Paper-Fabio e Jorge- globelics2014.pdf

8/11/2019 Paper-Fabio e Jorge- globelics2014.pdf

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Learning, Cooperation and Innovation in Industrial Agglomerations: evidences of the impacts ofterritorial proximity in the innovative performance of the Brazilian Industry

Abstract: The paper analyzes the influence of learning processes, cooperation and innovation for twogroups of Brazilian industrial firms: those companies included in industrial agglomerations and those that

are not included in them. Based on an evolutionary approach the paper tries to develop an exploratoryanalysis of the factors that influence patterns of learning, cooperative practices and innovativeperformance in industrial agglomerations. Initially, a methodology to identify industrial agglomerationsbased on secondary data is presented. Then, the impacts of cooperation and learning on the performanceof innovative firms included in those agglomerations are discussed, based on the construction of a set ofindicators extracted from Brazilian Industrial Surveys which are treated through econometric techniques.Specifically, an ordered probit model is developed to assess these impacts in terms of the introduction ofproduct innovations. From the econometric models analyzed, it appears that the companies included inindustrial agglomerations develop processes of learning and cooperation that are more virtuous, whencompared with the rest of Brazilian industry, enabling them to obtain higher innovative gains.

Key-words: Industrial agglomerations; Learning and Innovation, Cooperation and Innovation

Introduction

The concept of industrial agglomerations has been increasingly used as an analytical tool by theliterature of Industrial Economics. In this literature, an evolutionary approach has pointed the importanceof connecting the characteristics of the knowledge generation and the identification of critical dimensionsof those agglomerations. It is generally assumed that the discussion about how this knowledge isgenerated, appropriated, distributed and enhanced might contribute to understand how thoseagglomerations work, allowing not only to differentiate them according to a greater or lesser degree ofcomplexity but also to evaluate their potential to evolve along a virtuous path of competence growth.

In an evolutionary perspective, a major feature of those agglomerations refers precisely to theirability to operate as a mediator between the firm and the external environment, which increases thecapacity of absorbing knowledge potentially useful for the strengthening of efficiency, innovativeness andcompetitiveness. Basically, these agglomerations might redefine the dichotomy between "internal" and"external" sources of knowledge, acting as an intermediate instance which allows to "format" theknowledge according to the requirements of the competitive process, providing relevant externalities,stimulating the integration of competences and generating multiple spill-over effects. However, despitethe recognition of the learning process as a critical aspect of this dynamics - empirically illustrated by agrowing number of case studies there still a gap regarding cross-sector analyzes that enable the

identification and quantification of those gains at the firm-level, compared with firms not included inindustrial agglomerations.This article tried to expand the understanding of the relationship between territorial proximity,

cooperation and innovation, based on an analytical framework that seeks to articulate the intensity oflearning and innovative processes to elements that emerge from territorial specificities. Specifically, theanalysis tries to identify the influence of learning processes, cooperation and innovation to Brazilianindustrial firms, which were divided into two distinct groups: firms inserted in territorial agglomerationsand firms territorially isolated.

The article is structured in six sections. The next section presents the conceptual framework thatunderpins the study of the relationship between territorial proximity, learning and cooperation inindustrial agglomerations. The third section presents the methodological procedures adopted to identify

industrial agglomerations from secondary sources of information. The fourth section presents an overviewof the characteristics of the industrial agglomerations identified from those procedures. The fifth sectionpresents the methodology used to identify the impact of cooperation and learning to the innovative


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performance of firms inserted in industrial agglomerations, based on the construction and econometrictreatment of indicators extracted from Brazilian industrial surveys (PIA and PINTEC). The sixth sectionpresents the results of the econometric model (ordered probit) used to assess these impacts in terms of theintroduction of product innovations. The last section presents the main conclusions of the analysis.

2. Conceptual Framework

The concept of industrial agglomerations has been recurrently used as an analytical approach todiscuss aspects related to the territorial competitiveness by the modern literature of Industrial Economicsand Regional Economics. The basic assumption of those analyses is that industrial agglomerations mightprovide positive externalities at the territorial level, increasing productive efficiency and creating asuitable environment to the raise of innovativeness and competitiveness of the firms located in theterritory. Moreover, the interactions established between the firms inserted in those agglomerations mightalso have a significant impact on the dynamics of territorial development of the localities, contributing tothe attraction of other economic activities. The use of this analytical category to discuss structuralconditions that affect firms competitiveness goes back to classical theoretical approaches, starting fromthe works of Marshall (1890), Perroux (1955) and Myrdal (1957). These approaches have generated

important analytical developments in the field of the New Economic Geography (Krugman, 1991 and1995), Structuralist Regional Economics (Storper, 1996 and 1997, Scott and Storper, 1986, Piore andSabel, 1984), Innovation Economics (Audretsch, 1995; Audretsch and Feldmam, 2004; Maillat, 1996 and1998) and in the literature about modern Industrial Districts (Schmitz, 1997; Nadvi and Schmitz, 1994;Musyck and Schmitz, 1995; Pyke, Becattini and Sengenberger, 1990). At the same time, the concept hasbeen incorporated in the policy guidelines of international development agencies (OECD, 2001 and 2007,World Bank, 2009)

The concept of industrial agglomerations might also be articulated to the proliferation of empiricalstudies developed from an evolutionary theoretical perspective. In this sense, some relevant attributes ofthose agglomerations may be stressed: 1) Geographical proximity; 2) Sectoral specialization and intra-sectoral division of work; 3) Close inter-firm collaboration; 4) Inter-firm competition essentially based oninnovation rather than on lower wages; 5) Social embeddedness that facilitates trust, reciprocity andsocial sanction; 6) Different forms of state support. In a complementary approach, McCormick (1999)and Basant (2002) argue that the emergence of industrial agglomerations can facilitate knowledge flowsthrough the following effects: 1) a Market Access effect related to due to the attraction of customers andthe associated knowledge; 2) a Labour Market Pooling effect due to specialized skills that becomesconcentrated, which facilitates learning and knowledge transfer; 3) a Intermediate Input effect due to theemergence of specialized suppliers of inputs and services which enhance the dynamism of verticalknowledge flows; 4) Technology Spillovers associated with the diffusion of knowledge that permits arapid flow of information/know-how among firms operating in proximity; 5) a Joint Action effect basedon cooperation practices and networking that facilitates knowledge circulation and integration.

