Innovation system and developing countries: the Argentine’s failure
Alejandro Naclerio Universidad Nacional de La Plata, CEIL – PIETTE CONICET, Argentina Saavedra 15, Cap. Federal, Buenos Aires, Argentina E-mail: [email protected] E-mail: [email protected]
Abstract: Argentina, in the 1990s within the liberalisation reforms’ context, just purchased modern foreign technologies without making any innovation efforts. Conversely, other developing countries carried out institutional interventions in order to generate social and technological knowledge. The evidence included in this paper demonstrates that during the economic and productivity growth period (1991–1998), firms were not concerned by innovation activities. Consequently, it was impossible to build up a National Innovation System, supported, in essence, on national learning. Accordingly, our main hypothesis is: “importing new technology is not enough to encourage the innovation process, if there is lack of innovation effort”.
Reference to this paper should be made as follows: Naclerio, A. (2010) ‘Innovation system and developing countries: the Argentine’s failure’, Int. J. Technology and Globalisation, Vol. 5, Nos. 1/2, pp.132–160.
Biographical notes: Alejandro Naclerio is Lecturer in the Faculty of Economic Sciences, Universidad Nacional de La Plata and Universidad Nacional de Quilmes, Argentina. He is the Coordinator of the Science Technology unit at CEIL PIETTE CONICET. He obtained his PhD from Paris 13 – University, France. His research work is related with the specific conditions for innovation in developing countries and with development topics.
1 Introduction
Throughout the 1990s, Argentina followed the Washington Consensus recommendation. In this context, we see an important productivity growth in certain sectors where some firms were able to incorporate modern technologies. This productive rise, dominated by foreign investment, was possible without any domestic efforts. As a result, when there is no local innovation effort, it is not possible to generate an National Innovation System (NIS), even if new (foreign) technology has been incorporated. Considering this key relationship between effort and innovation process, we will focus on institutions that are more likely to weaken or reinforce the national innovative capacities in developing countries. In our view, the NIS conception in a developing country is a complex
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institutional process that reinforces the social Knowledge Base (KB). This KB is a systemic and a historic construction that depends on the industrial policy implemented in each country. Accordingly, innovation efforts and technological learning make the social knowledge production possible. On the contrary, when technologies are incorporated in the form of machines or codified knowledge, without making any innovation efforts to improve those technologies, the NIS has no role to play. This lack of technological efforts is one of the main reasons explaining why the liberalisation policies applied in Argentina during the 1990s could not consolidate an NIS.
Our main hypothesis is that “importing new technology is not enough to encourage the innovation process, if there is no innovation effort”.
In the first part, we will present a brief discussion on NIS approach for developing countries analysis. We consider:
• developed countries
• developing countries type-1, where KB is dynamic and has been strengthened by a technological policy
• developing countries type-2, where KB is not central in the local industrial sector.
These countries (the Argentine’s case during the 1990s) just purchase modern technologies and do not make any innovation effort.
In the second part, we will do cluster analysis and econometrical tests showing that during the economic and productivity growth period (1991–1998) in Argentina, most of the firms incorporated new technologies but they did not make innovation efforts.
2 Theoretical discussion: the NIS in a developing country
Freidrich List (Freeman, 1995, 2002; Freeman and Soete, 1997; OCDE, 1974)1 underlined how the less-developed countries should reach a higher industrialisation level. The particular situation of each industry and nation is a specific sign that would indicate which protection method would be able to be fulfilled in different historical contexts. We could set NIS framework, in this same outline, whereas speaking about developing countries. In this sense, following Naclerio (2004), an analytical typology is used to differentiate developing and developed countries. This typology was based on the large approach of NIS (Lundvall, 1992c) and also on the analytical model ‘PS-KB-OS’ (political system-knowledge base-operative system) (von Bertalanffy 1962, 1968; Le Moigne 1990):2 The functioning of the PS–KB–OS model is based on the more or less institutional complementarities prevailing in different countries. The core of this innovation complex system is the KB that has been built up throughout each country’s particular history. These constructions depend on interactions between the Political System (PS) and the productive or Operative System (OS). Summing up, the main characteristics of these three subsystems are:
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1 The OS is the subsystem that makes the NIS function. It shows the coordination between actors that are institutionalised in particular contexts. This coordination is possible when real or potential conflicts are turned into cooperation. These two opposite dimensions have been characterised as the dialectic ‘Conflict–Cooperation’ (CC). The cooperation, which overcomes conflicts, is capable of maintaining and improving the knowledge accumulation. Learning or knowledge accumulation also depends on the educational system, on the knowledge application, on the research capacities and, more specifically, on the interaction between technologies users and producers (Lundvall, 1992b). This is the frame where firms come out as coordination units that create knowledge. Organisational routines (evolutionist theory), contracts (agency problem), markets and trust constitute the coordination forms that shape the OS. The OS dynamics may be conflicting when actors carry out different strategies. For instance, this conflictive dynamics emerges when pressures to codify knowledge are opposed to the knowledge socialisation.
