paper brittovargassatllivieri rip 2014 id1379

8/10/2019 Paper BrittoVargasSatllivieri RIP 2014 ID1379

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UIS RIP-9th Regional Innovation Policies Conference

Paper ID 1379

The conceptual support of the academy to the implementation of Regional Innovations Policies in

Brazil: Learning, Cooperation and Innovation in Local Productive Arrangements (LPAs)

Jorge Br i tto - F luminense Federal University (UFF) and RedeSistMarco Vargas - Fluminense Federal University (UFF) and RedeSist

Fabio Stall ivieri - Fluminense Federal Universi ty (UFF) and RedeSist

Abstract: Based on the Brazilian experience, the paper discusses how concepts originally formulated inthe academic sphere can be used as instrumental and methodological tools for the implementation ofregional innovation policies. A horizontal-territorial focus of the regional innovation policy is discussed,comprising the use of the concept of Local Productive Arrangements (LPAs), originally developed in theacademic sphere and widely adopted as a tool for the implementation of public policies in Brazil. Theanalysis tries to identify general patterns regarding the innovative dynamics of enterprises inserted inLPAs, evaluating the influence of learning processes and cooperative networks at the local level to the

strengthening of the innovative performance of those companies.

Key words: Conceptual Support to Regional Innovation Policies; Local Productive Arrangements;Learning and cooperation; Horizontal-territorial policies

Introduction

The evolutionary approach of Industrial Economics has pointed the importance of connecting thecharacteristics of the knowledge generation and the identification of critical dimensions of industrialagglomerations. The discussion about how knowledge is generated, appropriated, distributed and

enhanced might contribute to understand how those agglomerations work, allowing not only todifferentiate them according to a greater or lesser degree of complexity but also to evaluate their potentialto evolve along a virtuous path of competence growth. In an evolutionary perspective, a major feature ofthose agglomerations refers precisely to their ability to operate as a mediator between the firm and theexternal environment, which increases the capacity of absorbing knowledge potentially useful for thestrengthening of efficiency, innovativeness and competitiveness. These agglomerations might redefine thedichotomy between "internal" and "external" sources of knowledge, acting as an intermediate instancewhich allows to "format" the knowledge according to the requirements of the competitive process,

providing relevant externalities, stimulating the integration of competences and generating multiple spill-over effects. However, despite the recognition of the learning process as a critical aspect of this dynamics- empirically illustrated by a growing number of case studies there still a gap regarding cross-sectoranalyzes that enable the identification and quantification of those gains at the firm-level.

Two main objectives orient the analysis developed in the paper. First, it describes and analyzes arelevant experience related to the transfer of analytical concepts developed in the academic sphere to thedesign and implementation of a regional innovation policy in Brazil. Specifically, this experience refers tothe contribution of the concept of "Local Production Arrangements" (LPAs), elaborated as a variant of the

broader concept of "industrial agglomerationsto the strengthening of a regional focus of the innovationpolicy. This concept refers to a specific set of economic activities spatially located and sectorallyspecialized, oriented to learning practices and to the generation and diffusion of new products and

processes. Second, the article tries to expand the understanding of the relationship between territorialproximity, cooperation and innovation, based on an analytical framework that seeks to articulate the

intensity of learning and innovative processes to elements that emerge from territorial specificities.The article comprises three sections. First, the analysis discusses the functionality of the concept

of Local Production Arrangements (LPAs) developed from a theoretical evolutionary perspective for the


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definition an implementation of regional innovation policies. Second, the analysis discusses the support ofthe academic sphere to provide an instrumental and methodological basis to the implementation ofregional innovation policies in Brazil, comprising the use of the concept of Local ProductiveArrangements (LPAs) as a tool for the implementation of public policies. Third, the analysis tries toidentify general patterns regarding the innovative dynamics of enterprises inserted in Local ProductiveArrangements (LPAs), evaluating the influence of learning processes and cooperative networks at the

local level to the strengthening of the innovative performance, of those companies. The analysis is basedon data collected from 1,187 companies inserted in 29 Local Productive Arrangements (LPA), trying toidentify clusters of firms with similar patterns regarding the characteristics of innovative efforts, learning

practices and insertion in local cooperative networks.

1. The concept of Local Production Arrangements (LPAs)

The concept of Local Production Arrangements (LPA) was developed as a variant of the broaderconcept of industrial agglomerations, recurrently used as an analytical approach to discuss aspects relatedto the territorial competitiveness by the modern literature of Industrial Economics and RegionalEconomics. The basic assumption of those analyses is that industrial agglomerations might provide

positive externalities at the territorial level, increasing productive efficiency and creat ing a suitableenvironment to the raise of innovativeness and competitiveness of the firms located in the territory(Maskell and Malmberg, 1999; Maskell and Kelbir, 2005).. The use of this analytical category to discussstructural conditions that affect firms competitiveness goes back to classical theoretical approaches,starting from the works of Marshall (1890). These approaches have generated important analyticaldevelopments in the field of the New Economic Geography (Krugman, 1991 and 1995), StructuralistRegional Economics (Storper, 1996 and 1997, Scott and Storper, 1986, Piore and Sabel, 1984),Innovation Economics (Audretsch, 1995; Audretsch and Feldmam, 2004; Maillat, 1995 and 1998; Maillatand Kelbir, 1999) and in the literature about modern Industrial Districts (Schmitz, 1997; Nadvi andSchmitz, 1994; Musyck and Schmitz, 1995; Pyke, Becattini and Sengenberger, 1990). At the same time,the concept is becoming increasingly present in the policy guidelines of international development

agencies (OECD, 2001 and 2007, World Bank, 2009)A proliferation of empirical studies developed from an evolutionary theoretical perspective

contributes to the refinement of the concept of industrial agglomerations. From these studies, somerelevant attributes of those agglomerations may be stressed: 1) Geographical proximity; 2) Sectoralspecialization and intra-sectoral division of work; 3) Close inter-firm collaboration; 4) Inter-firmcompetition essentially based on innovation rather than on lower wages; 5) Social embeddedness thatfacilitates trust, reciprocity and social sanction; 6) Different forms of state support. Bell and Albu (1999)develop an analysis of the elements that strengthen the integration of capabilities in the knowledgesystems associated to those agglomerations, stressing the differences between elements that increaseknowledge-using capabilities and elements that increase knowledge-changing capabilities. Concerningthe first aspect, they mention the passive experience of production ( learning by doing in production'') atthe firm level, the active efforts to adopt and improve specific technologies and the improved practicesderived from trial and experimentation on specific tasks. At the level of the agglomeration, they mentionthe mobility of skilled labor, the improvement of operational skills and the knowledge diffusion ofspecialized machinery or production-related services. Concerning the knowledge-changing capabilities atthe firm level, they mention the technological understanding gained from investment efforts (learning bydoing investment'') and the generic technological insights gained from adapting and improving existingtechnologies (learning by changing''). This dimension involves collective practices in planning andtechnology management, as well as collaboration to adapt machinery, to improve processes or to develop

product designs. The creative collaboration between firms and local technology-based institutions seemsalso to be very important.

The evolutionary approach also argues that geographical proximity is not enough for theachievement of collective learning processes and innovative dynamism. To amplify these effects, this

proximity has to be articulated with other elements, such as the institutional, cultural and technological


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context, in order to foster the existence of an innovative system. The Regional Systems of Innovation(RSI) concept rests on the relationship between technology, innovation and industrial location (Mothe andPaquet, 1998; DAllura, Galvagno and Destri, 2012), highlighting the regional dimension of the

production and the exploitation of new knowledge, thereby helping to explain regional differences ininnovation capacity and economic strength. RSIs usually consist of a set of interacting private, semi-

private and public organizations, interacting within an institutional framework which stress the generation,

exploitation and dissemination of knowledge and thus supports innovative activities on a regional level(Cooke, Uranga and Etxebarria, 1998; Doloreux, 2002).

According to this perspective, the presence of multiple ties among local actors performs a criticalrole to strengthen competence-building processes in industrial agglomerations that conform RSIs. Theestablishment of those ties may provide the necessary conditions to promote localized learning processesand to consolidate innovative paths based on incremental innovations. On the other hand, in order toavoid the danger of a geographical lock-in related to the exhaustion of learning processes, theagglomerations might also retain capabilities to break productive practices and to change technological

paths (Iammarino and Mccann, 2006; Paniccia, 2005; Cooke and Morgan, 1998; Cooke, 2001; Menzeland Fornahl, 2009). According to Christopherson, Michiel and Tyler (2010), these processes generate akind of regional resilience, defined as the capacity of a territory to overcome short -term or long-term

economic adversity (Hudson, 2010; Martin, and Sunley, 2006; Pike, Dawley and Tomaney, 2010). Thisresilience would be provided by a strong regional system of innovation (Clark et al., 2010; Howells, 1999)and by the effective creation of a learning region (Archibugi and Lundvall, 2001).