Bell and Albu (1999) develop an analysis of the elements that strengthen the integration ofcapabilities in the knowledge systems associated to those agglomerations, stressing the differencesbetween elements that increase knowledge-using capabilities and elements that increase knowledge-changing capabilities. Concerning the first aspect, they mention, at the firm level, the passive experienceof production (learning by doing in production''), the active efforts to adopt and improve specifictechnologies and the improved practices derived from trial and experimentation on specific tasks. At thelevel of the agglomeration, they mention the mobility of skilled labor, the improvement of operationalskills and the know-how diffusion of specialized machinery or production-related services. Concerningthe knowledge-changing capabilities at the firm level, they mention the technological understandinggained from investment efforts (learning by doing investment'') and the generic technological insightsgained from adapting and improving existing technologies (learning by changing''). At the level of the

agglomeration, this dimension involves collective practices in planning and technology management, aswell as collaboration in tests and experiments to adapt machinery or to develop product designs. The


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creative collaboration between firms and local technology-based institutions seems also to be veryimportant.

Expanding the evolutionary argument, the methodological framework argues that geographicalproximity is not enough for the achievement of collective learning processes and innovative dynamism. Infact, this proximity might also be articulated with other elements, such as the institutional, cultural andtechnological context, in order to foster the existence of an innovative system. According to this

perspective, the presence of multiple ties among local actors performs a critical role to strengthencompetence building processes in industrial agglomerations. The establishment of those ties may providethe necessary conditions to promote localized learning processes and to consolidate innovative pathsbased on incremental innovations. On the other hand, in order to avoid the danger of a geographical lock-in related to the exhaustion of learning processes, the agglomerations might also retain capabilities tobreak productive practices and to change technological paths (Cooke and Morgan, 1998). In this sense,Christopherson, Michiel and Tyler (2010) argue that these processes could be related to a kind ofregional resilience, defined as the capacity of a territory to overcome short-term or long-term economicadversity, which 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 institutional

context 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. In an evolutionary approach, theconcept of industrial agglomerations tries to articulate the static competitive advantages generated by thespatial agglomeration with dynamic competitive advantages obtained through the strengthening oflearning practices and multiple forms of cooperation. In this perspective, positive externalities generatedby the process of spatial agglomeration mentioned in the original analysis of Marshall (1890) mightalso be articulated to structural and institutional factors that stimulate collective actions oriented to theimprovement of local competences and to the strengthening of the innovative performance of local agents.It is supposed that the systematic interchange of information and knowledge generates a process ofcollective learning, which accelerates the diffusion of technological and organizational innovations. Theseflows 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 a lot of possibilities toimprove the competitiveness of local productive systems due to informal mechanisms of learning. Theevidence also shows that the competences of the firms inserted in those agglomerations might beupgraded based on the circulation of information and skilled workers. Another aspect that must bestressed refers to the impacts of the interchange of information to the definition of industrial standards,normalization procedures and quality control techniques.

In order to allow the integration of complex knowledge, particular importance might be attributedto interactive learning mechanisms structured at the local level. This process tends to transcend the sphereof the individual firm, involving continuous interaction between those firms and other institutions inserted

in local innovative systems. In this sense, learning-by-interaction becomes a critical aspect of industrialagglomerations. Typically, interactions develop in the form of cooperative efforts, formal or informal.Then, cooperation can be seen as a particular case of learning-by-interacting. Given the tacit character ofknowledge, innovation usually requires several forms of interaction among economic agents, who in turninteract with technology-based and knowledge-based institutions. In this context, the technologicaldevelopment of a firm becomes increasingly dependent on the capabilities of other firms, competitors,clients and suppliers. In this sense, it is possible to differentiate horizontal cooperative links among firmsinserted in similar stages of the value chain and vertical cooperative links involving firms, suppliers,customers and other agents and organizations. Among those organizations, it can be mentioned researchcenters, technical schools, public institutions and private representative associations. All these agentsrepresent the complex institutional context in which cooperative links are built.


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3. Identification of productive agglomerations: instrumental criteria and general characteristics

The identification of industrial agglomerations is based on data collected from the Annual Reportof Social Information provided by the Brazilian Ministry of Labour and Employment (RAIS / MTE) forthe year 2005. These data cover formal workers registered, the number of establishments present indifferent industrial sectors and the total amount of remuneration received by the employees. The

methodology also considers a spatial criterion to locate economic activities in the territory based on theconcept of homogeneous micro-regions, defined by IBGE, the Brazilian Institute of Economic Statistics.The economic activities were distinguished according to a 3-digit Brazilian industrial classification,which is compatible with the International Standard Industrial Classification (ISIC). Different groups ofeconomic activities were identified, in order to establish specific criteria for the identification of matchedpairs comprising those activities and the territorial concentration of the entrepreneurial activity.

Based on data extracted from RAIS, the existence of industrial agglomerations were evaluatedwith the aid of a traditional tool applied to regional economic studies, based on the calculus of LocationalIndexes (QLs) that try to consider the relative weight of the indicator (number of employees, number ofestablishments or amount of remuneration received) in each field of economic activity for the micro-regions, when compared to the same weight for the whole country. For all 559 micro-regions and 111

economic activities, Locational Indexes (QLs) were calculated, considering the relative weight in terms oftotal employment, number of establishments and amount of remuneration received. Additional criteriawere incorporated in the analysis, considering a minimum density of the number of establishmentslocated in each pair sector/micro-region, as well as a criterion related to the relevance of the employmentgenerated by the sector in the micro-region compared to the country as a whole. The criteria of densityand sectoral relevance were also adapted according to the type of economic activity, in terms of greateror lesser spatial concentration of the employment1. Based on these criteria, industrial agglomeration wereidentified for each one of the 111 economic activities, being distributed among the 559 micro-regions,based on the criteria of "territorial relative specialization", "density" and "sectoral relevance", which arealso adapted to the particularities of different groups of economic activities, as shown in Table 1.

Table 1 - Criteria applied to the identification of industrial agglomerations as a function of thedegree of spatial concentration of economic activities

Types of industries /Criteria AppliedRelative Specialization Index

(QL)Density Criterion Sectoral relevance criterion

Manufacturing sectors withHighSpatial Concentration

QL Employment > 1 and QLEstablishment > 1 and QLRemuneration > 1

Minimum of 2establishments

Minimum share of 1,5% to theemployments of the micro-region in the whole country.

Manufacturing sectors withMedium-High Spatial Concentration




Manufacturing sectors withMediumSpatial Concentration




Manufacturing sectors withMedium-Low Spatial Concentration

QL Employment > 1 and QLEstablishment > 1 and/or QLRemuneration > 1



Manufacturing sectors withLowSpatial Concentration

QL Employment > 1 and QLEstablishment > 1 and/or QLRemuneration > 1



1Five groups of economic activities were identified using a spatial concentration index (Herfindahl-Hirschman), with eachgroup being associated to a quintile in the ascending order of that index: 1) High Spatial Concentration; 2) Medium-High

Spatial Concentration; 3) Medium Spatial Concentration; 4) Medium-Low Spatial Concentration; 5) Low SpatialConcentration.