2 The PS refers to the institutions that emerge and predominate in a historical period. Therefore, the PS set the way the OS resolves the contradiction CC. Within the NIS framework, those institutions are put up to regulate R&D and the innovation activity. Besides this, those institutions are inherent to other key policy decisions like those involved in the promotion of strategic sectors, those involved in financial regulations, in labour markets legislation, in the industrial property rights’ regime, in educational policies and social cohesion. Furthermore, the PS characterises the national technological learning path and the flexibility (rigidity) of systems. As a result, the PS promotes a certain productive structure and the style of international specialisation.
3 The KB represents the knowledge that has been historically accumulated by the society and that is possible to be applied into the productive or OS. The KB implies certain coordination and it reproduces the knowledge sources that society possesses and needs to feed the innovation process. Those knowledge sources come not only from the technological information, such as R&D activities, patents, new products, processes and other innovations, but also from the structure or productive environment. As a result, the KB shapes the dynamic capacities that support the social learning. Generally speaking, the KB spawns the social cohesion in the Lundvall (2002) sense. Productive actors must continuously both use and feed the KB. This fluid feedback constitutes the fundamental source of the innovation process and the proactive changes. If knowledge were merely used as information or codes in the production process, only reactive changes would be possible and thus an NIS would not be likely to be generated. In our view, the NIS is developed when proactive changes characterise the productive sector.
Figure 1 recapitulates the principal characteristics of the PS-KB-SO (CC) model. Its systemic functioning is the result of the more or less institutional complementarities that are rooted in the social and economic system. The system organisation depends on institutions that guarantee the dynamic coordination between the acquisition of specific capacities and social capacities to innovate. Visibly, there are several features and relationships that are not shown in Figure 1. However, we have chosen ex ante some key aspects of PS-BC-CC model.
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Figure 1 PS-BC-OS (CC) model (see online version for colours)
The system stimulates innovative capacities in developing countries (Albuquerque, 2000; Bernardes and Albuquerque, 2003). However, dissimilar behaviour among countries could be observed, especially regarding innovation efforts. There is a general consensus that Asian newly industrialised countries are quite different from Latin American ones (Freeman and Soete, 1997).3 Two main questions arise from data analysis. On the one hand, not only the ratio R&D/GDP is higher but also R&D private share is much more important in Asian countries. On the other hand, the type of research is more applied to the productive sector in Asian countries than in Latin American ones. For instance, Argentina devotes less than 27% of total R&D to experimental development and the private sector R&D’s share is approximately 30%. These are opposite characteristics compared with the newly industrialised Asian countries. Thus, from these comparisons and comparing many other technology and innovation indicators4 (Naclerio, 2004) a typology can be postulated:
• developed countries
• developing countries type-1 (Tigers, South Asian countries, China and India)
• developing countries type-2 (Latin American).
In Figure 2, we can see three innovative systems. The key to understand this model is the coordination between the three subsystems (PS–KB–OS or CC).
Developed countries have a strong or very strong KB that makes them leaders in technology. Private strategies are faced to the localisation problem. In general, multinational firms tend to localise in developing countries some productive activities for labour cost reasons, though some infrastructure effects that ease the technological activities and stimulate the scientific research can be seen, which turn moving out of the country easier said than done.
Developing countries type-1 have a KB that has been constructed in parallel with industry. The difficulties are presented at the OS level where it raises the conflict vs. the cooperation problem (CC). One of the most significant forms of this CC contradiction is
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due to the international financial crisis. Indeed, the productive system faced some obstacles in the current globalisation or international context. When financial capital dominates productive capital (Chesnais, 1988, 1997; Chesnais and Sauviat, 2003), the accumulation logic is clearly different and long-term investments are relegated (Pack, 2000).5 The scientific infrastructure depends on a long-term policy strategy. When considering the large definition of NIS, these institutions are not only those strictly related to innovation processes (financing innovation, universities, public labs, etc.) but also a good educational system in all levels as well as social cohesion. In this sense, the type-1 model is based on the KB dynamics, which depends on the coordination between private and public actors in the medium and long-term.
Figure 2 Country typology: three different innovation systems (PS-BC-CC model)
Developing countries type-2 are excessively based on the international financial system. This is a crucial reason that explains why GDP might grow in some periods without a learning and innovation process supporting it. Accordingly, the type-2 model follows four phases:
(1) Abundant financial resources, (2) rent growing, (3) more financial resources. This process stops when, (4) a financial crisis bursts out and, as a result, the productive system collapses.
In this kind of system, the investment process does not demand any national KB implication and when this process takes place, the KB becomes feebler. Furthermore, this weakness occurs after the economy has received an important international capital flow.
The vital importance of financial systems cannot be denied. But, when there is no (or very weak) productive system, finance has no role to play. In other words, without NIS and learning processes, the industry is prone to disappear. Finally, what remains is the financial crisis. In this framework, our central hypothesis is that when the KB is not stimulated and there are no NIS institutions related to the productive system, the GDP growth (only based on financial systems) is not sustainable and inexorably leads to economic and social crisis.