The territorial proximity between agents inserted in a similar social, cultural and institutionalcontext enhances cooperative practices that reinforce learning gains (Johnson and Lundvall, 1994). Non-economic factors, socially defined rules and local institutional conditions affect the interactions betweeneconomic agents, generating incentives for cooperation and learning. The evolutionary approach tries toarticulate the static competitive advantages generated by the spatial agglomeration with dynamiccompetitive advantages obtained through the strengthening of learning practices and multiple forms ofcooperation. The systematic interchange of information and knowledge generates a process of collectivelearning, which accelerates the diffusion of technological and organizational innovations. These flows

involve intangible assets and the circulation of tacit knowledge. Although innovations intentionallydeveloped in co-operation tends to occur only in more structured systems, there are many possibilities toimprove the competitiveness of local productive systems due to informal mechanisms of learning. Theevidence also shows that the circulation of information and skilled workers would improve thecompetences of the firms inserted in those agglomerations. Another aspect refers to the impacts of theinterchange of information to the definition of industrial standards, normalization procedures and qualitycontrol techniques.Given the tacit character of knowledge, innovation usually requires several forms ofinteraction among economic agents, who in turn interact with technology-based and knowledge-basedinstitutions. In this sense, learning-by-interaction becomes a critical aspect of industrial agglomerations.Typically, interactions develop in the form of cooperative efforts, formal or informal. Then, cooperationconstitutes the main instrument to improve learning-by-interacting practices. While cooperation is aneffective tool for information processing, it is also an important alternative to enable the binding ofcomplementary skills, to increase productive efficiency and to improve the innovative potential of inter-industry arrangements.

In this context, the technological development of a firm becomes increasingly dependent on thecapabilities of other firms, competitors, clients and suppliers, being possible to differentiate horizontalcooperative links among firms inserted in similar stages of the value chain and vertical cooperative linksinvolving firms, suppliers, customers and other organizations. Among those organizations, it can bementioned research centers, technical schools, public institutions and private representative associations.All these agents conform the complex institutional context in which cooperative links emerge. Theexternal learning can complement but not replace the internal, increasing its effectiveness or changing its

direction. Particularly in knowledge intensive sectors, the viability of the innovation process requires adirect and permanent interaction between firms and different sources of information, through whichcapabilities could be calibrated, adjusted and incremented over time.


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Based on this perspective, the Research Network on Local Production Arrangements andInnovation Systems (RedeSist) - established as a Brazilian academic network with international linkssince 1997 - developed the concept of Local Production Arrangements (LPAs) in the late 1990s, with afocus on a set of economic activities spatially located and sectorally specialized. Some critical aspects ofthese arrangements involve learning practices and the generation and diffusion of new products and

processes, combining elements of the Evolutionary Neo-Schumpeterian theoretical approach about

innovation systems with contributions about industrial development and structural change elaborated bythe Latin American structuralist school (Lastres, 2007; Cassiolato and Lastres, 2008; Cassiolato, Lastresand Maciel, 2003). The focus on LPAs comprises a systemic view of the productive and innovativeactivities, considering a multiplicity of economic, political and social actors that define the contours ofthose activities in the territory.

The methodological framework that sustains the analysis of LPAs argue that geographicalproximity is not enough for the achievement of collective learning processes and innovative dynamism.The concept tries to articulate the static competitive advantages generated by the spatial agglomerationwith dynamic competitive advantages obtained through the strengthening of learning practices andmultiple forms of cooperation. The complexity of knowledge flows, the multiplicity of the relations, theintensity of interactive learning mechanisms and the degree of cooperation among agents are factors that

interfere in how learning processes take place, and therefore, in the generation, use and diffusion ofknowledge. The mapping of this diversity seems to be an important analytical tool to understand howthose processes occur and change over time.

2- Policies to Local Production Arrangements (LPAs) in Brazil

The concept of Local Production Arrangements (LPAs) has quickly disseminated not only in theacademic sphere as well at the policy level, covering a broad phenomena referring to the concentration ofsimilar or interdependent activities in economic space, without restrictions about the size of thecompanies, nor about the nature of economic activity, which can be primary, secondary or tertiary. Theincorporation of this approach in the sphere of public policies occurred so early and fast, going to replace

other similar frameworks on political agendas. Given the breadth of the concept to characterizeproductive agglomerations, the Federal Government, under the Permanent Working Group for LocalProductive Arrangements (GTP-APL), integrated to the Ministry of Development, Industry and ForeignTrade (MDIC), opted to incorporate the general terminology of LPA. The efforts to internalize the LPAframework to the support the federal programs involve a compromise between the original frameworkelaborated by the academia and the experience accumulated by policy institutions over many years. Inthis sense, the adoption of the concept seems to be very flexible, becoming a general guide to theoperational strategies of different institutions, whose strategies effectively have remained focused on thetraditional institutional subjects and on the general mission of each agency.

The institutional set-up of agencies engaged with the implementation of the innovation policy inBrazil comprised some key actors as well as some specific mechanisms of coordination. Theimprovement of the effectiveness to the Innovation Policy has involved the integration of the Science andTechnology Policy with the Industrial Policy. Each of these policies have been implemented by a specificMinistryrespectively, the Ministry of Science, Technology and Innovation (MCTI) and the Ministry ofDevelopment, Industry and Foreign Trade (MDIC). MDIC has also under its purview BNDES the (the

National Bank for Economic and Social Development) which constitutes the main source of longtermfinance for Brazilian companies.

The incorporation of the approach of LPAs as an instrument of policy occurred since 1999 underthe Ministry of Science, Technology and Innovation (MCTI). In partnership with federative states, LPAswere identified in projects that aimed to support the cooperation between research institutes and industrialfirms. It also included for the first time the mention of LPAs in the general framework of the

governments Multiyear Plan (PPA 2000-2003), in which the responsibility of the MCTI to support thosearrangements was explicitly mentioned. In this period, there was also an increase of government supportto academic research about the theme, both theoretical and empirical. The actions of MCTI were


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implemented through the support provided by its agencies, the Council of Scientific and TechnologicalDevelopment (CNPq) and the Financier of Studies and Projects (FINEP).

In 1999 began the collaboration between the MCTI and the Forum of Federal States Secretaries ofS&T for the identification and support of LPAs. This work adopted a methodology based on the broaderconcept of Technology Platforms, mobilizing local actors to identify bottlenecks and to propose solutionsfor specific problems of LPAs. In this effort, each federative state was tasked to identify three

arrangements to be supported. Platforms supported by MCTI were 54 in 2000, 53 in 2001 and 42 in 2002.In this period, CNPq handled the operation of the program to support innovation in LPAs, supported bygrants from FINEP, which had also created in 2001 a specific action to run this program, calledStructured Action for LPAs, implemented by the Area of Innovation for Regional Development - ADRE.Since 2003, with the beginning of the first term of the Lula government, the MCTI had undergone aninternal restructuring and the coordination of initiatives related to the support of LPAs was transferred tothe Minister of Development, Industry and Foreign Trade (MDIC), with MCTI no longer having aspecific line to support LPAs in the period 2004-2007.

Since 2003, while the policy for LPAs gained greater prestige and political support, not only at thefederal but also at state and municipal levels, the MCTI pioneer in the adoption and implementation ofthis approach - lowers the priority given to it. This trend might be connected to a recurring issue

concerning the main focus of the Brazilian policy for LPAs that can be identify not only in MCTI actions,but also in other institutional bodies that have taken this approach in their actions: an apparentdissociation between promoting innovation and local industrial development. This attitude, in turn,reflects a supposed decoupling between technological development and social development. Thus, itappears that while some bodies associated with the promotion of LPAs have tended to focus on thesupport for innovation (such as FINEP in the period 1999-2003), others have seemed to be primarilyconcerned about the promotion of local development (such as MDIC in Lula's first term).

Under the FINEP, the Program for Support Research and Innovation in Local ProductiveArrangements (PPI - APL) was reformatted to support activities undertaken by institutions of science andtechnology (ICTs) in cooperation with companies inserted in LPAs, focused on R&D, technologicalassistance, technological services and technological troubleshooting. Simultaneously to FINEP actions, it

was created in the first half of 2003 an inter-ministerial group to support LPAs, aiming to integrateactions implemented by different bodies, coordinated by the Ministry of Development, Industry andForeign Trade (MDIC), with the participation of 21 agencies working at the federal level, as well as othergovernmental and non-governmental bodies. This group was formalized in August 2004, with the nameof the Permanent Working Group for LPAs (GTP-APLs). The Multi-Year Plan (PPA) 2004-2007, andlater the PPA 2008-2011, have also incorporated the theme LPA in their structure. Coordinated by theMinistry of Development, Industry and Foreign Trade (MDIC), the GTP - APL was expanded in 2005,with the inclusion of over 10 institutions (totaling 33). The first goal of GTP - APL was to coordinate,articulate and integrate the different actors, policies and actions to promote LPAs at the federal level,carried out by public and private bodies. The main actions developed initially were directed to conceptualissues and to the establishment of a consensus criteria for the classification of LPAs, in order to permit ageneral identification of LPAs around the country and to construct a database containing the mapping ofall actions performed by existing bodies involved with LPAs or similar approaches.

