1 −0.9

0.2 – −0.3 −0.1

−1 −0.3 – 0.3

−0.9 −0.1 0.3 –

looking at the average outdegree centrality gap with respect to the most central sector: the PBC ranking in terms of centralisation actually confirms the results of a more institutional kind of literature.15 Furthermore, the identity of the most and of the least central sectors is not dissimilar from what we have found in dealing with total R&D expenditure flows (Leoncini and Montresor, 2000b), but with some important exceptions: the superior centrality of the electrical machinery sector (Sector 12), and the TS specificity of the natural 15 The outdegree network centralisation (Centr) for Japan and the UK in fact shows, respectively, the least (Centr = 63.3%) and the most (Centr = 68.1%) asymmetric (systemic) sectoral structures. On the other hand, by referring to financial flows the centrality gap is lower for Italy than for France and Germany, suggesting that the structure of the PBC does not necessarily overlaps with that of the correspondent TS.

resource intensive sectors (e.g. Sectors 7–9) are just the most relevant.16 On the basis of these and other strategic and institutional kinds of differences, the reference to the private financial system introduces in the analysis,17 further applications should consider how

16 For an extensive discussion of these differences see Leoncini and Montresor (2003, Chapter 6). 17 This is the case, for example, of motor-vehicles (Sector 14). Indeed, although the amount of acquisitions is in all the TS among the highest, the number of diffusions is quite variable across them. Accordingly, the sector ranges from highly dependent, in Italy and Japan, to nearly neutral, in France, to slightly pervasive, in Germany and UK. The reference to the private financial system therefore attenuates somehow the structural features that the sector itself reveal in all the TS when R&D expenditure is considered in aggregate terms (see, for example, Leoncini and Montresor, 2001).

Table 2 Freeman degree centrality: PBC, public and foreign sub-system PBC

Britain

France

Germany

Japan

Italy

OutPBC InPBC InPUB InFOR OutPBC InPBC InPUB InFOR OutPBC InPBC InPUB InFOR OutPBC InPBC InPUB InFOR OutPBC InPBC InPUB InFOR

Mean S.D. Sum Var NE Min Max Centr (%) PUB FOR

2.00 2.00 1.00 8.00 16.00 9.00 8.00 10.00 5.00 10.00 15.00 15.00 1.00 9.00 1.00 1.00

10.00 9.00 12.00 7.00 1.00 8.00 8.00 8.00 7.00 10.00 5.00 3.00 5.00 7.00 4.00 9.00

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

2.00 3.00 1.00 4.00 15.00 15.00 9.00 7.00 8.00 9.00 12.00 16.00 1.00 6.00 1.00 1.00

10.00 9.00 9.00 8.00 4.00 4.00 6.00 8.00 5.00 7.00 7.00 3.00 8.00 8.00 2.00 12.00

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.00

1.00 0.00 0.00 0.00 1.00 1.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00 0.00 1.00 0.00

1.00 2.00 2.00 5.00 16.00 11.00 9.00 7.00 4.00 11.00 15.00 16.00 2.00 12.00 1.00 1.00

9.00 8.00 10.00 8.00 3.00 6.00 9.00 6.00 4.00 8.00 7.00 4.00 8.00 8.00 4.00 13.00

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.00 1.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 0.00 0.00 1.00 0.00 0.00 1.00 0.00

7.06 7.06 1.00 1.00 6.88 6.88 0.94 0.63 7.19 7.19 0.94 0.25 5.21 2.77 0.00 0.00 5.23 2.62 0.25 0.50 5.49 2.53 0.25 0.45 113.00 113.00 16.00 16.00 110.00 110.00 15.00 10.00 115.00 115.00 15.00 4.00 27.18 7.68 0.00 0.00 27.36 6.86 0.06 0.25 30.15 6.40 0.06 0.20 35.11 30.35 34.55 29.43 36.18 30.48 1.00 1.00 1.00 1.00 1.00 2.00 0.00 0.00 1.00 3.00 0.00 0.00 16.00 12.00 1.00 1.00 16.00 12.00 1.00 1.00 16.00 13.00 1.00 1.00 68.10 37.62 69.52 39.05 67.14 44.29 16.00 16.00

15.00 10.00

15.00 4.00

1.00 3.00 3.00 10.00 16.00 13.00 16.00 14.00 12.00 13.00 6.00 8.00 3.00 1.00 1.00 3.00