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This hypothesis implies that developing countries type-2 are more vulnerable to financial crises. In fact, our decisive piece of reasoning is placed around the problem of international technology transfers (Dosi, 1982; Kuhn, 1970).6 When importing technology (capital goods or codified knowledge), the local productive capacity might be increased, and, at the same time, the national learning might be asphyxiated. As a consequence, excessive foreign technology incorporation may weaken the KB. Every (innovation) system must be able to absorb technology and knowledge coming from the external world, but it must also be able to produce and to resend technology and knowledge to this external world. In other words, if technology is not set up into the KB, technological accumulation will not be able to feed the learning process and, afterwards, the system will not be able to receive any other technology either.
3 Argentina in the 1990s: a developing country type-2
During the 1990s, the Washington Consensus recommendations were applied in Argentina. Meanwhile, as we will verify, there was no innovation effort during that period. This lack of effort is coherent with three important facts:
• Duplicate the payments for technology transfer between 1991 and 1998. In particular, triplicate patent granted to no residents while patents granted to residents diminished between 1993 and 1998.
• Annually, machine importation had been multiplied by seven comparing 1992–1997 with 1986–1990, at the same time that the national industry of capital goods almost disappeared.
• Foreign Direct Investment had been multiplied by five comparing 1990–1997 with 1981–1989.
Accordingly, our main hypothesis is that “importing new technology is not enough to encourage the innovation process, if there is no national effort on technological learning”. Thus, the KB could be weakened, even in a favourable context for technology incorporation.
3.1 Data analysis: innovation effort in the Argentine industry
The data considered involved 1639 firms of the Argentine industrial sector during the 1992–1997 period. We consider the variable ‘innovation’ in its different forms (incremental, radical, change in organisation, product and process) and also the key variable ‘effort’. Innovation effort means that firms should devote a minimum of resources to innovation activities (not only formal activities in R&D, but also informal ones may be included). Surprisingly, almost 60% of the firms that are supposedly innovative do not make any innovation efforts. From INDEC (1998), we will define the variables that are useful to characterise innovation and innovation efforts that firms carry out (see Box 1).
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Box 1 (Definitions) Types of innovation carried out by firms and efforts in innovation
1 Product incremental innovation: Firms that have modified an existing product whose performance becomes clearly superior. This improvement in the product may result in either getting a technologically better product or getting a differentiated one.
2 Process incremental innovation: Firms that have adopted a production method that was technologically improved by including product distribution and logistic methods.
3 Organisational innovation: Firms that have reduced stocks or material costs. Also those firms that are more flexible or have better logistic synchronisation (just in time) or that reduce the rejecting products.
4 New equipment: Firms that have incorporated new machinery to develop new processes and consequently improve the technology.
5 New processes: Firms that have adopted new processes for new products. The objective of new production methods is the fabrication of new products that are not likely to be produced with traditional methods.
6 New processes based on scientific and technological progress: Firms that have been able to change processes by using scientific and technological information.
7 New products: Firms that have made a product radical innovation. The characteristics of the new product (or components) must present significant differences from existing products. New products may result from both the utilisation of new technologies and the combination of technologies responding to new necessities.
8 Effort in innovation7: Firms that have spent an amount of money (minimum) in innovation activities. Not only R&D but also activities whose objective is to improve technology inside the firm.
We have classified our statistical information, considering the concept ‘innovation effort’. We consider three modalities (0, 1 and 2) for our seven innovation variables defined above. Firms that do not innovate (0), firms that innovate but do not make innovation efforts (1) and firms that innovate and make innovation efforts (2). In Figure 3, we sum up the 1639 cases of the sample considering different types of innovation corresponding to each modality.
Figure 3 Innovative firms that make innovation efforts (2); innovative (1); non-innovative (0) (see online version for colours)
Source: After INDEC (1998)
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Following the effort definition, only 33% of firms make innovation efforts. At the same time, firms introduce different types of innovation. Which types of innovation are those that reinforce the KB? In our theoretical frame, our answer tries to identify innovative firms that make innovation efforts. Thus, we consider firms that make efforts like those, which play a proactive role in the KB creation. Those firms are different from the reactive firms that respond to the particular context. In this sense, there are many firms that introduce new machinery but as they do not make any effort, they do not accumulate knowledge in the KB. Those firms correspond to what Lundvall (1992b) and Freeman and Perez (1988)8 labelled ‘Stationary Technology’. In contrast, radical innovation is the type of innovation where the changes in the communication codes make it possible to develop new technologies. Therefore, those changes need innovation efforts that enable the development of new knowledge. For this reason, those innovations feed the KB. At the same time, when there are incremental innovations resulting from user–producer interactions, there is a KB reinforcement.
3.2 Firms typology considering innovation type and innovation effort
In the view of classifying firms by the innovation efforts and the type of innovation they make, a Multiple Correspondence Analysis (MCA) and then a Hierarchical Classification (HC) will be presented in this section. As a result, a typology of firms divided into eight groups or clusters will be presented. We will identify, on the one hand, firms that are innovative (by type of innovation) and, on the other hand, firms that do not make any effort although some of them incorporate new technologies. Afterwards, independent variables (sector, size, cooperation, nationality, financial sources, patent) are more likely to be associated9 to the different clusters.