Reflecting this effort, 27 Support Nuclei to LPAs were installed in federative states in the period2006-2008. The institutional design of these Nuclei was very heterogeneous, following the historical-institutional trajectory of each federative state concerning this issue, with different institutional settingsand different legal frameworks. The federal banksincluding not only BNDES, but also Banco do Brasil,Caixa Econmica Federal, Banco do Nordeste and Banco da Amaznia - also began to use or expand theuse of a LPA approach in their operations. There is also an increasing interest not only of these public

banks, but also from private banks that have integrated later the GTP-APL. This interest reflects the aimto extend financial services to smaller companies inserted in LPAs and the recognition that such a

strategy facilitates the proximity of actors, their formalization and their access to the financial system.The creation of the GTP-APL occurred within the framework of the first experience of industrial

policy in Lulas government, the Industrial, Technological and Foreign Trade Policy (PITCE), elaborated


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in 2003. The issue was articulated to the strategic theme "Regionalization" included in the ProductiveDevelopment Policy (PDP), the general framework of the industrial policy implemented in 2008, as wellas to the cross-cutting theme Special Actions in Regional Development, mentioned the in thesubsequent Brasil Maior Plan launched in 2011. It is also important to highlight the spread of the conceptof LPAs as a guiding policy to other instances of the government. In the case of BNDES, the main agencyfor the promotion of industrial development, the incorporation of that concept in operational practices

resulted from the need to articulate long-term national policies with regional and local priorities. In thiscontext, challenges were associated with the need to increase the presence of BNDES in the regions andfederative states less attended, reducing intra-regional imbalances. This process was reflected in theestablishment in 2007 of the Committee of Productive Arrangements, Regional Development, Innovationand the Environment, as well as in the creation of a Special Office for the Development and LocalProductive Arrangements, directly linked to the BNDES President. These instances try to articulatevarious operational areas of BNDES, contributing to incorporate regional development and a territorialsystemic approach to their lines of action.

The GTP - APL had contributed to the spread of the concept of LPA, allowing the exchange ofexperiences and cooperation among public and private stakeholders at the national and federative statelevels. It also contributed decisively to the improvement of information systems and to the use of

indicators for the assessment of policies to support LPAs (Lastres, 2011). Three key mechanisms oflearning emerged from this experience. First, it indicates that it is possible to overcome the limits ofoccasional, strictly sectoral and one-dimensional policies, advancing in the understanding that the

productive development depends on the interaction between multiple actors and institutions, includingthose responsible for the generation of knowledge, funding and representation. Second, it indicates that itis possible to overcome policy models driven by an administrative logic that reduce the politicalmanagement to unique and de-contextualized frameworks, based on general parameters andmethodologies, which, at the end, tend to reinforce sectoral, social and territorial inequalities. Thirdly, itindicates that the territorial dimension constitutes a fundamental issue to be considered in infrastructure

projects, not only at the macro-regional, but also at the sub-regional and local levels.However, despite the progress achieved, there were some limitations intrinsic to this pattern of

policymaking. Some of these limitations involves the development of general methodologies to select andclassify LPAs, many of which start from a recognition of the existence of arrangements in different"stages" incipient, potential, stagnated, dynamic, mature, world-class etc. Additionally, there were alsolimits to the application of traditional rules and quantitative methods to characterize territorialagglomerations (and consequently LPAs) and to define their formats, which are often based on modelsand typologies overly schematic. There were also situations in which policy programs seek to "build"cooperation and governance, treating firms and other actors as patients who hypothetically need to learnhow to interact, cooperate and innovate. Thus, it is common to impose policy prescriptions based onmodels that ignore local conditions in terms of their historical and socio-political context. Another

problem comprises the implementation of policies to support LPAs as part of a strategy that lacksconvergence with other policies of the Federal Government. In this sense, Brazilian experience shows anemphasis on traditional compensatory actions in which the promotion of innovation in LPAs is often

placed in a disconnected manner, or even in opposition to the promotion of local development and socialinclusion. The need to train a staff of policy-makers and local agents qualified to deals with thecomplexity of the development of LPAs also constitutes an important challenge for the advancement ofthe policies.

To overcome these threats, a relevant issue refers to the effective contribution of the academia tothe improvement of regional innovation policies based on a LPA approach. In this sense, three types ofcontribution might be stressed. The first involves academic professionals specialized in discussing andformulating normative conceptual guidelines for the definition and implementation of those policies. Thesecond involves a more direct contribution, through which qualified personnel originated from academia

assume a relevant executive role in the implementation of those policies. The third comprises thecontribution of the academic sphere to the training of the staff responsible for the implementation of those

policies at different institutional instances. Regarding these contributions, it is important to highlight the


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role of research networks with a "dual" character, dedicated to both academic research as well as tomethodological and operational support of policy implementation through consulting activities. Thecontinuity of the policies implemented favors a process of learning and the accumulation of a "criticalmass", which enables their adjustment and calibration in order to achieve a greater efficiency in theimplementation of actions.

The Brazilian experience concerning the implementation of a regional innovation policies based

on a LPA approach reflects this trend. The establishment of channels of communication between theacademic sphere and policy-makers has allowed an exchange of information and experiences that

promote policy adjustments, increasing or reducing the scope of actions according to the results generatedand to the responses of the audience of the policies. These adjustments reflect a process of "institutionalinteractive learning" that mobilizes and articulates the academic sphere and policy-makers instances,which can improve the effectiveness of the innovation policies. Reflecting this trend, the identification ofa set of problems related to the implementation of regional innovation policies based on a LPA approachresulted in a movement towards a "2nd generation" of policies. These adjustments reflect three dynamics:1) a natural process of "institutional learning " on the part of policy makers; 2) a (self) diagnosis about thecritical limits of the current standard implementation of these policies and of the challenges for theirfurther development; 3) a return to the academic sphere in order to get a conceptual and methodological

basis suitable to the reformulation of the policies.Two general principles guide this evolution. The first refers to the reinforcement of the cohesion

of the local development through an orientation to the economic potential of the territories and to theirinstitutional and productive environment, even when considering the surroundings of large projects. Thesecond principle incorporates the notion of a sustainable development trajectory, linking the differentdimensions of economic development with an emphasis on sustainable exploitation of socio-biodiversity,exploiting new niches based on clean technologies and activities socially and environmentally responsible.This evolution towards a 2nd generation of policies to support LPAs relies too heavily on a coordinationwith the academic sphere, both in terms of conceptual and methodological foundations as well as in theformat of actions and programs .

Concerning these changes, it is possible to mention the demand of the GTP- APL towards the

format of a training program by the academy directed to managers responsible for formulating andimplementing policies for LPAs at different governmental levels. The creation of the ThematicCommittee "National Training Plan" by GTP-APL reflects this trend. To format this program threedifferent audiences were identified: 1) Formulators and Operators of LPAs Policies; 2) Managers andlocal multipliers agents; 3) Entrepreneurs and local productive agents. Another contribution of theacademy refers to the structuring of an Integrated Knowledge Management System to LPA policies,through the establishment of the Brazilian Observatory of LPAs. The structure of this nationalobservatory comprises the creation of a database, an Internet portal with various features and a socialnetwork, operated by the GTP-APL with the support of the representatives of the Nuclei established infederative states. Another important contribution of the academy refers to the identification and the use ofa set of indicators for the assessment of policies to support LPAs..

3Learning and Cooperation in Brazilian Local Productive Arrangements: some characteristics

3.1 - Data and Indicators

The use of the concept of Local Productive and Innovative Arrangements requires thedevelopment of analytical tools with the aim to capture dimensions not found in statistics based ontraditional sectoral and territorial dimensions. It also points the importance of conducting case studies

based on a common methodological framework that captures the relevance of learning practices andcooperation to the generation and diffusion of new products and processes.