0.00 1.00 0.00 0.00 1.00 0.00 1.00 1.00 1.00 0.00 1.00 1.00 0.00 0.00 1.00 0.00

0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

7.69 7.69 0.50 5.50 2.57 0.52 123.00 123.00 8.00 30.21 6.59 0.27 37.80 32.42 1.00 3.00 0.00 16.00 12.00 1.00 63.33 32.86

0.06 0.25 1.00 0.06

8.00 1.00

8.00 8.00 12.00 8.00 4.00 6.00 5.00 4.00 3.00 8.00 9.00 8.00 9.00 9.00 11.00 11.00

0.00 1.00

1.00 1.00 1.00 1.00 15.00 14.00 5.00 7.00 13.00 14.00 12.00 15.00 2.00 2.00 1.00 1.00

9.00 7.00 9.00 8.00 3.00 4.00 9.00 8.00 4.00 7.00 7.00 6.00 7.00 7.00 1.00 9.00

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.00

0.00 0.00 0.00 0.00 1.00 1.00 0.00 0.00 1.00 1.00 1.00 1.00 0.00 0.00 1.00 0.00

6.56 6.56 0.94 0.44 5.88 2.32 0.25 0.51 105.00 105.00 15.00 7.00 34.62 5.37 0.06 0.26 35.26 27.84 1.00 1.00 0.00 0.00 15.00 9.00 1.00 1.00 64.29 18.57

R. Leoncini, S. Montresor / Research Policy 34 (2005) 83–100

k = 0.005 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

15.00 7.00

93

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Table 2 (Continued ) PBC

Britain

France

Germany

Japan

Italy

OutPBC InPBC InPUB InFOR OutPBC InPBC InPUB InFOR OutPBC InPBC InPUB InFOR OutPBC InPBC InPUB InFOR OutPBC InPBC InPUB InFOR 1.00 1.00 1.00 1.00 11.00 2.00 1.00 2.00 1.00 1.00 7.00 8.00 1.00 1.00 1.00 1.00

2.00 3.00 5.00 4.00 1.00 2.00 3.00 4.00 2.00 5.00 2.00 1.00 2.00 2.00 1.00 2.00

Mean S.D. Sum Var NE Min Max Centr (%)

2.56 3.04 41.00 9.25 15.91 1.00 11.00 64.29

2.56 1.27 41.00 1.62 11.45 1.00 5.00 18.57

PUB FOR

9.00 10.00

0.00 1.00 0.00 1.00 0.00 0.00 0.00 1.00 1.00 0.00 1.00 1.00 1.00 0.00 1.00 1.00

1.00 0.00 0.00 0.00 1.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 0.00 1.00 0.00

1.00 1.00 1.00 1.00 10.00 5.00 1.00 2.00 2.00 1.00 2.00 4.00 1.00 1.00 1.00 1.00

3.00 4.00 3.00 2.00 1.00 2.00 2.00 3.00 1.00 4.00 2.00 1.00 1.00 1.00 1.00 4.00

0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 1.00 0.00 1.00 1.00 1.00 0.00 1.00 0.00

1.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 0.00 1.00 0.00 1.00 0.00

1.00 1.00 1.00 1.00 11.00 1.00 1.00 3.00 2.00 2.00 2.00 8.00 1.00 1.00 1.00 1.00

0.56 0.63 0.51 0.50 9.00 10.00 0.26 0.25

2.19 2.32 35.00 5.40 12.77 1.00 10.00 59.52

2.19 1.13 35.00 1.28 9.85 1.00 4.00 13.81

0.44 0.51 7.00 0.26

0.31 0.48 5.00 0.23

0.00 1.00

0.00 1.00

2.38 2.80 38.00 7.86 14.70 1.00 11.00 65.71

0.00 1.00

0.00 1.00

7.00 5.00

11.00 1.00

3.00 2.00 3.00 3.00 1.00 2.00 2.00 1.00 2.00 3.00 2.00 1.00 3.00 2.00 1.00 7.00

0.00 1.00 1.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00

1.00 1.00 1.00 1.00 14.00 3.00 4.00 4.00 3.00 2.00 1.00 5.00 1.00 1.00 1.00 1.00

3.00 2.00 5.00 2.00 1.00 2.00 2.00 2.00 1.00 4.00 4.00 2.00 2.00 3.00 6.00 3.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.00 1.00 1.00 0.00 12.00 5.00 1.00 1.00 4.00 3.00 2.00 6.00 1.00 1.00 1.00 1.00