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Table 2 highlights some characteristics related to the set of industrial agglomerations identified.Based on the outlined procedures, 1.129 agglomerations were identified, representing an average of 10.2agglomerations per sector of economic activity, defined from a 3-digit industrial classification. Theaverage degree of the Locational Index (QL) in terms of jobs reaches 12.78 for the industrialagglomerations, indicating that the correspondent economic activities are representative and have asignificant degree of specialization, compared to the national territory. The total amount of jobs generated

by the industrial agglomerations reaches 2,2 million, equivalent to 33.23% of the employment inmanufacturing industries in the country. The weight of the employment generated by industrialagglomerations corresponds to 2.65% of the employments generated in the micro-regions. The industrialagglomerations comprise 65.147 establishments, which represent 20.05% of the manufacturingestablishments of the country. On average, each industrial agglomeration consists of 57 establishments,which represent only 0.85% of the total establishments in the micro-region they are located. The datarelated to remuneration - comprising salaries in minimum wages for December 2005 - indicate that theremuneration generated by industrial agglomerations represent 33.75% of the remuneration generated bythe manufacturing sector in the country and 2.73% of the total remuneration generated in the micro-regions. Establishments integrated to the industrial agglomerations tend to be small, comprising 34employees and paying a remuneration equivalent to 3.9 times the minimum wage.

The criteria applied to the identification of industrial agglomerations seem to generate relevantresults, in the sense that, on average, 33% of the employment and remuneration and 20% of manufactureestablishments in the country were classified as belonging to those agglomerations. Based on theobservation that approximately another third of these variables refer to large metropolitan areas2, the restof the Brazilian industry comprising firms not included in industrial agglomerations - also concentrateapproximately another third of that total. This feature points to a balance in the Brazilian industrialstructure, in terms of the relative weight of industrial agglomerations, large industrial metropolitan areasand relatively isolated firms not included in those agglomerations.

2Six large metropolitan areas received differential treatment due to the fact that they have more than 150 thousand formal jobsin the manufacturing sector: So Paulo, Campinas, Belo Horizonte, Rio de Janeiro, Curitiba and Porto Alegre. This differential

treatment might be justified due to the large size and huge diversification of its industrial structures, which distorts the criteriaused to identify industrial agglomerations. In this sense, firms located in the correspondent micro-regions can be considered

jointly, forming a group of large industrial metropolitan areas, which were not considered for the purpose of identification ofindustrial agglomerations.


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Table 2 Industrial agglomerations: general characteristics in terms of employment,establishments and remuneration (2005):

Values

Dimension Indicator

1th Quintile -Sectors withHigh SpatialConcentration

2th Quintile -Sectors with

Medium-High

SpatialConcentration

3th Quintile -Sectors with

Medium SpatialConcentration

4th Quintile-Baixa / MdiaConcentrao

5th Quintile -Baixa

ConcentraoTotal

Agglomerations

N 84 170 184 239 452 1.129

% 7,44% 15,06% 16,30% 21,17% 40,04% 100,0%Mean* 3,82 7,73 8,36 10,86 19,65 10,17

Employments

Locational Index QL** 42,46 12,19 10,58 8,83 10,46 12,78N of employments 126,833 265,288 451,613 604,05 762,455 2.210.239

% 5,74% 12,00% 20,43% 27,33% 34,50% 100,0%Mean** 1.509,92 1.560,52 2.454,42 2.527,41 1.686,85 1.957,70

% in Brazil* 43,74 34,47 29,08 27,52 31,43 33,23% in Micro-region**** 1,66 1,23 2,56 2,54 3,10 2,50

Establishments

Locational Index QL** 5,53 2,32 2,53 1,81 1,16 8,17N of establishments 634 2,815 12,953 20,872 27,873 65.147

% 0,97% 4,32% 19,88% 32,04% 42,78% 100,0%Mean** 7,55 16,56 70,40 87,33 61,67 57,70


Remuneration***

Locational Index QL** 41,34 12,06 10,70 11,63 14,77 15,01Amount of remuneration 1.358.954,28 1.557.194,71 1.932.309,67 1.732.334,44 2.140.171,06 8.720.964,16

% 15,58% 17,86% 22,16% 19,86% 24,54% 100,0%Mean** 16.178,03 9.159,97 10.501,68 7.248,26 4.734,89 7.724,50


Notes: * Defined for each group of economic activity, considering a 3-digit industrial classification. ** Defined for each industrial agglomeration identified.*** Defined in terms of Minimum Wages paid in December 2005. **** Defined as a share of the total employments, establishments or remunerations of themicro-regions in which the agglomerations are located.Source: data extracted from RAIS / MTE (2005)..

After identifying industrial agglomerations from the pairs of groups of economic activities andmicro-regions, the analysis tries to advance in a comparative analysis between the characteristics of thefirms inserted in industrial agglomerations and of those not inserted in this kind of structure. This analysisis developed from a consolidated database, containing micro-data extracted from two Industrial Surveys

carried by IBGE for the year 2005: PIA (a general Annual Industrial Survey) and PINTEC (an InnovationSurvey structured from the general guidelines of the Oslo Manual). A comparative analysis betweenindustrial agglomerations and the rest of the Brazilian industry was carried out based on this database,using data collected at the firm-level. For this purpose, after the identification of firms inserted inindustrial agglomerations (considering the superposition of criteria related to territorial and sectoralspecialization), a new instrumental variable was defined, which would takes the value of 1 for firmsinserted in those agglomerations and 0 if they not meet this criterion. Based on this procedure, theanalysis applies econometric instruments (a ordered probit model) in order to identify the specificcharacteristics of the processes of learning, cooperation and innovation in firms inserted in industrialagglomerations, vis--vis the firms that are not included in those agglomerations.

4. Learning, cooperation and innovation in industrial agglomerations: methodology and analyticalprocedures

Based on the analytical framework of this study, we can postulate the hypothesis that the existenceof industrial agglomerations can generated dynamic comparative advantages. The benefits ofgeographical proximity might be associated to tangible externalities on the production of goods andservices as well as to intangible externalities related to the increase in the stock of knowledge, providedby interactive learning processes. This section identifies the characteristics of learning processes,cooperation and innovative performance of firms, differentiating two control groups: companies insertedin industrial agglomerations and companies not inserted in those agglomerations. To capture thesedifferences, a set of econometric models based on ordered Probit regressions was applied. First, theanalysis checks the impact of the processes of learning and cooperation in the introduction of productinnovations and then a second procedure tries to capture those impacts in the introduction of processinnovations.