The analysis developed in this section attempts to fill up the gap from the lack of systematizedinformation about the structure, the internal processes and the innovative performance of LPAs inBrazilian economy. The methodology comprises empirical surveys based on direct collection of data through


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interviews with firms inserted in LPAs developed by academic researchers integrated to REDESIST.Information was collected from a questionnaire designed with the aim of understanding learning andinteraction processes, evaluating externalities of the local environment and assessing different aspects that

could affect firms performance. The questionnaire was designed in such a way to make it compatible withthe Brazilian Innovation Survey (PINTEC) carried by IBGE (the Brazilian Institute of Geography andStatistics), incorporating indicators to evaluate learning and cooperation processes in LPAs. Specific

questions were formulated to evaluate the origin of the information used for learning, internal or externalto the firm. Other questions verify the intensity of interactions and the strength of the relationships withother agents in LPAs.

To identify the influence of technological efforts, learning processes, cooperation, localexternalities and networking to the innovative performance, the analysis recurs to the use of 30 indicators,divided into four distinct groups: 1) indicators of innovative efforts; 2) indicators of external learning andcooperative actions; 3) indicators of the density of externalities to local production; 4) indicators ofinnovative performance. These indicators were estimated based on questionnaires applied to the firms,transforming qualitative attributes, such as the importance attributed by the company to a particular event,in quantitative ones, establishing a value between 0 and 1 to express the opinion of the company abouteach event. These indicators were individually calculated for each one of the 1.187 firms in the sample.

Table Ain the annex summarizes the indicators used in the analysis and the events captured by each oneof them.

The analysis comprises a self-evaluation of the surveyed firms about the main factors thatinfluenced their innovative efforts, learning, cooperation and performance. Although recognizing thatsuch procedures may distort results, once interviewee not always has the proper understanding about whatis questioned, they are fully recognized as adequate to evaluate innovative dynamics at the firm level,

being accepted as an important tool by OECDs Oslo Manual (2005) that establishes the methodologicalprinciples that have guided national Innovation Surveys in several countries. Furthermore, the possibilityof obtaining empirical data from different sources based on common methodologies and concepts tends tominimize problems related to the diversity of the interpretation of questions among the agents.

Based on the set of indicators, we sought to identify general patterns using statistical procedures

related to Multivariate Analysis1. Through the implementation of four Factor Analyses, one for eachgroup of indicators, we try to systematize and reduce the number of aspects related to each dimension,starting from 30 indicators extracted from data collected by questionnaires. A second step appliestechniques of Cluster Analysis, based on the synthetic factors identified and on the scores attributed tothem. This procedure might be justified because of the heterogeneity of the sample. The common patternsidentified operate as an analytic tool to format this diversity, elucidating aspects of the innovativedynamics in LPAs. The sample from which the analysis was developed has some specificities. On the onehand, the companies are located in territorial agglomerations of different areas of economic activity,which affects the intensity of the learning process and the orientation of technological efforts. On theother hand, the sample consists mainly of micro and small enterprises. Table 1shows the stratification ofthe sample according to the economic activity performed and the size of the firms. There is a strong

predominance of micro enterprises, which together represent 59.2% of the sample; small enterprises reach32% of the total and medium enterprises 8%. We also find a greater concentration on activities related totraditional industries (furniture, clothing, footwear, plastics and fishing), which together represent 68% ofthe sample. The most capital-intensive and knowledge-intensive activities (mechanical equipment andcomponents, oil and gas, software, computer equipment and telecommunications and biotechnology)represent a smaller part of the firms in the sample (32%).

1For a mathematical and statistical formalization of Multivariate Analysis, see Hair et al (2005), Johnson and Wichern (1998).


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Table 1Stratification of the sample according to the activity performed by the companies and

their size (N=1.187)

Size Micro Small Medium Total

Activity

Location of LPAs

(Municipality and

federative state) N % N %. N %. N %Metal Mechanic,

Equipment andComponents, Oil and Gas

Joinville - SC; Camaari

BA; Ribeiro PretoSP; Maca - RJ

54 7,68% 69 17,92% 32 32,32% 155 13,06%

Furniture and WoodUb - MG; Linhares - ES;Vitria - ES; ChapecSC; Unio da Vitria - SC

164 23,33% 91 23,64% 20 20,20% 275 23,17%

Textile, Clothing andFootwear

Colatina - ES; Apucarana- PR; Terra Roxa - PR;Petrpolis - RJ; Cabo Frio- RJ; Ibitinga - SP;Campina Grande - PB;Jaragu - GO; NatalRN; Tobias BarretoSE;BiriguiSP

284 40,40% 140 36,36% 23 23,23% 447 37,66%

Hardware, software andtelecommunication

Petrpolis - RJ; Ilhus -BA; Curitiba - PR; RecifePE; BrasliaDF; Santa

Rita do Sapuca-MG

136 19,35% 51 13,25% 14 14,14% 201 16,93%

Biotechnology Belo Horizonte - MG 15 2,13% 4 1,04% 0 0,00% 19 1,60%

Plastics Cricima - SC 12 1,71% 14 3,64% 8 8,08% 34 2,86%

Fishery Itaja - SC 38 5,41% 16 4,16% 2 2,02% 56 4,72%

Total 703 59,22% 385 32,43% 99 8,34% 1187 100%

Source: Micro data collected at the firm level. Information extracted from REDESIST Database on Local Productive Arrangements (LPAs).

The descriptive statistics presented in Table 2 refer to the average value of the indicators in theentire sample of 1.187 companies. Innovative efforts seem to be mainly associated with informationobtained internally, since the indicators related to internal learning show high values - 0.59 for internallearning derived from different organizational sources2 (LEARN ORGFONT) and 0.28 for the use ofR&D department (LEARNR&D) as a source of information for innovation. Despite the high relative

importance of the R&D department, these activities are conducted in a small scale by the firms in thesample, since the indicator relating to the constancy of R&D activities (CONSTR&D) presents a reducedvalue (0.21). The firms develop similar efforts related to pre-innovative activities(CONSTPREINOV)

and to organizational upgrade (CONSTORGUPDATE), since these indicators show similar values-0.26 and 0.28 respectively. Efforts related to acquisition of incorporated technologies are significant,since this indicator (CONSTINCTECH) assumes relatively high value (0.33). For all the companies, thetraining effort of the workforce (TRAINEEWORK) is also significant, with an indicator of 0.34, but theeffort to absorb skilled workers (ABSSKILLWOR) is more limited (0.15). These data indicate that thefirms give more importance to the collection, systematization and dissemination of information obtainedfrom various departments of the company. In parallel, there is a reasonable effort oriented to atechnological upgrade, mainly based on the purchase of machinery and equipment. In contrast,

conducting pre-innovative activities and organizational update tend to be less intense. Additionally, theimportance attributed to actions related to the training of the workforce is higher than that attributed to thehire of more qualified workers.

Indicators of external learning and cooperative actions show that the main form of interactionobserved in the sample refers to vertical learning. From the value obtained by the indicator of externalvertical learning (LEARNVERT = 0.63), it appears that the information obtained from customers and

suppliers are very important to innovative firms. There is also a relatively high value of the indicatorrelated to horizontal learning based on information derived from competitors in the industry

(LEARNHOR = 0.41). It should be noted that the indicators that capture the importance attached toinformation obtained from S&T institutions (LEARNS&T) and specialized services(LEARNTECHSERV)) have the lowest values among the indicators related to external learning (0.13 and0.21, respectively), demonstrating the limited priority attributed to these sources of information.

2Production, marketing and sales, customers services


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Regarding cooperation, the data reinforce the perception that it occurs in a limited scalefor the

sample as a whole, since all indicators relatedto this dimension assume valuesrelatively reduced. Twoforms of cooperation might be highlighted: vertical cooperation (COOPVER) related to cooperation withsuppliers and clients, with an average value of 0.21; and horizontal cooperation with competitors andother agents in the industry (COOPHOR), with an average value of 0.16. Indicators of cooperation withother agents (COOPOTHERS), with S&T institutions (COOPS&T) and with specialized services

(COOPTECHSERV) showed lower values (0.09, 0.07 and 0.06, respectively).Considering these indicators as whole, general evidences about the pattern of external learning and

cooperative actions might be captured from the sample. Among the different forms of interaction, thefirms attach considerable importance to vertical relationships, giving priority to the exchange ofinformation with customers and suppliers and, to a lesser extent, to horizontal interactions withcompetitors and others agents in the industry. However, the interactions developed with S&T institutionsand specialized services tend to belimited, suggesting a great difficulty to capture information and toaccess complementary competences from these sources.

Table2Descriptive statistics of the indicators d (N = 1.187).