3.00 3.00 4.00 1.00 1.00 2.00 4.00 4.00 2.00 4.00 2.00 1.00 2.00 1.00 1.00 6.00

0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.00

0.00 0.00 0.00 0.00 1.00 1.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00

2.38 0.69 0.06 1.41 0.48 0.25 38.00 11.00 1.00 1.98 0.23 0.06 11.05 1.00 0.00 0.00 7.00 1.00 1.00 35.24

2.75 3.19 44.00 10.19 16.85 1.00 14.00 85.71

2.75 1.35 44.00 1.81 12.25 1.00 6.00 24.76

0.06 0.25 1.00 0.06

0.00 0.00 0.00 0.00

0.25 0.45 4.00 0.20

0.00 0.00

2.56 1.46 41.00 2.12 11.79 1.00 6.00 26.19

0.50 0.52 8.00 0.27

0.00 1.00

2.56 2.94 41.00 8.62 15.59 0.00 12.00 71.91

1.00 0.00

8.00 4.00

0.00 0.00 1.00 1.00

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k = 0.05 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

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far the centrality of the PBC sectors in terms of R&D financing translates into the centrality of the TS sectors in terms of R&D expenditure: our suggestion is that this translation occurs but with some important exceptions one should consider case by case. Coming now to the other two sub-systems, let us briefly observe that by considering both small and large flows the relative weight of the public sub-system appears very high in all the investigated European TS, while it is definitively lower in Japan (Table 2). Although apparently counterintuitive, the result for Japan might simply suggest the different way in which the correspondent public sub-system interact with the other sub-systems when compared with the other European TS. And, indeed, the studies carry out on the Japanese TS over the 1980s suggest an important role for the State which was not marginalized to R&D financing. The high degree of involvement that the literature on national systems of innovation identifies in the case of Japan (Odagiri and Goto, 1993) is thus apparently more institutional and systemic rather than economic/financial and sectoral, as in the European TS. The role of the foreign sub-system (Table 2), considering both small and large flows, is quite different in the different TS. In this last respect, the present application seems to support a result we would expect also from broader applications of our methodology: the relationships of a certain TS with the rest of the world are not as necessary and structural as the institutional ones which are associated to public funds, but more dependent on their inner characteristics. Also with respect to the foreign sub-system, the analysis can be usefully complemented by drawing on both standard and system kinds of international studies: the foreign orientation of the TS, especially in terms of FDI, and the relationship between the international openness and the internal connectivity of the TS in particular. The former kind of studies strongly supports our results: UK, for example, actually emerges as a highly multinational system, both in outward and, especially, in inward terms, while the international technological activities of Japan appear very much outward unbalanced (Cantwell, 1995). From the latter kind of studies, instead, an interesting substitutability relationships seems to exist between internal connectivity and international openness with respect to both Japan – highly connected internally but inward oriented in international terms – and UK – highly segmented internally

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but very much open internationally (Leoncini and Montresor, 2000a). Both the previous pieces of analysis can be refined by looking at the correspondent oriented graphs (Fig. 1). As for the former, the nature of the sectors (e.g. resource intensive or science-based) which are reached by above-the-threshold public innovative flows can be related to such system kind of arguments as resource availability and strategic choices in terms of international competitiveness, and different sectoral patterns of public involvement in R&D can be identified accordingly. In the Japanese TS, for example, the public funds did not reach a minimum threshold (0.5% of the total intersectoral acquisitions) neither in the most traditional sectors, nor in the majority of the scale intensive ones (although with exceptions). In 1985, the public (finance) interface was instead active with respect to nearly all the sectors intensive of natural resource (both ferrous and non ferrous basic metals), of which Japan is not well endowed, and with respect to the most high-tech sectors of the disaggregation (chemical products and electrical machinery), at that time retained more strategic in terms of international competitiveness. As far as the graph analysis of the foreign subsystem is concerned, it allows us to relate the sectoral breakdown of the foreign interface of a TS to questions of international and technological competitiveness. In the present application, for example, it would be sensible to argue that the comparative advantages that some European TS (in particular, France and Italy) in the middle 80s had in such scale intensive sectors as rubber and plastic (Sector 6), fabricated metal products (Sector 10) and non electrical machinery (Sector 11) (see, for example, Amendola et al., 1992), also benefited from a significant foreign innovative support. 4.2.2. Large innovative flows: k = 0.05 By considering large innovative flows (k = 0.05), the centrality of the 16 sectors of the PBC reduces substantially (Table 2), and the correspondent TS structures become less significant: indeed, in all the TS, the ‘meansector’ innovates and gets innovated by not more than three of the others. At the outset, when we look at private R&D financial flows, the investigated TS seem to differ more with respect to the present than to the previous

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Fig. 1. Oriented graphs for small and large innovative flows (k = 0.005).