Multiple Correspondence Analysis (MCA)
The MCA is the first step to sort out innovation variables. The seven variables presented in Figure 3 with their three modalities (0, 1 and 2) are considered. Thus, 21 possibilities. In Figure 4, those possibilities can be seen spread out in a factorial space. To read the axes, the MCA inertia has to be read (Table 1). The inertia rate of F1 axe is 35% and inertia rate of F2 axe is 24%. Thus, 59% of the information is included in the first space factorial (F1 and F2).10
To understand the 21 modalities in Figure 4, the information in Table 2 is considered. The coordinates lead to the points that represent the modalities on F1 and F2. The contribution and the Cosine indicate the best position of different modalities in the two axes.
In F1, “Product Incremental Innovation”, “Organisational Innovation” and “New Product” can approximately be situated. In more detail, when the partial contribution corresponding to each modality is considered, the firms with modality 2 are better situated on axe F1, whereas the other firms (with modality 0 and 1) are better situated on axe F2 (except “organisational innovation = 1”). Consequently, F1 is good to consider the distribution of firms with modality 2 and to consider the opposition between these firms (=2) and all others. On the contrary, F2 represents better “Process Incremental Innovation”, “New Equipment”, “New Process” and “New Process based on S&T”. F2 is a good axe to oppose the modalities 0 and 1.
Figure 4 MCA: innovation and innovation effort (see online version for colours)
Examining Figure 4, we are able to sort out the value (modalities) of the variables. We have three groups. This sorting that we are able to see on the axes concerns the innovation types and the innovation efforts that firms carry out (the columns of the database). Firms (the rows of the database) are not placed in the figure. Considering the number of firms (1639), it would have been impossible to make out the firms’ clusters. For this reason, an HC is necessary.
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The HC permits to identify clusters of individuals (firms) with more accuracy than with simple eye observation. To sort the 1639 firms on the factorial space, we have followed the classification of proximity. The more similar the two firms are, the higher the probability of those two firms to belong to the same cluster11.
Following this method, we will obtain 8 clusters of firms. In Table 312, we present the statistical justification of the partition in 8 clusters. Subsequent to our statistical justification, we are able to present the graph of the HC. In Figure 5, we can see the central point (firm) of each cluster. We can see that clusters A and B are next to modalities 0 (firms that do not innovate), the clusters C, D and E are next
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to the modality 1 (firms that introduce innovations but that they do not make innovation efforts) and clusters F, G and H are next to modality 2 (innovative firms that make innovation efforts).
Table 3 Cluster history
NCL Clusters joined FREQ SPRSQ RSQ ERSQ CCC PSF PST2
Figure 5 Hierarchical classification (see online version for colours)
Reading Table 4, the eight clusters (Figure 5) can be differentiated. When the firms placed in each modality are statistically significant, this type of innovation is a common characteristic of the cluster. Nevertheless, when two or three modalities of a same variable are statistically significant, it becomes rather tricky to attribute a certain modality to the cluster. For this reason, particular attention should be paid to the theoretical interpretation of each cluster.
Cluster A (CL A) contains 291 firms, 18% of the sample. Those firms are the worst in terms of innovation and innovation effort. Modality 0 is characteristic of this cluster.
CL B contains 158 firms that represent almost 10% of total firms. As well as CL A, those firms are not innovative. CL A and CL B are similar except for little changes in the organisation that is carried out by CL B. However, modality 0 is characteristic of this cluster.
CL C contains 490 firms. It is the biggest cluster representing about 30% of the sample. Those firms do not make innovation efforts. Nevertheless, they introduce new
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equipment, new processes and consequently organisational innovation. It cannot be affirmed that incremental innovation, in both products and processes, is a distinctive characteristic of CL C. This type of firms is the most representative of the 1990s in Argentina. They are firms that purchased new equipment (almost all of them imported) and introduced organisational changes. Our critical point is that those firms did not accumulate knowledge in the KB.
Table 4 Characteristics of clusters in Figure 5
CLUSTER Total Type of innovation Modality A B C D E F G H Cas %
CL D contains 101 firms, 6% of total firms. Those firms introduce new processes based on scientific and technological progress. Those firms make use of the KB. However, they do not feed the KB. In other words, even if this cluster can respond to the context, it does not generate the changing of the context. Thus, those firms have a reactive role, as they are able to adapt themselves to a particular situation purchasing a technological package. They purchase but they do not generate technology. But, is it possible to use and understand the new technological packages in an adequate way without carrying out innovation efforts?
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CL E contains 47 firms, only 3% of total firms. Those firms include the ones that do not make any innovation efforts, but that introduce new products. This cluster uses the KB but the firms belonging to this cluster are not capable of feeding and reinforcing the KB.