Indicators Mean Min MaxStandard

Deviation

Indicatorsof

innovative

efforts

Constancy in conducting R&D (CONSTR&D) 0,2193 0 1 0,3085Constancy in the acquisition of incorporated technologies (CONSTINCTECH) 0,3385 0 1 0,3392

Constancy in pre-innovative efforts (CONSTPREINOV) 0,2677 0 1 0,3166

Constancy in organizational update (CONSTORGUPDATE) 0,2888 0 1 0,3446

Training of the workforce (TRAINEEWORK) 0,3437 0 1 0,2766

Absorption of skilled workers (ABSSKILLWORK) 0,1538 0 1 0,2360

Internal learning from R&D department (LEARNR&D) 0,2867 0 1 0,4211

Internal Learning from other sources (LEARNORGFONT) 0,5943 0 1 0,3553

Indicatorsof

e

xternallearning

andcooperation

Vertical Learning (LEARNVERT) 0,6331 0 1 0,3434

Horizontal Learning (LEARNHOR) 0,4136 0 1 0,3528

Learning through S&T institutions (LEARNS&T) 0,1359 0 1 0,2692

Learning through technical services (LEARNTECHSERV) 0,2133 0 1 0,2591

Learning through other agents (LEARNOTHERS) 0,2159 0 1 0,3392

Vertical cooperation (COOPVER) 0,1625 0 1 0,2844

Horizontal cooperation (COOPHOR) 0,0611 0 1 0,1876

Cooperation with S&T institutions (COOPS&T) 0,0714 0 1 0,1748

Cooperation with technical services (COOPTECHSERV) 0,0909 0 1 0,1822

Indicatorsof

local

externalities

Local Subcontracting Networks (EXTERSUBCONTNET) 0,2700 0 1 0,3223

Externalities of Skilled Workers (EXTERSKILLWORK) 0,6076 0 1 0,2894

Externalities of Inputs to Production (EXTERINPUTS) 0,5665 0 1 0,3191

Externalities of Equipments to Processes (EXTERNEQUIP) 0,4262 0 1 0,3354

Sales Externalities (EXTERSALES) 0,5594 0 1 0,3658

Externalities related to Technical Services (EXTERNTECHSERV) 0,6281 0 1 0,2678

Local S&T Externalities (EXTERNS&T) 0,2938 0 1 0,3518

Indicators

of

innovative

performan

Radical innovation in products (INRDPRD) 0,1449 0 1 0,2854

Radical innovation in processes (INRDPRC) 0,1601 0 1 0,3668

Incremental innovation in products (ININCPRD) 0,5687 0 1 0,4093

Incremental innovation in (ININCPRC) 0,5366 0 1 0,4989

Organizational innovationsType 1 (INORG1) 0,3095 0 1 0,3470

Organizational innovationsType 2 (INORG2) 0,3787 0 1 0,4351


Indicators of local externalities show that the firms attach considerable importance to externalitiesof skilled workers (EXTERSKILLWORK = 0.60), as well as to the presence of local technical services(EXTERNTECHSERV = 0.62).Local externalities related to the supply of raw materials, parts andcomponents (EXTERINPUTS = 0.56) are more relevant than those related to the supply of machinery andequipment (EXTERNEQUIP = 0.42). Local externalities can also be associated with the increase of thesales, since the average of this indicator is also significant (EXTERSALES= 0.55). On the other hand,local infrastructure related to science and technology is considered deficient by the average of the firms inthe sample (EXTERNS&T = 0.29). The firms of the sample (on average) have a limited insertion insubcontracting networks that operate at the local level, as indicated by the lowest value attributed to theindex related to this event (EXTERSUBCONTNET = 0.27).

Indicators related to innovative performance reflect the ability of the companies to introducedifferent types of innovations. With regard to innovations in products and processes, it is clear that mostcompanies in the sample has a high capacity of imitation, with the indicators related to incremental


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innovations in products (ININCPRD) and incremental innovations in processes (ININCPRC) reaching thehighest values (0.56 and 0.53, respectively). There is also a reasonable ability to introduce organizationalinnovations, which tends to be higher for the introduction of organizational innovations related tomarketing practices (INORG2 = 0.37), vis--vis the introduction of changes in organizational structureand the adoption of new management practices (INORG1 = 0.30).

However, indicators assume low values when they attempt to measure capabilities oriented to the

introduction of more "radical innovations. The indicator related to the introduction of new products fordomestic or international markets has a low value (INRDPRD = 0.14), reflecting the low ability of firmsto innovate in this field. The introduction of process innovations that are completely new to the sector hasalso low values (INRDPRC = 0.16). The analysis ofTable 3 indicates a general emphasis in imitativeinnovations for the firms in the sample. For both types of innovation analyzed, most companies in thesample are not innovative - 39.17% for product innovations and 55.52% for process innovation. Whencompanies innovate, the introduction of new products for the companies, but already existent on themarket, tends to be predominant (37.8%). The same occurs in terms of process innovations that are newfor the companies, but already existent in the sector (28.4%).

Table 3 - Distribution of the firms in the sample according to company size and type of innovation(N=1.187)

Size / Type of innovationMicro Small Medium Total

N % N % N % N %

Product Innovation

Non innovative 314 44,67% 130 33,77% 21 21,21% 465 39,17%

New to the firm 265 37,70% 151 39,22% 33 33,33% 449 37,83%

New to the market 124 17,64% 104 27,01% 45 45,45% 273 23,00%

Process Innovation

Non innovative 443 63,02% 179 46,49% 37 37,37% 659 55,52%

New to the firm 172 24,47% 139 36,10% 27 27,27% 338 28,48%

New to the sector 88 12,52% 67 17,40% 35 35,35% 190 16,01%


The broader picture provided by the analysis of all indicators indicate that the firms in the sample

concentrate their technological efforts in the systematization of internal learning. The most relevantinteractive actions occur with other productive agents, especially with customers and suppliers and, to alesser extent, with competitors and other companies. These companies have a huge capacity to imitate

products and processes and to introduce innovations in their organizational structures, but a reducedcapacity to implement intensive innovations - new products for the market or new processes to theindustry. The combination of efforts and interactions based on this pattern reinforces the imitativecharacter assumed by the innovative performance. However, to develop dynamic capabilities required toimplement intensive innovations, efforts to acquire new technologies, to develop pre-innovative activities,to absorb qualified personnel and to interact on a larger scale with S&T infrastructure might be necessary.

Another aspect concerns the heterogeneity observed not only at the sectoral level, but also at thefirm level of the sample. This trend reflects the fact that a large number of the indicators have a standard

deviation expressively higher or very close to its mean, indicating that the behaviors of the firms tend tobe significantly distinct in relation to the dimensions captured by those indicators. This feature suggeststhe existence of distinct patterns concerning the links between innovative efforts, learning mechanisms,cooperation practices and innovative performance. To capture this diversity, the application ofMultivariate Analysis seems particularly useful.


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3.2. Learning and cooperation in LPAs: empirical evidences

The analysis developed in the previous section identified the general characteristics of the samplein relation to the indicators used, which could be capture by its means. We have also verified the highheterogeneity of the sample, with the standard deviation being higher than the average value of theindicators, indicating that companies differ significantly in relation to the dimensions analyzed. Due to

these characteristics, statistical techniques are applied to identify general patterns between companies andthe processes. First, a factor analysis is applied, seeking to systematize and reduce the dimensions to beinvestigated. Subsequently, a cluster analysis tries to clarify general patterns in the sample, resulting inthe identification of groups of firms with similar behavior in terms of the dimensions analyzed.

Starting from the calculated indicators, we sought to develop a factor analysis based on theprincipal component method, using the criteria of normalized varimax3for each subset of indicators. Themain purpose of factor analysis is to capture and describe the covariance relationships among manyvariables in terms of a few factors not directly observable. Therefore, factor analysis could identify themain factors and the weight of the variables for each factor, characterizing the behavior of the sample inrelation to these factors. In this sense, Table 4 shows the eigenvalues related to each factor and the

percentage of data variation explained by each subset of indicators.

We opted to apply four factor analyzes separately, one for each subset of indicators. Concerning"innovative efforts" four (4) factors were selected, which together explain 78.5% of the variance of thedata. Concerning "external learning and cooperative actions, four (4) factors were selected whichrepresent 75.8% of the data variation. Regarding indicators of local externalities three (3) factors wereselected, which together express 74.5% of the variance of the data. With respect to "innovative

performance", three (3) factors were selected which together explain 74% of the variations of the data.The definition of the number of factors for each analysis considers the total explained variance of the data,which was located in the range between, 74% and 78%.