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We have opted for the use of ordered probit models, believing that these models capture moreaccurately the reality of the innovative processes in Brazilian industry. Similar analyzes have tended touse conventional probit models applied to the database provide by PIA and PINTEC, in which thedependent variables assume a dichotomous character. In these studies, the dependent variable takes thevalue 1 for firms that have innovated and 0 for those that did not innovate 3. However, one characteristic

of PINTEC is that most of the asked questions related to innovative efforts, learning strategies andcooperation are applied only to firms that have introduced some kind of innovation in product and/orprocesses. Therefore, to enable the application of the model, the studies usually restrict the dependentvariable, which would take the value 1 when the company introduced a new product in the domesticand/or international market (in the case of product innovations), assuming a similar value when thecompany introduced a new process for the sector (in the case of processes innovations); otherwise, thedependent variable takes the value 0. According to those criteria, firms that introduced a new productalready existent on the market, or adopted a new process already existing in the sector, are not consideredinnovative. This type of procedure creates serious distortions in the analysis, in face of some specificitiesof a significant part of the innovative firms in the Brazilian industry.

From a total sample of 5.854 innovative companies surveyed by PINTEC, 2.505 introduced a new

product for the firm, but already existent on the market, and 4.042 introduced a new process for the firm,but already existent in the sector. In contrast, 1.067 companies have introduced a new product for thedomestic or international market and 670 companies have introduced a new process for the sector. Thesefigures show that most of the introduction of innovations in the Brazilian industry has a characteristic ofbeing effectively imitation of new products and processes. Therefore, the ordered probit models permit tocapture these particularities enabling a better understanding of innovative processes in the Brazilianindustry. To capture these aspects, in the model proposed the dependent variable assumes threecategories: not innovate, innovate for the company and innovate for the market-sector. Because of thisfeature two cutoffs will be considered.

The result obtained by the model will capture the impacts in terms of the marginal probability of aparticular event, considering the possibility of introducing "imitative" innovations or more "relevant"innovations4 . Specifically, three marginal probabilities related to each explanatory variable will beevaluated: the first refers to the possibility of the firm does not innovate, the second to the possibility ofintroduce a product/process new to the firm but already existent on the market/sector and the thirdconcerns the possibility of introduce a product/process effectively new to the market/sector.

The vectorX of explanatory variables can be divided into two sets, individually stipulated for eachcompany of the sample. The first set refers to the variables that represent the main focus of the analysis,comprising processes of learning and cooperation. The second set refers to control variables used in theanalysis5.

In the first set, eight variables (indicators) seek to quantify the processes of learning andcooperation in which the firms were engaged. These indicators were derived from some asked questions

of PINTEC relating to: i) the importance attributed to different sources of information used between 2003and 2005, for the development of products (goods or services) and/or processes which are technologicallynew or substantially improved; ii) the importance attributed to different partners in the development ofcooperative activities. Therefore, these variables seek to transform qualitative attributes (the importance

3See, for example, De Negri, et al.(2005).4"Imitative" innovations are conceived as the introduction of new processes and products for companies but already existentson the market or sector. On the other hand, relevant innovations are conceived as the introduction of new products and newprocesses that are effectively inexistent in the market and in the sector.5The introduction of control variables reflects the fact that the innovative performance of firms in the sample might also beexplained from a broader spectrum of variables that go beyond the limited set of variables that we intend to analyze. Thehypothesis related to the introduction of these variables assumes that there are other factors that might influence the innovativeperformance of companies in addition to variables related to the processes of learning and cooperation.


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attributed to certain event) in quantitative attributes, finding a value between 0 and 1. Table 3 shows thesevariables as well as the event captured by each indicator6.

Table 3 Indicators related to interactive processes, constructed from PINTEC/IBGE (2005)according to the event/agent captured by the indicator

Indicator Event Agents

Internal Learning (INTLEARN)

Relevance of the agents as a source of

information

Department of R&D; Other departments.

Vertical Learning (VERTLEARN)Relevance of the agents as a source ofinformation

Suppliers of machinery, equipment, materials, components andsoftware, Customers or consumers.

Horizontal Learning(HORIZLEARN)

Relevance of the agents as a source ofinformation

Competitors; Consulting firms and independent consultants.

Learning with S&T institutions(S&TLEARN)


Universities and research institutes, Centers for professional trainingand technical assistance; Institutions dedicate to test, trial andcertification.

Other Sources of Learning(OTHERLEARN)


Acquisition of licenses, patents and know how; Conferences, meetingsand publications, Fairs and exhibitions; Computerized informationnetworks.

Vertical cooperation (VERTCOOP)Effective involvement in cooperativepractices with the agent.

Customers or consumers; Suppliers.

Horizontal cooperation (HORCOOP)Effective involvement in cooperativepractices with the agent

Competitors; Consulting firms..

Cooperation with S&T institutions(S&TCOOP)

Effective involvement in cooperativepractices with the agent

Universities and research institutes, Centers for professional trainingand technical assistance.

Source: Own elaboration based on PINTEC / IBGE (2005).

The first indicator intends to capture the importance of interactions inside the firms, referring tointernal learning. Other indicators capture the characteristics of learning and cooperation processesdeveloped with external actors. The vertical learning and vertical cooperation indicators try tocapture the importance of the relations within the supply chains in which firms are embedded. Regardingcompetitors and consulting firms the analysis try to consider how the firms interact horizontally with therest of the production structure, through the definition of two indicators horizontal learning andhorizontal cooperation. Indicators related to the S&T infrastructure try to capture the relevanceattributed by the firms to universities and research centers as a relevant source of information learningwith S&T institutions as well as the effective involvement in cooperation with them (cooperation withS&T institutions). A final indicator captures the importance attributed to other sources of informationthat contribute to the improvement of learning, for instance, licenses and patents, conferences andmeetings, etc. This set of variables constitutes the main focus of analysis, which intends to compare thecharacteristics of these processes in the firms inserted in industrial agglomerations vis--vis those firmsthat are not included in these agglomerations.