R. Leoncini, S. Montresor / Research Policy 34 (2005) 83–100

cut-off. In other words, the intersectoral diffusion of ‘primary’ (possibly radical) innovation projects appear more differentiating than that of ‘secondary’ (possibly incremental) ones: a hypothesis that, depending on the network analysis methodology we use, is general a fortiori. But also some other results were largely expected and candidate to be instead genuinely general. First of all, the results of the PBC centralisation analysis are substantially different from the previous cut-off and, this time, less aligned with those of the institutional studies on the correspondent innovation systems: indeed, cutting-out small and intermediate flows we are, in fact, referring to the connectivity of super-cores which do not perfectly overlap with the correspondent PBC and, a fortiori, with the TS within which they operate. Furthermore, in spite of the structural homogeneity of the super-cores of the TS, relevant differences still emerge with respect to both the pervasive and the dependent sectors, so that the system-wide effect of the PBC density (Section 4.1) does not guarantee any correspondence between the PBC and the TS sector centrality. Coming to the other two sub-systems, the picture that we get with respect to the public one is much more differentiated across the TS than for the previous cutoff (Table 2). If confirmed in wider applications, this would suggest that drawing on large public funds for innovation is actually an institutional, rather than a structural feature of the TS. In other words, while up to a certain level the intervention of the public sub-system appears unavoidable, after a certain minimal threshold the degree of interaction with the public interface becomes more dependent on the specificity of the TS itself and, possibly, more strategic. In the present application, for example, with the exception of the minimal incidence that the public sub-system shows in Japan, the other four TS appear now more clearly and differently ranked. The analysis of the foreign sub-system seems to show that resources from abroad are a less structural need than that of obtaining them from public institutions. Although with respect to a very limited number of TS, this result appears quite robust: with the only exception of the British TS, the relative weight of the foreign sub-system is now definitively lower than that of the correspondent public one. Furthermore, the ranking of the openness degree is quite stable for the different cut-offs, while what changes is the relative distance

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among the TS.18 Indeed, that some TS are simply more dependent on foreign innovative funds than others, no matter the size of the funds, seems to suggest a relationship with other structural indicators of the different international positions of the investigated TS. When graph analysis (Fig. 2) is considered, an intense differentiation emerges in the sectoral orientation of the public interface. Accordingly, in this context such a kind of analysis appears more useful in detecting which are the public-PBC linkages which, holding, or not holding at all, in several TS and with respect to large innovative flows, can be more appropriately considered as structural: in the present application, the public recipient role of non ferrous metals (Sector 9) and, conversely, the public insulation of food, beverages and tobacco (Sector 1) can be read in these terms. Graph analysis can also be used to compare the different cut-off values in correspondence of which the public and the foreign sub-systems select (in terms of R&D financing) certain PBC sectors. If, for example, the public sub-system insulates certain sectors only by retaining consistent innovation flows, while the same sectors had been already selected by the foreign subsystem at lower cut-offs, one can conclude that, with respect to these sectors, the public sub-system is less selective than the foreign one, as it replicates its dynamics only at a higher dimensional level: in the present application, this happens with respect to the most high-tech sectors, that is non electrical (Sector 11) and electrical machinery (Sector 12), precision instruments (Sector 13), and aerospace (Sector 15). As far as the role of the foreign sub-system is considered, graph analysis allows us to look for those sectors which receive systematically large financial flows from abroad, that is, PBC partitions where internal innovative resources are possibly either less consistent or/and less effective in comparative terms: in the present application, this is the case of the aerospace sector (Sector 15), the only one in which all the TS have consistent foreign relationships.19 Similarly, graph analysis can 18 The German TS, for example, is now much closer to the Japanese one as a nearly ‘autonomous’ system. Furthermore, the gap between France and Italy, on the one hand, and the most foreign dependent TS (the UK), on the other, narrows substantially. 19 The only relevant exception is that of the Japanese TS, whose scarce international propensity gets further confirmed by a nil OutFOR. Once more, this result should be read by considering that the

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R. Leoncini, S. Montresor / Research Policy 34 (2005) 83–100

Fig. 2. Oriented graphs for large innovative flows (k = 0.05).