Table 4 - Eigen values and variance related to selected factors (N = 1.187)

Subsets of Indicators Factor Eigenvalue % of totalvariance

explained

EigenvalueAccumulated

% of cumulativevariance

explained

Innovative efforts

Factor 1 3,8005 47,5064 3,8005 47,5064

Factor 2 0,9445 11,8063 4,7450 59,3127

Factor 3 0,7910 9,8869 5,5360 69,1996

Factor 4 0,7445 9,3067 6,2805 78,5063

External learning and

cooperative actions

Factor 1 3,7185 41,3162 3,7185 41,3162

Factor 2 1,2737 14,1520 4,9921 55,4682

Factor 3 1,1008 12,2313 6,0930 67,6995

Factor 4 0,7314 8,1265 6,8243 75,8261

Local Externalities

Factor 1 2,4466 40,7759 2,4466 40,7759

Factor 2 1,0800 17,9997 3,5265 58,7756

Factor 3 0,9442 15,7368 4,4707 74,5124

Innovative Performanceo

Factor 1 2,6121 43,5351 2,6121 43,5351

Factor 2 0,9842 16,4039 3,5963 59,9391

Factor 3 0,8456 14,0930 4,4419 74,0321

Source: Micro data collected at the firm level. Information extracted from REDESIST Database on Local Productive Systems

Table 5summarizes the information generated by the analysis of the factor loadings matrix. Thetable presents the value of the factor load associated with the most relevant indicators for each factor.Concerning the indicators of innovative effort, the Factor 1 may be called "innovative activities",comprising indicators related to the constancy in the acquisition of incorporated technologies(CONSTINCTECH), constancy in organizational update (CONSTORGUPDATE) and constancy in

pre-innovative efforts (CONSTPREINOV). The Factor 2 may be referred to the "R&D Factorcomprising the internal learning from R&D department (LEARNINTR&D) and the constancy inconducting R&D(CONSTR&D). The Factor 3 is influenced only by the high intensity of one indicator

3This method is more usual and more accurate since it promotes the rotation of the orthogonal axis related to the factors andvariables (indicators) in order to reach the best result concerning the frame of the indicators in the respective factors.


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the "internal learning from other sources" (LEARNORGFONT) and can be entitled "Internal LearningFactor. Finally, the Factor 4 groups the subset of indicators related to the absorption of skilled workers(ABSSKILLWORK) and to the training of the workforce (TRAINEEWORK), so this factor is definedas "Development and Training of the Workforce Factor".

For the subset of indicators of external learning and cooperative actions, four factors were selected.The first factor is influenced by larger scale indicators that represent cooperation vertical cooperation

(COOPVER), horizontal cooperation (COOPHOR), cooperation with technical services(COOPTECHSERV) and cooperation with other agents (COOPOTHERS) and can be called"Cooperation Factor". The second factor comprises vertical learning (LEARNVER) and horizontallearning(LEARNHOR), so this factor may be titled Learning with Productive Agents. The third factormay be titled "Interaction with S&T institutions, including indicators related to learning with S&Tinstitutions (LEARNS&T) and cooperation with S&T institutions (COOPS&T). The fourth factor isstrongly influenced by the indicator of learning through technical services(LEARNTECHSERV) andcan be called Learning through t specialized services.

Concerning the local externalities, the indicators more directly related to the sphere of theproduction - namely, the externalities of inputs to production (EXTERINPUTS), the externalities ofequipment (EXTERNEQUIP) and externalities related to technical services(EXTERNTECHSERV) -

are grouped in the first factor, which can be named as "Production Externalities. The second factorgroups the indicators related to two dimensions externalities of skilled workers(EXTERSKILLWORK) and S&T Externalities (EXTERNS&T) - and can be called Workforce andS&T Externalities ". The third factor is influenced only by one indicator sales externalities(EXTERSALE) - and can be called "Sales Externalities.

Table 5 - Summary of extracted factors for the subsets of indicators (N = 1187)Innovative efforts

Factor 1 - Innovative activities

Constancy in the acquisition of incorporated technologies(CONSTINCTECH) 0,80

Constancy in organizational update(CONSTORGUPDATE)0,76

Constancy in pre-innovative efforts(CONSTPREINOV)0,71

Factor 2 - R&D

Internal learning from R&D department (LEARNR&D)

0,86

Constancy in conducting R&D (CONSTR&D)0,80

Factor 3 - Internal Learning

Internal Learning from other sources (LEARNORGFONT)0,93

Factor 4Absorption and Training of the Workforce

Absorption of skilled workers (ABSSKILLWORK)0,81

Training of the workforce (TRAINEEWORK)0,77

External learning and cooperation

Factor1 - Cooperation

Horizontal Learning (LEARNHOR)0,78 Cooperation with other agents (COOPOTHERS)

0,78

Vertical cooperation (COOPVER)0,70 Cooperation with technical services

(COOPTECHSERV)0,70

Factor 2 - Learning with productive agents

Horizontal Learning (LEARNHOR)0,84 Vertical Learning (LEARNVERT)0,79

Factor 3 - Interaction with S&T

Learning through S&T institutions (LEARNS&T)

0,85

Cooperation with S&T institutions (COOPS&T)

0,82

Factor 4 - Learning through technical services

Learning through technical services

(LEARNTECHSERV)0,81

Local Externalities

Factor 1 - Production externalities

Externalities of Inputs to Production

(EXTERINPUTS) 0,88

Externalities of Inputs to Production(EXTERINPUTS) 0,84

Externalities related to TechnicalServices (EXTERNTECHSERV)0,63

Factor 2 - Workforce and S&T Externalities

Local S&T Externalities(EXTERNS&T)0,88

Externalities of Skilled

Workers(EXTERSKILLWORK)0,58

Factor 3 - Sales Externalities

Sales Externalities(EXTERSALES)0,93

Innovative Performance

Factor 1 - Organizational innovations

Organizational innovationsType 2 (INORG2)0,84

Organizational innovationsType 1 (INORG1)0,83

Factor 2 - Radical Innovations

Radical innovation in processes(INRDPRC)0,83

Radical innovation in products(INRDPRD)0,81

Factor 3 - Incremental Innovations

Incremental innovation inproducts (ININCPRD)0,92

Incremental innovation inprocess(ININCPRC)0,61

Source: Micro data collected at the firm level. Information extracted from REDESIST Database on Local Productive Arrangements (LPAs)

Regarding the subset of indicators related to innovative performance, Factor 1 represents the

two indicators related to the implementation of organizational innovations comprising new production


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methods (INORG1) and new concepts and practices of marketing (INORG2) - being called as"Organizational Innovations. The indicators related to radical innovation in products (INRDPRD) andradical innovation in processes (INRDPRC), are grouped in Factor 2, which could be called "Radical

Innovations". The Factor 3 groups the indicators of incremental innovations in products (ININCPRD) andprocesses (ININCPRC) and may be entitled as "Incremental Innovations".

Regarding the factor loadings, with the exception of the indicators proposed for each factor, the

others have less influence on their behavior. A similar trend is observed for indicators that have aninverse relationship with the factors, which is generally very low (less than -0.05), slightly influencing thefinal value of the factor. Considering the factor scores4related to the sampled companies, a comparativeanalysis was developed based on a reduction of the number of variables, which allows the formation ofclusters grouping the companies with similar characteristics in terms of the factors identified. It isexpected that theses clusters reflect similar patterns with respect to innovative efforts, external learning,cooperative actions, local externalities and innovative performance.

The cluster analysis was applied to identify distinct groups of companies with similarcharacteristics in terms of the factors identified. An additional indicator related to the participation inlocal subcontracting networks was also included in the analysis 5. Cluster analysis evaluates a set ofinterdependent relationships between the data, without any distinction between dependent and

independent variables. It enables the classification of the objects in this case, the firms in the sample - inrelatively homogeneous groups, based on the set of variables or on the set of factors generated byMultivariate Analysis. These procedures allow, at first, to reduce the dimensions of the analysis fromthirty (30) original indicators to fourteen (14) underlying factors. Secondly, based on the application ofthese factors for the 1.187 companies in the sample and on the subsequent use of cluster analysistechniques, four (4) groups of companies with similar characteristics were identified. The next step triesto identify the specific characteristics of these groups in terms of the dimensions analyzed, stressing theinfluence of these factors to the performance of the innovative companies with a higher degree ofaccuracy.

3.3- Similar Firms inserted in LPAs: the characteristics of the clusters

The analysis developed identified four groups (clusters) of firms with similar characteristics fromthe factors previously generated. Figure 3 shows the values of the factors identified for each cluster. Forthe entire sample, the mean of a given factor is always zero (0) and its standard deviation is equal to one(1) 6 . Table 6 summarizes the specific features identified for the four distinct clusters. The maincharacteristics of each cluster are subsequently discussed.