The second set of variables comprises control variables that seek to capture some structuralfeatures of the firms that can influence their innovative performance. Two variables are related to firmssize: the Number of Employees (EMP) and the Net Sales Revenues (NETSALE) ". The hypothesisthat better-paid workers have greater incentives to contribute to innovative processes is captured in the

model through the variable Average Wage (AW). The existence of a direct relationship between thegrowth in productivity and the innovative performance of firms is tested using two variables:Productivity Value added per Employees (PRD) and Value added (VAD). The participation inforeign trade and its effect on innovative performance are captured by two variables: Trade Balance(Exports Imports) (TBE) and Flow of Foreign Trade (FFT). The use of these variables aims toestimate the participation of the firms in international trade (both as an importer and exporter), as well astheir trade surpluses, which could encourage the obtainment of a higher innovative performance.

6 The indicators were calculated as follows:k

n

I

k

l

li

ji

=

=1

,

,, where jiI, corresponds to the indicator j (INTLEARN,

VETLEARN, HORIZLEARN, ...) to the firm i, j represents the set of events/agents that conforms each indicator; k = 1,2,...,n

corresponds to the number of events/agents grouped in each set jand, lin , correspond to the level of relevance attributed by

the firm i to the event l (l j) according to the following codes: high importance = 1; medium importance = 0,66; lowimportance = 0,33; no importance = 0. Each one of those indicators would vary within a range from 0 to 1.


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The relationship between firms innovative efforts and their innovative performance is tested byfive variables: (1) Employees in R&D activities (EMPR&D); (2) Spending on Innovative Activities /Net Sales (SIA/NETSALE); (3) R&D Expenses / Spending on Innovative Activities (R&D/SIA); (4)Spending on machinery and equipment / Spending on Innovative Activities (SME/SIA); (5)Expenditures on Training and Competence Building / Spending on Innovative Activities (ETCB/SIA).This set of variables was obtained based on information provided by micro-data from PIA and PINTEC

for the year 2005, which are individually collected by each firm in the sample.Additionally, a variable was introduced with the aim of differentiating two groups of firms: firmsinserted in the industrial agglomerations previously identified and firms that are not inserted in them. This"agglomeration dummy" variable takes the value 1 when the firm is included in any of the industrialagglomerations and 0 if it is not. So, three groups of explanatory variables were defined in the model:variables related to the processes of learning and cooperation, control variables and dummyagglomeration variables.

The dependent variables would be related to the introduction of product innovation (INOVPROD),being also based on data from PINTEC (2005). These variables assume the following intervals: i) 1 iffirm iintroduced a new product for domestic and/or international market; ii) 0.5 if firm iintroduced a newproduct for the company but already existent on the market, iii) 0 if if firm ihas not introduced product

innovation. Three ordered probit models were considered in the analysis, concerning the introduction ofproduct innovations. The parameters and were estimated by maximum likelihood procedure. Allprerequisites for the application of the ordered probit regression are met for these models. The errors arenot autocorrelated, there are no correlations with the explanatory variables and those errors have expectedvalues equal to zero. Multicollinearity is minimized for two reasons: the size of the sample and thetransformation of the explanatory variables in standardized variables with mean zero and standarddeviation equal to one (Hair et al, 2005; Johnson and Wichern, 1999). To prevent the heteroscedasticity inthis type of regression, we use the marginal probabilities.

The statistical likelihood ratio (LR) is rejected at the significance level of 1% in all models, i.e.,the hypothesis that all estimated slope coefficients are statistically different from zero is not accepted inany case. The level of adjustment of the model verified by McFadden R2 index and the classificationbased on the expected probability suggest that the variables used in the study reinforce the capacity topredict the probability of innovation, particularly by type of innovation.

5 Analysis of results

5.1 - Descriptive statistics of the variables

The models constructed were based on the set of variables presented in the previous section. Table4 highlights the descriptive statistics of these variables. Concerning the control variables, it appears thatthe firms in the sample employ an average of 400 employees, achieving net sales revenues in the amount

of R$ 91,5 millions per firm in 2005

7

. The average productivity of these firms can be considered high, inthe range of R$ 66.000 per worker, and the average annual salary paid in 2005 was approximatelyR$ 18.400. The value added to production per worker would be in the range of R$ 46.000, an amount thatcan also be considered high, when compared with the mean of the Brazilian industry.

Regarding the participation in foreign trade, the firms in the sample generated R$ 140 billion intrade with foreign countries; on average, the sum of exports was equivalent to R$ 35 million, generating atrade surplus of R$ 13 million. Information about innovative efforts reveal that, on average, R&Ddepartments of these firms are small, employing only 4 employees, which is equivalent to approximately1% of their employees. Spending on innovative activities was equivalent to 5.5% of net sales, focusingprimarily on the acquisition of machinery and equipment (31% of total expenditure on innovativeactivities), and, in a much smaller scale, to the development of R&D activities (comprising 12% of total

7Information presented in Brazilan monetary unit, the Brazilian Real.


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expenditure on innovative activities). It is noteworthy that efforts related to training seemed also to be low,comprising only 1.9% of the total expenditure on innovative activities.

Table 4 Descriptive statistics of the independent variables Data extracted from PIA andPINTEC (2005). (N = 3.978)

Variables MeanMinimum

ValuesMaximum

ValuesStandardDeviation

Internal Learning (INTLEARN) 0,1980 0 1 0,334Vertical Learning (VERTLEARN) 0,6148 0 1 0,307Horizontal Learning (HORIZLEARN) 0,3094 0 1 0,288Learning with S&T institutions (S&TLEARN) 0,1760 0 1 0,272Other Sources of Learning (OTHERLEARN) 0,4769 0 1 0,341Vertical cooperation (VERTCOOP) 0,0677 0 1 0,219Horizontal cooperation (HORCOOP) 0,0239 0 1 0,114Cooperation with S&T institutions (S&TCOOP) 0,0343 0 1 0,144Employees (EMP) 401,69 1 45.176 1311,16Net Sales (NETSALE)* R$ 91.527,73 R$ 8,31 R$ 11.809.132,37 433.316,80Productivity Value added per Employees (PRD)* R$ 66.320,00 R$ 0,012 R$ 2.293,43 387,40Value added (VAD)* R$ 46.821,19 R$ (152,34) R$ 1.556,41 133,16Average Wage (AW)* R$ 18.391,97 R$ - R$ 1.557,77 247,48Trade Balance (Exports Imports) (TBE)* R$ 13.251,48 R$ (1.605.304,85) R$ 4.790.014,98 158.793,96Flow of Foreign Trade (FFT)* R$ 35.238,09 R$ - R$ 12.185.566,42 297.411,61Employees in R&D activities (EMPR&D) 4,1090 0 3278 54,3245Spending on Innovative Activities / Net Sales (SIA/NETSALE) 5,5% 0% 98,38% 0,1136R&D Expenses / Spending on Innovative Activities (R&D/SIA) 12,16% 0% 100% 0,2604

Spending on machinery and equipment / Spending on Innovative Activities (SME/SIA) 41,33% 0% 100% 0,4246Expenditures on Training and Competence Building / Spending on Innovative Activities (ETCB/SIA) 1,98% 0% 100% 0,0825

* Information in R$ 1.000,00 (Thousand Brazilian Reais).Source: Own elaboration based on micro-data from PINTEC / PIA IBGE (2005).