CL F contains 355 firms, 21% of total firms. This is the biggest cluster among those that make innovation efforts. Those firms introduce new equipment and new processes derived from that new equipment. The fundamental differences when compared with CL C (also introduce new equipment) are that CL F makes innovation efforts. Thus, CL F, irms are more prone to introduce incremental innovation. When the actors interaction is possible (Lundvall, 1992b), then the proactive role is possible. Accordingly, when firms have a proactive role, a systemic change is possible. The passage from individual efforts to systemic efforts is parallel to the transformation of reactive behaviours into proactive behaviours.
CL G contains 102 firms, 6% of total firms. Those firms introduce radical innovations and, particularly, they introduce new processes based on scientific and technological progress. The CL D also introduces this type of innovation, but the CL G makes innovation efforts. Thus, CL G has a proactive role as firms of this cluster interact with the KB.
Finally, CL H contains 95 firms, almost 6%. Those firms have a remarkable innovative performance. They introduce radical innovations, new products making innovation efforts. This cluster may be considered the ‘elite’. It is composed of firms that generate the change in the context. Those firms have a proactive role and they interact permanently with the KB.
Globally, we can point out that CL F, CL G and CL H (33% of total firms) are the clusters that carry out innovation efforts. CL G and CL H implement the proactive role as they introduce radical innovations and, thus, they produce the change of the context. Concerning CL F, this cluster may have a proactive role if their firms have a systemic role. This systemic role implies that firms interact, use common information and knowledge and are able to use different information sources. In this case, it could be said that there is feedback with the KB.
Furthermore, the points (firms) that represent the central clusters in Figure 5 first follow a clear trajectory from CL A to CL E. Here, we have all the clusters that do not make innovation efforts. Those firms are sorted out from the less innovative to the most innovative ones. They may improve and even modify their production processes or improve their products without implementing learning efforts. Thus, the line CL A–CL E does not feed or engender the KB.
Conversely, the line CL F–CL G–CL H marks a break-up with the other clusters. Those firms make efforts to produce new products with new processes or they carry out efforts to improve the existing products or processes. Those firms feed and engender the KB and, in general, they play a proactive role in the innovation process.
This clear division among firms that have a proactive role in generating knowledge on interaction with the KB (CL F, CL G, CL H) and the other firms that do not have any creative role or only reactive role (CL A, CL B, CL C, CL D, CL E) could be explained by the complex and systemic NIS model where we consider that the innovation process is not an action fixed in time like, for instance the acquisition of a new machine.
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The HC method (Figure 5) enables a classification similar to the trajectory found in the MCA (Figure 4). Each cluster is different because the innovation types and the efforts carried out by firms are different. In Table 5, we summarise which the clusters that we have found in our statistical analysis are, indicating the role in innovation process that each cluster has.
Table 5 The clusters of firms: type of innovation and innovation effort
Incremental innovation, new equipments and organisational changes
No CL A, CL B, CL C CL C, CL D CL D, CL E Innovation effort Yes (proactive role) CL F CL F CL G, CL H
3.3 The characteristics of clusters: econometric analysis
The econometric equations are presented in the annexe. The (independent) variables considered so as to characterise the clusters are size, sector, market share, types of financial sources, information or knowledge sources, cooperation agreements, belonging to an international holding, sales growth and patents. In Table 6, the principal associations between dependent variables (clusters) and those independent variables are summarised. Two types of ‘innovative’ firms can be described. First, the principal characteristics of firms that make innovation efforts are pointed out (CL H, CL G, CL F) and, second, the firms that introduce innovation but that do not make innovation efforts (CL E, CL D, CL C).
1 Firms that carry out innovation efforts and make radical innovation (CL G and CL H, equations (6) and (7) in the annexe) can be identified. They are large firms that make use of own information or knowledge sources. The sector dimension is crucial in this frame. There are few sectors that innovate. CL H considered the cluster ‘elite’, which contains large national firms of the pharmaceutical sector. CL G contains other sectors that have introduced process innovations such as pharmaceuticals, beverages, editing–printing, mechanicals and housing products. We cannot confirm that this cluster makes cooperation agreements, but the information sources used by these firms come from other private actors and from public sources. It can be highlighted that CL G and CL H are the most dynamic clusters. However, the sector composition of those clusters is not related to new technological paradigm sectors. In effect, there are no innovation efforts in sectors like information–communication technologies or significant innovation efforts in high-technology sectors. This is a typical trait of developing countries type-2. CL F (equation (5) in the annexe) has introduced new equipments and consequently has changed or improved their production processes. In CL F firms of electric–electronic components, metallurgy and motor vehicle can be found. Those firms use their own financial resources to innovate and their own information or knowledge sources. They also make use of other private knowledge sources. Those firms have patented their innovation. Thus, even if they do not introduce radical innovations, they are able to codify the knowledge they produce.