4 The score comes from the factorial coefficients related to each indicator. The factor coefficients are multiplied by eachindicator of the companies, obtaining a final value equivalent to the individual factor score of each company.5For operational reasons, the indicator related to the insertion in local subcontracting networks was not included in the factor

analysis developed for the group of indicators related to local externalities. It was added in the second stage of analysis anddoes not refer to any factor. To ensure uniformity of the analysis, this indicator was standardized for the sample companies,6In the Factor Analysis the value obtained by a given factor - associated to a group of companies with similar characteristics -can only be analyzed in comparison with the entire sample mean or with other groups with distinct characteristics.


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Figure 3Mean of the factors for the firms inserted in the clusters identified


Table 6Characteristics of the clusters

Characteristics

/ Clusters

Cluster 1 (386 firms)

Medium / Low

Innovation-Intensive


Low Innovation-

Intensive


Medium / High

Innovation-Intensive

Cluster 4 (183 firms s)

High Innovation-

Intensive

Innovative EffortsMedium intensity and related

to internal learningOccurs with very limited

intensity

Occurs with high intensitybeing strongly associated withinformal innovation activities

Occurs with high intensity beingassociated with R&D and withthe training and absorption of

skilled workers

External Learning

and Cooperation

Related solely to productiveagents

Almost inexistent

Development of cooperativeactions. Interaction with

specialized services and to alesser extent with other

productive agents

Interaction with S & Tinstitutions and to a lesser extent

with specialized services.Cooperation and interaction

with productive agents

Local Externalitiesand networking

Externalities in the orbit of

production activities andpresence of relativelystructured networks

Relevant only to improve the

companies sales. Limited inthe sphere of production.Lack of structured networks

Externalities have medium

relevance. S tructuring oftechnical-productive networksat the local level.

Externalities and insertion inlocal networks have low /

medium importance, except

those related to labor trainingand to the presence of

universities and researchinstitutions

Innovative

Performance

Medium and based on theintroduction of innovativeproducts and processes

Low for all types ofinnovations

Medium-high. Predominanceof organizational innovations

and, to a lesser extent,incremental innovations

High, being associated with theintroduction o f radical

innovations and, to a lesserextent, organizational andincremental innovations


The first group of companies identified (cluster 1) comprises 386 companies, from which 68.6%%are micro enterprises, 29% are small enterprises and 2.3% are medium enterprises 7. Concerning theactivities developed by these firms, there is a predominance of the textile and garment companies, with

46.37% of the firms in the cluster, and furniture and wood, with 34.71%. The remaining 19% would bedivided between others activities. These companies had an average revenue around R$ 900.000 in theyear 2003and had employed on average 25 employees. They present a low intensity concerning thedevelopment of innovative efforts. These efforts comprise mainly internal learning, which occurs moreintensively than in the other clusters, and, to a lesser extent, R&D activities, which equals the intensity ofthe sample mean. Concerning external learning and cooperative actions, these companies are intensivelyinvolved with productive agents, but other forms of interaction are very restricted. Externalities directlyrelated to the orbit of production are very important, as well as other local sources of externalities, butthere is a low integration in technical-productive networks, reflecting a more passive attitude towardsthose aspects. The innovative performance of these companies is strongly oriented to the introduction ofincremental innovations in products and processes, presenting a higher score attributed to these aspects

7Comprising the size of the firms, four ranges was considered: 1) Micro enterprises (up to 19 employees), 2) Small enterprises(between 20-99 employees) 3) Medium-sized enterprises (between 100-499 employees); 4) Large enterprises (more than 500employees).


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when compared with other clusters. On the other hand, the introduction of organizational innovations andof radical innovation occurs on a very small scale. Considering these characteristics, this cluster might becharacterized as having a "medium- low innovative dynamism.

The second cluster (cluster 2) comprises 352 companies from which 256 (72%) consist of smallfirms, 84 (23%) of small firms and 12 (3%) of medium-sized enterprises. These firms employed onaverage 23 employees in 2003, with revenues of around R$ 1.2 million. Regarding the activities

performed, 44% of the firms work in the textile, clothing and footwear industry, 19% in furniture andwood, 14% in fishing, 10% in software and computer and the remaining 14% distributed among a widespectrum of activities. Compared to the sample, these companies are not very involved with innovationefforts, and even the information obtained from the various departments of the firms (characterizinginternal learning) are restricted used, which would be reflected in the scores attributed to this factor, thelowest value compared with other clusters. The limited strategies related to external learning andcooperative actions illustrate this aspect. Concerning factors related to local externalities and to theinsertion in subcontracting networks, the values assumed by the factors are somewhat higher.Externalities directly related to the production have a value close to the sample mean and the externalitiesrelated to local sales are high. This last feature shows that companies of this group concentrate its sales inregions where they are located. On the other hand, participation in subcontracting networks is very low,

reflecting the fact that local sales do not involve inter-industry trade. Regarding the innovativeperformance, these companies tend to generate different types of innovations in a very small scale. Due tothese characteristics - namely, the lower values in most of the factors related to innovative performancewhen compared with other clusters and with the mean of the sample - the firms in cluster 2 can becharacterized as presenting "low innovative dynamism.

The third cluster (cluster 3) includes266 companies, from which 36% are micro enterprises, 46%small enterprises and 16% medium-sized enterprises. These companies operate in activities related tometal mechanics, equipment and components (35%), textiles and clothing (33%) and furniture and wood(16%). Their sales revenue reached R$ 2.4 million on average in 2003 and they had employed 58employees on average per company. Compared with other clusters, these companies carry intensiveinnovative activities and, despite having R&D on a limited scale, they try very hard to exploit the

potential of internal learning. They also develop actions related to the absorption and training of skilledworkers at a high scale. The cooperative actions and learning related to specialized services are also verysignificant in this cluster, as well as learning with productive agents. However, interactive actions withS&T institutions are virtually nonexistent for this group of companies. Concerning the set of localexternalities analyzed in this study, companies in this cluster attach similar importance when comparedwith the sample mean. However, these companies are the ones in the sample that participate moreintensively in subcontracting networks. They have high capacity to innovate and introduce organizationalinnovations incrementally in products and processes on a scale considerably high. The introduction ofmore radical innovations, however, is still restricted. The performing of innovative efforts, theinvolvement with learning and cooperation and the integration to more complex networks are factors thatgenerate capabilities to innovate for these firms. However, the R&D activities are still performed on asmaller scale, as well as the interaction with S&T institutions, limiting the possibilities of implementingmore virtuous innovation processes. Due to these characteristics, the firms in this cluster can becharacterized as presenting "medium-high technological dynamism.

The fourth cluster (cluster 4) involves 183 companies from which 46% are micro enterprises, 34%are small enterprises and 18% are medium enterprises. In 2003, those firms earned on average R$ 3.1million with 59 employees each. These companies are concentrated in computing and software-relatedactivities (33%), metal mechanics, equipment and components (33%), textiles and clothing (8.5%),

biotechnology (7.5%), furniture (6%) and other activities (10 %). Their innovative efforts areconcentrated mainly in conducting R&D and in training skilled workers. Innovative activities occur inmedium-high scale for these firms. They also establish deeper interactions with S&T institutions, when

compared with other clusters extracted from the sample. Factors related to learning processes and externalcooperation also have relatively high values, significantly above the average of the sample. The moreimportant local externalities identified by this group are related to availability of skilled workers and of


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S&T infrastructure. Concerning the innovative performance, these companies are those that introducemore intensive innovations in products and processes. They also innovate in their organizationalstructures at medium-high intensity. Therefore, innovative efforts are higher, with an emphasis on highinteraction with S&T institutions and intensive R&D activities, leading them to develop strongcapabilities to implement intensive innovations, both in products and processes. Due to thesecharacteristics, the pattern of firms in this cluster reflects a situation of "high technological dynamism.

4- Concluding Remarks

The analysis indicates that concepts originally developed by the academic sphere can contributesignificantly to the improvement of regional innovation policies. Specifically, it deals with thetransference of concepts and competences from the academy to the policy sphere, identifying how thoseconcepts have been incorporated in the repertory of skills and practices of the policy makers. The analysis

points out some relevant trends that guide this transference. A relevant aspect refers to the provision of aconceptual and methodological basis for these policies. This conceptual base can be important to enablethe implementation of actions in contexts and realities very heterogeneous in terms of their productive,social and territorial environment, as in the case of policies to support LPAs. In this sense, the

mobilization of LPAs of different sizes and types seems to be a way to stimulate a better regionaldistribution of economic activities, as well as to mitigate social and territorial inequalities.