R. Leoncini, S. Montresor / Research Policy 34 (2005) 83–100

serve to detect those PBC partitions which are systematically insulated from the foreign sub-system, and in which therefore internal financing possibly appear essential or simply preferred in comparative terms: in the present application, this is the case of four traditional sectors together with the residual sector (Sector 16) and, especially, motor-vehicles (Sector 14), which are systematically insulated from large foreign flows.

5. Conclusive remarks Intersectoral analyses of innovation diffusion have become an established methodology in the empirical investigation of the innovative process at the system level (DeBresson, 1996; Verspagen, 1997; Leoncini and Montresor, 2003). In particular, when the reference is to a broad notion of TS (Leoncini, 1998), the intersectoral analysis of embodied R&D expenditure flows appears suitable to investigate the structure of what can be defined the PBC of the TS, while the relationships between the PBC and the other TS constituent sub-systems – namely the institutional set-up and the foreign sub-system – can be caught only indirectly. Measuring these extra-core relationships of the TS directly and coherently is extremely important, in particular in order to suggest concrete policy recommendations. However, the research efforts to incorporate the institutional set-up and the foreign sub-system in the TS analysis have been less intensive. Furthermore, they have mainly drawn on, respectively, qualitative studies of Science and Technology policies and empirical investigations of Foreign Direct Investments (FDI) which, although extremely useful, are hard to integrate with the intersectoral analysis of the PBC (e.g. Nelson, 1993). Indeed, in unfolding the TS with respect to its sub-systems one has to face a trade-off between data availability and methodological accuracy. Privileging the former, in a previous application of ours (Leoncini et al., 1996) we have shown how the intersectoral analysis of embodied R&D expenditure can be directly extended by referring to R&D public expenditure disaggregated by socio-economic objective – taken as sector itself is one of the more ‘intensive’ of international partnerships and innovative collaborations, and one in which the internationalisation process has recently been quite strong (McGuire, 1999).

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a proxy of the institutional set-up – and to technological balances of payments disaggregated by economic sector – taken as a proxy of the foreign sub-system. Searching for a more accurate methodology to investigate the core and extra-core relationships of the TS systematically, in this paper we propose to shift the intersectoral analysis from R&D expenditure to R&D financing, and to differentiate the diffusion channels which approximate the TS relationships accordingly. More precisely, we provide arguments which support that intersectoral flows of embodied privately funded R&D could be used to map the core (i.e. within the PBC) relationships of a TS. And that disembodied flows of public and foreign funded R&D could be an appropriate proxy for the extra-core relationships through which the PBC is innervated by, respectively, the public and the foreign sub-system. By implementing these two hypotheses, a coherent matrix representation can be formulated for the TS as a whole and network analysis indicators applied, with some limitations, but still more extensively than with respect to the extended analysis of intersectoral R&D expenditure. This new methodology is extremely demanding in terms of data, at least when the present data availability is taken into account. However, by crossing and integrating different datasets, at least a limited application (for 5 TS with respect to 1985) can be built up to show its potential for wider applications, should further data made available. In particular, such an application shows how the density of the different sub-systems can be compared and related to that of the TS as whole, and how centrality indicators and graph analysis can be used to identify substitutability or complementary relationships in the role they play in shaping the TS. Although many of the results of this limited application are merely illustrative, and wait for further data to be confirmed in wider and more updated applications, they however represent important pros of the methodology we propose, one should therefore consider along with the cons the same methodology inevitably entails. Acknowledgement Previous versions of this paper have been presented at the 2001 EAEPE Conference on Comparing Economic Institutions, Siena, Italy, 8–11 November 2001, and to the Seminars Program of the Economics Department of the University of Padua, Italy, 31 October

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2002. The authors are grateful to their participants, and in particular to Stefano Solari and to Francesca Gambarotto. The authors are also grateful to two anonymous referees and to one of the editors of this journal for their comments and suggestions. Usual caveats apply. Appendix A Sectoral disaggregation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Food, beverages and tobacco Textiles, apparel and leather Wood, cork and furniture Paper, paper products and printing Chemical products (including pharmaceuticals) Rubber and plastic products Non-metallic mineral products Basic metals, ferrous (iron and steel) Basic metals, non ferrous Fabricated metal products Non-electrical machinery (including office and computing) Electrical machinery (including radio, TV and communication equipments) Instruments Motor-vehicles Aerospace Other manufacturing n.e.c.

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