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2 CL C (equation (1)) contains firms that do not belong to national holdings and that are not large-size firms. CL C is the largest cluster in terms of number of firms. These firms do not make innovation efforts and they import machinery. This type of firms can be associated to the decline of the tool-machinery national industry. In effect, they import machines (so cheap due to a high value of the national currency and to the lack of tariff protection) but they do not make any additional innovation effort. In this sense, the innovation process is fractured, as there is no coordination between users and producers of technologies. CL D (equations (2) and (3)) is the only cluster for which we can affirm that sales have significantly increased. This cluster is also the only one that makes cooperation agreements with public bodies. They are firms of national holdings and they are the only ones that do not use their own financial sources. CL E (equation (4)) is a very particular cluster. They introduce radical innovation (new products) and do not make innovation efforts. There are two significant variables that explain this cluster. On the one hand, the sector dimension is fundamental (mineral products) and, on the other hand, those firms use public financial sources. Thus, those firms are able to carry out big projects for mining exploitations without local innovation efforts.
Table 6 Characteristics of firms clusters: econometric analysis
Firms Non-innovative Innovative without effort (1) Innovative with effort (2)
Cluster Variable A B C D E F G H
Size No No No No – No Yes Yes
Sector Editing – printing
Beverages Editing – printing Housing products
Chemical, rubber and
plastics
Minerals Electric – Electronics components Metallurgy
Motor Vehicles
Beverages Editing – printing
Pharmaceutics Housing products
Mechanical
Pharmaceutics
Financial source
No No Own Not own Public Own Private, own, public
–
Holding No national
– No national
National – – – National
Cooperation agreement
Public – – – –
Information source
Others Others – Own others Own, publics Own
Sales Yes
Patent – Yes – Yes
Summing up, CL F, CL G and CL H are clusters that make innovation efforts and generate feedback with the KB. However, the sector dimension is the key for understanding that these firms have little systemic effects on the KB. It could be accepted that CL F makes innovation efforts and has a behaviour that reinforces the user–producer relationships in the Lundvall (1992b) sense. However, it must be considered that sector firms belonging to CL F (motor vehicles and electronic components) are dominated
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by multinational firms, which do not have R&D labs in Argentina. Concerning CL G, we can see the same sector problem. Finally, the CL H, as we have said, is the cluster ‘elite’. But, this cluster contains only firms of the national pharmaceutical sector and this sector depends on a particular patent legislation (Correa, 1998).13
4 Conclusions
Argentina, in the 1990s, was a developing country type-2 in spite of the production and productivity growth. The key problem here was the lack of innovation efforts seen during that period. This lack of effort was due to both the decline in national capacities to produce new technologies and the excessive technology importation. Thus, it can be concluded: “importing new technology is not enough to encourage the innovation process, if there is no local effort of technological learning”. In fact, without institutional efforts engaged in innovation and learning activities, knowledge accumulation is inexorably relegated.
The technological modernisation triggered off by foreign investment might not be accompanied by a reinforcement of the national creation capacity. This thesis means that KB might be damaged because of the lack of learning efforts. In this sense, Lundvall’s definition of NIS (in its large sense) is crucial and more suitable for understanding the dynamics of developing countries. Institutions, in particular those related to the education system, may bring social cohesion and stimulate capacities of the society as a whole. When that cohesion is absent, learning processes become more difficult. In this way, it is relevant to analyse innovation not only as a final result, but also as the style of institutional coordination that permits to strengthen the learning process and therefore the reinforcement of the KB.
If learning processes are not important, then the national capacities in innovation are prone to disappear. This kind of paradox would be coherent with postulating that multinational firms and the globalisation may weaken the NISs. In this respect, Lundvall stressed that:
“A process of internationalisation based upon multinational corporations might actually weaken the innovative potential not only of a single national system, but also of the global economy as whole.” (Lundvall, 1992b, p.65)
Subsequently, the following paradox may be postulated: after technological modernisations process, a destruction of national capacities might take place.
Finally, the liberal policies carried out in Argentina weakened the KB almost to the point of its destruction. Nevertheless, throughout the 1990s, the Argentine productive sector was reached by an important flow of foreign direct investment. Moreover, massive importation of technologies and financial valorisation favoured economic growth. Consequently, it can be concluded that this growth process was rather volatile and transitory and that it caused the severe and long (three years and a half) economic recession initiated in the third term of 1998 and the brutal social 2001–2002 crisis.
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Freeman, C. (1995) ‘The ‘national system of innovation’ in historical perspective’, Cambridge Journal of Economics, Vol. 19, pp.5–24.
Freeman, C. (2002) ‘Continental, national and sub-national innovation systems – complementarity and economic growth’, Research Policy, Vol. 31, pp.191–211.
Freeman, C. and Perez, C. (1988) ‘Structural crises of adjustment and investment behaviour’, in Dosi, G., Freeman, C., Nelson, R., Silverberg, G. and Soete, L. (Eds.): Technical Change and Economic Theory, Pinter, London, New York, pp.38–66.
Freeman, C. and Soete, L. (1997) The Economics of Industrial Innovation, 3rd ed., The MIT Press, Cambridge.
INDEC (1998) Encuesta Sobre La Conducta Tecnológica De Las Empresas Industriales Argentinas, Estudio No. 31, INDEC, República Argentina.