For the set of indicators related to the processes of learning and cooperation, it seems that themain form of interaction developed by the firms if the sample refers to vertical learning (VERTLEARN),with an average indicator of 0.61. Learning related to other sources of information (OTHERLEARN) andlearning with competitors and consulting firms (HORIZLEARN) have a secondary importance for thefirms in the sample, with indicators of 0.47 and 0.30, respectively. The internal learning (INTLEARN) is,on average, considered of low importance for the firms in the sample with an average indicator of 0.19, aswell as the Learning with S&T institutions, with an average indicator of 0.17.

Regarding cooperative relations, as well as to the learning sources, those related to customers andsuppliers assume greater importance for the companies analyzed. However, it is emphasized thatcooperation with universities and training centers is considered more important that cooperation withcompetitors and consulting firms. Another point to be highlighted is the high standard deviation shown bythese indicators, indicating that the firms in the sample have very different behaviors concerning thesevariables.

The frequency distribution of the variable "dummyagglomeration" reveals that it takes the value 1for 1.885 firms in the sample. Thus, it indicates that 47% of the firms in the sample are inserted in theindustrial agglomerations previously identified in the analysis, and that 53% are not inserted in thosestructures.

Table 5 shows the frequency distribution of the dependent variables related to the innovative

performance of the firms in the sample. Regarding the introduction of innovative products, it appears that43% of the firms introduced new products that were already existent on the market; in contrast, only14.35% of the firms introduced products effectively new for the domestic and/or international market..

Table 5 Frequency distribution of the indicators related to the introduction of product andprocess innovations - Data extracted from PINTEC (2005) (N = 3.978)

Indicator / ValueNot Innovate - 0 Innovate for the firm 0,5

Innovate for the market/ sector 1

N % N % N %INOVPROC 680 17,09% 2.935 73,78% 363 9,13%

Source: Own elaboration based on micro-data from PINTEC IBGE (2005).

Therefore, based on the descriptive statistics of the variables used in the analysis, it is observedthat, for the firms in the sample, the relations of learning and cooperation are mainly concentrated alongthe production chain. The firms in the sample invest approximately 5% of its net sales revenues in


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innovative activities. However, these activities are mainly focused on the purchase of machinery andequipment, with R&D expenses comprising only 12% of the expenses with innovative activities andefforts related to training being particularly reduced. Participation of those firms in the international tradeflows is high, with the sample generating trade surpluses consistent with the volume of their foreigntransactions.

Regarding innovative performance, it is observed that companies have high capabilities to

"imitate" products and processes, but the introduction of new products and processes occurs on a smallerscale. The sample is also divided in similar percentages among the firms inserted in industrialagglomerations and those not included in these structures. The next subsection presents the resultsobtained from the econometric models applied to capture the differences between these two groups.

5.2 - Determinants of the introduction of product innovations

The first ordered probit model was constructed for the dependent variable related to theintroduction of innovative products. In this case the dependent variable takes the value 1 if firmsintroduced new products for the domestic/international markets, 0.5 if firms introduced new productsalready existents and 0 if they not introduced any of these innovations. The variables presented in the

previous section was suffered a mathematical manipulation (being standardized) and operates asindependent variables. Table 6 shows the results obtained by the model in terms of significance of thevariables related to the processes of learning and cooperation, as well as to the set of control variables. Itshould be noted that, for the set of 12 control variables, 50% of them were not statistically significant at asignificance level of 10%; however, from the 8 variables that capture the processes of learning andcooperation only 3 were not significant. This feature reinforces the influence of those processes for theinnovative performance in terms of product innovations.

Table 6 Results of OrderedProbit Model for the explanatory variables selected to innovativefirms with product innovation.

Ordered Probit

Dependent Variable: INOVPROD N=3978

Explanatory Variables Coef.Std. Err.

z

Dummy Agglomeration -0,029 0,038 -0,760Internal Learning (INTLEARN) 0,456 *** 0,028 16,020Vertical Learning (VERTLEARN) 0,067 *** 0,021 3,130Horizontal Learning (HORIZLEARN) -0,046 ** 0,022 -2,090Learning with S&T institutions (S&TLEARN) 0,070 *** 0,024 2,940Other Sources of Learning (OTHERLEARN) -0,033 0,021 -1,600Vertical cooperation (VERTCOOP) 0,095 *** 0,027 3,480Horizontal cooperation (HORCOOP) -0,031 0,026 -1,210Cooperation with S&T institutions (S&TCOOP) 0,042 0,027 1,560Employees (EMP) 0,085 ** 0,041 2,100Net Sales (NETSALE)* 0,389 *** 0,149 2,600Productivity Value added per Employees (PRD)* 0,017 0,063 0,260Value added (VAD)* 0,029 0,032 0,900

Average Wage (AW)* 0,041 0,152 0,270Trade Balance (Exports Imports) (TBE)* -5,240 *** 1,062 -4,930Flow of Foreign Trade (FFT)* 5,287 *** 1,063 4,980Employees in R&D activities (EMPR&D) 0,055 0,094 0,590Spending on Innovative Activities / Net Sales (SIA/NETSALE) 0,076 0,067 1,140R&D Expenses / Spending on Innovative Activities (R&D/SIA) 0,065 ** 0,026 2,520Spending on machinery and equipment / Spending on InnovativeActivities (SME/SIA) -0,296 *** 0,020 -14,570Expenditures on Training and Competence Building / Spending onInnovative Activities (ETCB/SIA) 0,009 0,019 0,460

Cutoff point1 -0,383246 0,0296Cutoff point2 1,20884 0,0346

Adjustment ModelLog likelihood: -3324,3811 Pseudo R2: 0.1670LR chi2(6): 1346,76***

*Significance level of 10%, ** Significance level of 5%, e *** Significance level of 1%.Source: Own elaboration based on micro-data from PINTEC / PIA IBGE (2005)..


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It was also found that the control variables related to economic performance (productivity andvalue-added production), remuneration of employees, spending on innovative activities as a proportion ofnet sales, employees in R&D and training efforts showed no significant coefficients, suggesting that thesevariables do not influence the introduction of product innovations for the firms in the sample. Due tothese features, the model was "re-estimated" without the control variables that seemed to be notsignificant. The data presented in Table 7 comprises the results obtained after this procedure from the

ordered probit model for the entire sample.