A second important aspect highlighted by the analysis refers to the academic contribution to thetraining of a qualified staff responsible for the implementation of innovation policies in different fields.Situations in which personnel originated from academia assume an important executive role in theimplementation of these policies were identified in the case of policies to support LPAs, when researchersoriginated by the academy assume an executive role in public organizations such as BNDES and theMinistry of National Integration. Another important contribution refers to the role of communities ofexperts originated from the academic sphere that come to play a systematic advisory role to theformulation of innovation policies. Concerning policies to LPAs, we can mention the role of the BrazilianConference of Local Productive Arrangements held annually. In these Conferences, academic agents took

part with members of public bodies in various forums, thematic groups and advisory boards, much ofthem articulated to the regional nuclei of the GTP - APL. A further contribution refers specifically to thetraining of policy-makers, public managers and other staff responsible for operate concrete actions basedon that framework.

A third aspect that seems to be very important concerns the establishment of channels ofcommunication between the academic sphere and policy-makers, allowing an exchange of informationand experiences in order to promote policy adjustments, increasing or reducing the scope of actionsaccording to the results generated and to the responses of the target audience of the policies. Concerningthe support to LPAs, this adjustment might be articulate to the ability to evolve along different"generations" of policies, through an adjustment of the focus according to the feed-back obtained fromthe practical experience gained in policies operation. This adjustment reflects a process of "institutionalinteractive learning" that mobilizes and articulates the academic sphere and policy-makers instances,which can improve the effectiveness of the innovation policies. These processes seem to have a directimpact on the consolidation of a more analytically grounded policy, better adapted to the reality on whichit intends to intervene and with a greater capacity to meet specific needs of the agents affected, becomingmore lasting and sustainable.

The analysis developed also tries to characterize the behavior of firms inserted in LPAs,identifying the importance of interaction with other agents and how they are affected by externalitiesassociated with the local environment. Specifically, we sought to identify the dimensions that influencethe introduction of innovations by firms included in different LPAs. Four clusters of firms with similar

patterns were identified. In this sense, the innovative dynamics of a LPA would be influenced by the

relative participation of companies in each one of those clusters. This work is part of a broader researchprogram that seeks to identify and analyze indicators related to "innovative dynamics" of local productivesystems. To go beyond on the research agenda some additional steps are needed, such as a more detailed


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analysis of the influence of different territorial specificities in the dynamics of innovative processes. Italso seems very important to advance in a better understanding of the learning processes and cooperative

practices at the level of structured industrial agglomerations, particularly through more detailed empiricalanalysis. Another natural extension of the analysis consists in the use of econometric techniques toevaluate possible relationships between the factors identified.

Annex - Table ADescription of the Indicators.Indicators Events Indicators Events1) Indicators of innovative efforts 2) Indicators of external learning and cooperation

Constancy in conducting R&D(CONSTR&D)

Performing R&D within the firm; external acquisition ofR&D.

Vertical Lea rning (LEARNVERT)Information absorbed from inputs suppliers (equipment, rawmaterials); and customers.

Constancy in the acquisition ofincorporated technologies(CONSTINCTECH)

Acquisition of machinery and equipment that implysignificant technological improvement; acquisition of othertechnologies (incorporated in software, licenses, patents,trademarks and trade secrets).

Horizontal Learning (LEARNHOR)Information absorbed from competitors and other firms inthe sector.

Constancy in pre-innovative efforts(CONSTPREINOV)

Industrial project related to products / processes eithertechnologically new or significantly improved; and training

program associated to the introduction of products /processes either technologically new or significantlyimproved.

Learning through S&T institutions(LEARNS&T)

Information absorbed from Universities; and ResearchInstitutions.

Constancy in organizational update(CONSTORGUPDATE)

Programs oriented to quality management or organizationalmodernization; new forms of comm ercialization ordistribution of products either new o r significantlyimproved.

Learning through technical services(LEARNTECHSERV)

Information absorbed from Centers of Professional training,technical assistance and maintenance; Information absorbedfrom Laboratories of tests and certification; Informationabsorbed from Consulting Enterprises.

Training of the workforce

(TRAINEEWORK)

Training within the firm; training in local technical courses;training in technical courses outside the cluster; training at

either supplier or customer firms.

Learning through other agents

(LEARNOTHERS)

Licenses patents and know-how; Conferences, seminars,courses and technical publications; Fairs, Exhibitions and

Shops; leisure meetings; local business associations; andinformation based on Internet or digital networks.

Absorption of skilled workers(ABSSKILLWORK)

Hiring of technicians / engineers from other firms in thecluster; hiring of technicians / engineers from firms outsidethe cluster; absorption of graduates from universities.

Vertical cooperation (COOPVER)Cooperation with Inputs suppliers (equipment, materials,components and software); and Customers.

Internal learning from R&Ddepartment (LEARNR&D)

Department of R&D as a relevant source of information forinnovation.

Horizontal cooperation (COOPHOR)Cooperation with Competitors; and other firms of thesector.

Cooperation with S&T institutions(COOPS&T)

Cooperation with Universities; and Research institutes.

Internal Learning from other sources(LEARNORGFONT)

Production area; Sales and marketing area and customerservice.

Cooperation with technical services(COOPTECHSERV)

Cooperation with Centers of Professional training, technicaland maintenance assistance; Cooperation with laboratoriesof tests and certifications and with consulting enterprises.

Cooperation with other agents(COOPOTHERS)

Cooperation with representation; trade union entities;bodies of support and promotion; and financing agents.

3) Indicators of local productive externalities 4) Indicators of innovative performance

Externalities of Skilled Workers(EXTERSKILLWORK)

Availability of skilled labor, low local labor costs

Radicalinnovation in products(INRDPRD)

New product to the international market; new product to thenational market.

Externalities of Inputs to Production(EXTERINPUTS)

Proximity to suppliers of inputs and raw materials;Acquisition of raw materials; Acquisition of componen tsand parts.

Externalities of Equipments to

Processes (EXTERNEQUIP)

Proximity to equipment suppliers and acquisition of

equipment

Radical innovation in processes(INRDPRC) New process for the sector.

Sales Externalities (EXTERSALES) Proximity to customers / consumers; Strong sales ofproducts to local markets

Incremental innovation in products(ININCPRD)

New product for the firm, although existing in the market;Innovation in design of products; creation of substantialimprovement of packaging

Externalities related to TechnicalServices (EXTERNTECHSERV)

Physical infrastructure (energy, transport, communications),availability of technical expertise and services, purchases oflocal specialized services (maintenance, marketing, etc..).

Incremental innovation in (ININCPRC)New technological processes for the firm, although existingin the sector.

Local S&T Externalities(EXTERNS&T)

Proximity to universities and research centers.Organizational innovationsType 1(INORG1)

Advanced management techniques; changes in theorganizational structure; implementation of new methodsrelated to ISSO 9000/ 14000.

Local Subcontracting Networks(EXTERSUBCONTNET)

Multiple subcontracting links among local business:Organizational innovationsType 2(INORG2)

Changes in the concep ts and/or practices of marketing;changes in the concepts and/ or practices ofcommercialization.

5. References

ARCHIBUGI, D. and LUNDVALL, B.-(2001) eds.The Globalizing Learning Economy. Oxford:Oxford University Press

AUDRETSCH, D. B.(1995)Innovation and Industry Evolution(MIT Press, Cambridge).AUDRETSCH, D. B.; FELDMAM, M. P.(2004) Knowledge spillover and the geography of innovation.

In: HENDERSON, Vernon; THISSE, Jaques-Franois (Ed.).Handbook of urban and regionaleconomics. Amsterdam: Elsevier, v. 4, cap. 3.

BASANT, R. (2002) Knowledge Flows and Industrial Clusters, A background paper commissioned bythe International Development Research Centre (IDRC), 2002.

BELL, M. and ALBU, M. (1999) Knowledge Systems and Technological Dynamism in Industrial

Clusters in Developing Countries, World Development, Vol. 27, No. 9, pp. 1715-1734,CASSIOLATO, J. E.; LASTRES, H. M. M. (2008). Discussing innovation and development: converging

points between the Latin American school and the innovation Systems perspective? GLOBELICS

2008CASSIOLATO, J. E.; LASTRES, H. M. M. e MACIEL, M. L. (eds) (2003) Systems of Innovation andDevelopment. Cheltenham: Edward Elgar.


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CHRISTOPHERSON, S. MICHIE, J.; TYLER, P. (2010)Regional resilience: theoretical and empirical

perspectives, Cambridge Journal of Regions, Economy and Society,3, 310,CLARK, J., HUANG, H.I. and WALSH, J.P. (2010) Typology of Innovation Districts: What it Means

for Regional Resilience", Cambridge Journal of Regions, Economy and Society, 3.1: 121-137. 2010COOKE, P. and MORGAN, K. (1998)The associational economy: firms, regions, and innovation. Oxford

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COOKE, P. (2001) Regional innovation systems, clusters, and the knowledge economy, Industrial andCorporate Change, 10(4): 945-974, 2001COOKE, P.; URANGA, M. G.; ETXEBARRIA, G. (1998) Regional systems of innovation: an

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