Kuhn, T. (1970) The Structure of Scientific Revolutions, The University of Chicago Press, Chicago. Lall, S. (1999) ‘Science technology and innovation policies in East Asia. Lessons for Argentina
after the crisis’, Conference SECyT, Ministerio de Educación, Rep. Argentina, 06/09/1999, pp.13–83.
Lall, S. (2000) ‘Technological change and industrialization in the Asian newly industrializing economies: achievements and challenges’, in Kim, L. and Nelson, R. (Eds.): Technology, Learning, and Innovation, Cambridge University Press, Cambridge, pp.13–68.
Le Moigne, J-L. (1990) La Modélisation des Systèmes Complexes, DUNOD, Paris. List, F. (1857) Système National d’Economie Politique, Gallimard, Paris. Lundvall, B-Å. (1992a) ‘Introduction’, in Lundvall, B-Å. (Ed.): National System of Innovation:
Towards a Theory of Innovation and Interactive Learning, Pinter, London, New York, pp.1–19.
Lundvall, B-Å. (1992b) ‘User-producer relationship. National system of innovation and internationalisation’, in Lundvall, B-Å. (Ed.): National System of Innovation: Towards a Theory of Innovation and Interactive Learning, Pinter, London, New York, pp.45–94.
Lundvall, B-Å. (Ed.) (1992c) National System of Innovation: Towards a Theory of Innovation and Interactive Learning, Pinter, London, New York.
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Naclerio, A. (2004) La Dimension Systémique du Système National d’Innovation: Une Application au cas de l’Argentine, PhD Thesis, Université Paris 13, France.
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Notes 1C. Freeman quotes F. List many times (for instance Freeman, 1995, 2002; Freeman and Soete, 1997). In particular “The National System of Political Economy”. From this concept, he postulates the National System of Innovation. Another background concept is brought in by François Chesnais who thinks about technological learning and independent production systems based on the Scientific and Technological System (OCDE, 1974).
2For systemic analysis, it was considered the general system theory (von Bertalanffy, 1962, 1968) and complex systems modelisation (Le Moigne, 1990).
3Many authors (Lall, 1999) have explained these differences. See for instance Freeman and Soete (1997), pp.305, 306.
4Not only R&D data. Fundamentally, it is observed the ratio exportation/importation of technologies and the transfer of technology among countries, Foreign Direct Investment and the local innovation related to investment, and sector specialisation observing sectors that are capable of spreading out technology progress. It has also taken into account educational performance in all levels for different countries.
5For a particular context of the 1997 Asian crisis, Pack (2000) highlights that Asian countries are faced to the institutional responsibility of generating new learning trajectories after the financial crisis. In general, he refers to more flexibility in the economy and agile and quick responses. See also Lall (2000).
6This process is relevant for us regarding the changing paradigm context (Dosi, 1982; OCDE, 1992; Kuhn, 1970) and also regarding what Regulation Theory entitles “institutional forms or accumulation regime”. The ‘Fordist regime’ was replaced by a “global finance-dominated accumulation regime” (Chesnais and Sauviat, 2003).
7This effort definition may be not clear-cut, as firms make not only pecuniary efforts but also other coordination and knowledge accumulation efforts. However, this effort definition is large enough (to be a firm that makes innovation efforts, only one-person employed in innovation activities or only one dollar granted to improve the technology inside the firm are needed). It is understood that firms interact better with the KB when they make innovation efforts (as we have developed in part 1 of this paper).
8Lundvall (1992b) considers four types of innovation: 1) stationary technology, incremental innovation, radical innovation and technological revolution. See also Freeman and Perez (1988).
150 A. Naclerio
9We applied Binary logit Method for econometric work. Independent variables are defined in the annexe.
10From a statistic viewpoint, it is difficult to comprehend the secondary axes. This is why we only consider F1 and F2. Axes F3 and F4 are corrective terms of the proximities verified in the principal axes.
11This criterion follows these steps: first, the total sample is considered (1639 firms); second, a distance matrix among firms, which results from the MCA, is built up. The classification algorithm regroups the nearest elements in pairs. Thus, a group of 1638 firms (n – 1) instead of 1639 (n) is first obtained. The next step is a second grouping with 1637 firms (n – 2 clusters) that contains the first. This procedure continues till only one element remains, containing all the elements of the last cluster.
12Considering eight clusters, we have another cluster loss (represented by semi partial R2 SPRSQ) of 0.028. ERSQ (Equal R2) means that 43% of the information is summarised in the eight clusters. Also, the CCC (cubic clustering criterion is superior to 3 when the number of clusters is eight, which represents a good classification. Moreover, when we have eight clusters, the static Pseudo F (PSF) y Pseudo T2 (PST2) correspond to a peak of PSF and at the same time, to weak PST2 followed by a strong PST2 in the next aggregation. As we can see in the figure:
13In particular TRIPS’ agreements. For further analysis of the Argentine case see Correa (1998).