Table 7 Ordered Probit model results for the selected explanatory variables and significantcontrol variables concerning product innovations

Ordered Probit Marginal effects

Dependent Variable: INOVPROD N=3.978 Mean

Explanatory Variables Coef. Std. Err. z Not Innovate Innovation for the firmInnovation for the

market

Dummy Agglomeration -0,030 0,038 -0,800 0,01165 -0,00660 -0,00506Internal Learning (INTLEARN) 0,460 *** 0,028 16,550 -0,17701 *** 0,10013 *** 0,07688 ***Vertical Learning (VERTLEARN) 0,067 *** 0,021 3,110 -0,02565 *** 0,01451 *** 0,01114 ***Horizontal Learning (HORIZLEARN) -0,045 ** 0,022 -2,070 0,01748 ** -0,00989 ** -0,00759 **Learning with S&T institutions (S&TLEARN) 0,071 *** 0,024 2,960 -0,02718 *** 0,01537 *** 0,01181 ***Other Sources of Learning (OTHERLEARN) -0,032 0,021 -1,540 0,01232 -0,00697 -0,00535Vertical cooperation (VERTCOOP) 0,095 *** 0,027 3,490 -0,03666 *** 0,02074 *** 0,01592 ***Horizontal cooperation (HORCOOP) -0,031 0,026 -1,190 0,01177 -0,00666 -0,00511

Cooperation with S&T institutions (S&TCOOP) 0,043 0,027 1,590 -0,01653 0,00935 0,00718Employees (EMP) 0,089 ** 0,039 2,300 -0,03414 ** 0,01931 ** 0,01483 **Net Sales (NETSALE) 0,391 *** 0,128 3,040 -0,15034 *** 0,08504 *** 0,06530 ***Trade Balance (Exports Imports) (TBE) -5,222 *** 1,056 -4,940 2,00906 *** -1,13642 *** -0,87261 ***Flow of Foreign Trade (FFT) 5,271 *** 1,057 4,990 -2,02766 *** 1,14703 *** 0,88062 ***R&D Expenses / Spending on InnovativeActivities (R&D/SIA)

0,066 ** 0,026 2,580 -0,02554 ** 0,01445 ** 0,01109 **

Spending on machinery and equipment /Spending on Innovative Activities (SME/SIA)

-0,294 *** 0,020 -14,550 0,11291 *** -0,06387 *** -0,04904 ***

Cutoff point 1 -0,383246 0,0296Cutoff point 2 1,20884 0,0346

Adjustment Model

Log likelihood: -3330,196 AIC: 6694,392 Pseudo R2: 0,1670

LR chi2(21): 1335,13*** BIC: 6801,297

*Significance level of 10%, ** Significance level of 5%, e *** Significance level of 1%.Source: Own elaboration based on micro-data from PINTEC / PIA IBGE (2005).

Based on the data, all coefficients related to the explanatory variables seem to be statisticallysignificant, with the exception of "Dummy agglomeration", "Other Sources of Learning ", "HorizontalCooperation" and "Cooperation with S&T institutions. The signs of coefficients relate to the explanatoryvariables seem to be positive8, validating the argument related to a positive influence of those dimensionsfor the introduction of product innovations.

It should be noted that the marginal probabilities were calculated for an average firm in the sample,reflecting the situation in which the firm performs those processes with the same intensity of the mean ofthe sample. Regarding the control variables, we find that variables related to foreign trade seems to bethose that most positively and negatively influence the likelihood of firms introduce product innovations,both to imitative (new to the firm) as well as to more relevant ("new to the market") innovations. An

increase of one unit, that is, one standard deviation above the average, in the flow of foreign tradeincreases expressively the likelihood of firms to innovate in products: this probability increases 114% forthe possibility of introducing innovations new to the firm and increases 88% for the possibility ofintroducing innovations new to the market9. In contrast, an increase in the foreign trade surplus reducesthe likelihood of firms to innovate, both to innovations new to the firm as well as to innovations newto the market. This fact reflects, in part, the structure of Brazilian exports, which are strongly based oncommodities that make it difficult the introduction of product innovations.

Concerning other control variables, it should be noted that firms size positively influences thelikelihood of innovate in products. The variation of a unit of employee increases the likelihood of firms to

8 Except for those related to Trade Balance and to Spending on machinery and equipment/Spending on InnovativeActivities.9However, it should be noted that the amount achieved by the marginal probabilities regarding the variables related to foreigntrade should be analyzed with caution, being affected by the high standard deviation of this variable.


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Table 8 Results of Ordered Probit models for innovative firms inserted and not inserted inindustrial agglomerations Product innovations.

Ordered Probit Firms Inserted in Industrial Agglomerations (N=1.885) Firms Not Inserted in Industrial agglomerations (N=2.093)

Dependent Variable: INOVPRODMarginal Effects Marginal Effects

Mean Mean

Explanatory Variables Not InnovateInnovation for

the firm(incremental)

Innovation for themarket

(relevant)Not Innovate

Innovation forthe firm

(incremental)

Innovation for themarket

(relevant)

Internal Learning (INTLEARN) -0,17807 *** 0,08847 *** 0,08960 *** -0,17233 *** 0,10803 *** 0,06430 ***Vertical Learning (VERTLEARN) -0,03041 ** 0,01511 ** 0,01530 ** -0,02248 ** 0,01409 ** 0,00839 **Horizontal Learning (HORIZLEARN) 0,03060 ** -0,01520 ** -0,01540 ** 0,00816 -0,00511 -0,00304Learning with S&T institutions (S&TLEARN) -0,04302 *** 0,02137 *** 0,02164 *** -0,01440 0,00903 0,00537Other Sources of Learning (OTHERLEARN) 0,01055 -0,00524 -0,00531 0,01505 -0,00944 -0,00562Vertical cooperation (VERTCOOP) -0,04224 *** 0,02099 *** 0,02125 *** -0,02241 0,01405 0,00836Horizontal cooperation (HORCOOP) -0,00111 0,00055 0,00056 0,02814 * -0,01764 * -0,01050 *Cooperation with S&T institutions (S&TCOOP) 0,00335 -0,00166 -0,00169 -0,04141 *** 0,02596 *** 0,01545 ***Employees (EMP) -0,03016 0,01499 0,01517 -0,03070 0,01925 0,01145Net Sales (NETSALE) -0,32834 *** 0,16314 *** 0,16521 *** -0,06598 0,04137 0,02461Trade Balance (Exports Imports) (TBE) 1,35647 *** -0,67402 *** -0,68246 *** 3,52368 *** -2,20890 *** -1,31473 ***Flow of Foreign Trade (FFT) -1,37607 *** 0,68370 *** 0,69238 *** -1,82499 * 1,14409 * 0,68090 *R&D Expenses / Spending on InnovativeActivities (R&D/SIA)