Annexe I
Definition of independent variables used in the econometric equations:
I Size: This variable has six values considering the number of person working inside the firms:
1 EMP1: less or equal to 20 persons
2 EMP2: more than 20 and less or equal than 49 persons
3 EMP3: more than 50 and less or equal than 99 persons
4 EMP4: more than 100 and less or equal than 199 persons
5 EMP5: more than 200 and less or equal than 499 persons
6 EMP6: more than 500 persons.
II Sector: Following the United Nations Nomenclature adapted to argentine’s case (Table A1).
Innovation system and developing countries: the Argentine’s failure 151
III Market share: Corresponds to the firms’ total sales in relation to the firm sector’s total sales. The share market ‘Ca’ (a = 4, 8) shows the sales of a first firms related to the sector’s total sales. When Ca is nearer to 100%, the market is more concentrated. Hirschman Herfindahl’s index is equal to the addition of the square of market share of each firm (total firms = n) belonging to a certain industry. When its value is nearer to 1, the market share is stronger (see Table A1).
IV Variation % of total sales: For each sector between 1992 and 1997 (see Table A1).
V Financial sources: This variable has four values:
1 Public: when the funds are granted by a public bank or public programme in at least 10% of total cost of the project
2 International: when funds are granted by a foreign body in at least 10% of total cost of the project
3 Own funding: when the funds are granted by the firm in at least 10% of total cost of the project
4 Private: when the funds are granted by a private bank, a client or a supplier in at least 10% of total cost of the project.
152 A. Naclerio
VI Cooperation agreements: This variable has two values: ‘yes’ or ‘no’. When ‘yes’ there are three possibilities
1 International: when the firm makes agreements with international bodies or firms
2 Private: when the firm makes agreements with other private firms, consultancies, etc.
3 Public: when the firm makes agreements with research centres or national universities.
VII Information sources:
1 Own sources: when the firm innovates using its own information
2 Public: when they use information from universities or research centres
3 Other sources.
VIII Belonging to a holding: Firms that belong to a holding may belong to
1 international holding
2 national holding.
IX Patent: Patents granted to firms.
Annexe II: Equations (Binary Logit method) (see online version for colours)
Innovation system and developing countries: the Argentine’s failure 153
Annexe II: Equations (Binary Logit method) (see online version for colours) (continued)
Equation (1). Dependent Variable: CL C Method: ML – Binary Logit Sample: 1 1639 Included observations: 1533 Excluded observations: 106 Convergence achieved after four iterations Variable Coefficient Std. error z-Statistic Prob.
Sector (Reference: Clothes, footwear and garment) Motor vehicles –0.530 0.370 –1.433 0.152 Mechanical –0.204 0.295 –0.693 0.488 Electric and electronics –0.250 0.346 –0.723 0.470 Minerals –0.224 0.352 –0.637 0.524 Textile 0.239 0.284 0.840 0.401 Wood and paper –0.063 0.333 –0.188 0.851 Chemical, rubber and plastics –0.537 0.311 –1.724 0.085 Metallurgy –0.189 0.308 –0.615 0.538 Electric and electronics components 0.344 0.511 0.673 0.501 Energy –0.046 0.666 –0.069 0.945
Financial sources International 0.125 0.238 0.526 0.599 Private –0.048 0.138 –0.348 0.728 Own funding 0.217 0.130 1.665 0.096 Public 0.058 0.132 0.442 0.659
Information sources
Other sources 0.403 0.158 2.549 0.011 Own sources –0.144 0.150 –0.955 0.340 Public –0.043 0.260 –0.163 0.870 Belonging to an international holding –0.250 0.184 –1.356 0.175 Belonging to a national holding –0.345 0.163 –2.108 0.035
Variation of total sales 0.072 0.095 0.753 0.451 Mean dependent var. 0.302 S.E. of regression 0.451 Sum squared residue 304.933 Log likelihood –894.324 Restr. log likelihood –939.067 LR statistic (33 df) 89.486 Probability (LR stat.) 4.1E-07
154 A. Naclerio
Annexe II: Equations (Binary Logit method) (see online version for colours) (continued)
Equation (1). Dependent Variable: CL C Method: ML – Binary Logit Sample: 1 1639 Included observations: 1533 Excluded observations: 106 Convergence achieved after four iterations Variable Coefficient Std. error z-Statistic Prob.
S.D. dependent var. 0.459 Akaike info criterion 1.211 Schwarz criterion 1.329 Hannan-Quinn criterion 1.255 Avg. log likelihood –0.583 McFadden R-squared 0.048 Obs. with Dep. = 0 1070 Obs. with Dep. = 1 463
Equations (2) and (3) Dependent Variable: CL D Method: ML – Binary Logit Sample: 1 639 Included observations: 1533, Excluded observations: 106 Convergence achieved after seven iterations
Equation (2) Equation (3)
Variable Coeff. Std.
error z-
Statistic z-Prob. Coeff. Std.
error z-
Statistic Prob.
C –3.571 0.683 –5.225 0.000 –3.648 0.692 –5.275 0.000