-0,02098 0,01043 0,01056 -0,03117 ** 0,01954 ** 0,01163 **

Spending on machinery and equipment /Spending on Innovative Activities (SME/SIA)

0,10955 *** -0,05443 *** -0,05512 *** 0,11649 *** -0,07303 *** -0,04346 ***

Cutoff point 1: -0,327 Cutoff point 1: -0,33867Cutoff point 2: 1,151 Cutoff point 2: 1,36522

Adjustment Model Adjustment Model

Log likelihood: -1573,4309 AIC:3178,86 Log likelihood: -1735,24 AIC: 3502,49LR chi2(14): 740,47*** BIC: 3267,25 LR chi2(14): 608,36*** BIC: 3592,83Pseudo R2: 0,19 Pseudo R2: 0,14

*Significance level of 10%, ** Significance level of 5%, e *** Significance level of 1%.Source: Own elaboration based on micro-data from PINTEC / PIA IBGE (2005).

For firms inserted in industrial agglomerations, the number of significant variables related tolearning processes and cooperation is equivalent to the whole sample and is slightly larger than to firmsnot inserted in those agglomerations11. Regarding the control variables, two of them - Number ofEmployees (EMP) and the ratio between R&D Expenses and the Spending on Innovative Activities(R&D/SIA) - cease to be significant for firms inserted in agglomerations and, in addition, the variables

related to the external trade - Trade Balance captured by the difference between Exports and Imports(TBE) and the Flow of Foreign Trade (FFT) reduce their influence in the introduction of productinnovations. Simultaneously, the variable related to the size of the firm (NETSALE) expands its influence.In contrast, to firms not inserted in industrial agglomerations, the two variables related to the size of thefirms Number of Employees (EMP) and Net Sales (NETSALE) become more significant. At the sametime, to this group of firms, the variables related to foreign trade - Trade Balance and the Flow of ForeignTrade (FFT) tend to influence firms innovative performance in a much higher scale when compared tothe overall sample and to the group of firms inserted in industrial agglomerations.

Regarding the interactive processes, for both firms inserted and not inserted in industrialagglomerations, internal learning seems to be the factor with higher impact in the likelihood to innovateboth to incremental and radical innovations. For firms inserted in industrial agglomerations, it seems clear

that indicators related to interactive practices influence on a higher scale the likelihood to innovate,especially with regard to the introduction of more relevant innovations, those that are new to the market.This trend can also be observed to the interactions along the supply chain, as well as to interactions withuniversities and research centers, with a one-unit increase in the S&TLEARN indicator raising thelikelihood to innovate incrementally by 2.13% and to innovate radically by 2.16%.

A comparative analysis of the two groups of firms (inserted and not inserted in industrialagglomerations) indicates that interactive processes influence the likelihood of introducing innovationsnew to the firm in a similar way: 13.07 % for firms inserted in agglomerations and 13.04% for firmsnon-inserted. In contrast, concerning the introduction of more relevant innovations (innovations to themarket), the influence of these processes tends to be much higher for the firms inserted in industrialagglomerations. While an increase of one unit in the set of variables related to interactive processes

11Five variables are relevant for firms inserted in industrial agglomeration vis--vis four variables for firms not inserted inthem.


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increases by 7.7% the probability of firms not inserted in industrial agglomerations introduceinnovations to the market, for the group of firms inserted in industrial agglomerations the samevariables raise by 13.24% that probability.

We can conclude that, concerning product innovations, interactive processes seem to be positivelyrelated to the likelihood of introducing this type of innovation. Specifically, the internal learning, relationsalong the productive chain - Vertical Learning (VERTLEARN) and Vertical Cooperation (VERTCOOP)

- raise considerably the likelihood of firms to innovate in products, both in terms of innovations to thefirm and to the market.. The influence of the interactive processes in firms innovative performancetends to be more effective when those firms are inserted in industrial agglomerations. Therefore,interactions seem to influence on a higher scale the introduction of product innovations to firms insertedin industrial agglomerations, and those effects seem to be stronger to innovations that are new to marketcompared with those that are new to the firm.

6. Concluding Remarks

An exploratory analysis of industrial agglomerations was developed from an evolutionaryapproach, trying to articulate the static competitive advantages generated by the spatial agglomeration

with dynamic competitive advantages obtained through the strengthening of learning practices andmultiple forms of cooperation. The procedures used in the study allowed the identification of a set of1.129 agglomerations in the Brazilian manufacturing sector which together were responsible for 33% ofemployment, 20% of establishments and 33% of the remuneration generated in those activities. Therelative degree of territorial concentration, for different groups of economic activities, influences thecreation of industrial agglomerations. A differentiation between firms inserted in industrialagglomerations and firms not inserted in them has clarified some issues related to the determinants of theinnovative performance of the these two groups.

Based on econometric models, we conclude that interactive processes affect on a higher scale theintroduction of product innovations. Mechanism of internal learning (INTLEARN) and verticalcooperation (VERTCOOP) along the productive chain seem to be the factors that raise more intensivelythe likelihood of firms to innovate in products, both incrementally as radically. The impact of interactivepractices to the innovative performance of firms seems to be more effective when the firms are inserted inindustrial agglomerations. Therefore, it is suggested that interactions influence on a higher scale theintroduction of product innovations in this group of firms. As a general trend the analysis suggests thatfirms inserted in industrial agglomerations develop learning processes and cooperation practices moreintensively, when compared to the rest of the Brazilian industry. The greater virtuosity of these processesenables those firms to obtain higher innovative gains.

Regarding analytical unfolding of the research, it seems very important to advance in a betterunderstanding of the learning processes and cooperative practices at the level of structured industrialagglomerations, particularly through more detailed empirical analysis. Assuming that relevant

institutional differences might be identify in local spaces, it is important to delimit the maincharacteristics of these factors and how they can influence firms innovative performance. It is alsoimportant to consider other dimensions not adequately captured by general innovation surveys likePINTEC, which are formatted to capture general characteristics of the whole industry. In a context ofhuge regional and sectoral heterogeneity, such as observed in the Brazilian industry, the analyticalframework should be flexible enough to capture this heterogeneity. In this sense, empirical studiesfocused on more representative models of industrial agglomerations, based on a similar methodology tothat explored in the article, are likely to contribute to a better understanding of the subject.

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