Academy–industry links in Brazil: evidence about channels and benefits for firms and researchers

Science and Public Policy August 2010 0302-3427/10/070485-14 US$12.00 Beech Tree Publishing 2010 485

Science and Public Policy, 37(7), August 2010, pages 485–498 DOI: 10.3152/030234210X512016; http://www.ingentaconnect.com/content/beech/spp

Academy–industry links in Brazil: evidence about channels and benefits for

firms and researchers

A C Fernandes, B Campello de Souza, A Stamford da Silva, W Suzigan, C V Chaves and E Albuquerque

Knowledge flows between universities, public research institutes and firms may take various channels according to agents’ motivations and expected benefits. Models were estimated to investigate which channels of interaction lead to which benefits for firms, universities and research institutes in Brazil. Bi-directional channels are shown to be particularly relevant, yielding both innovative and productive benefits for the firms and intellectual and economic benefits for the universities. As for interactions between firms and research institutes, bi-directional channels are the most important in terms of intellectual benefits for the researchers and innovative benefits for the firms. These findings seem to confirm the dual role of the universities, versus a more focused one for the research institutes, and raise policy issues. Moreover, a negative correlation between investment in internal research and development and productive benefits for the firms was found, indicating that the expected benefits of public expenditure are not turning into innovation.

HE CONCEPT OF A NATIONAL SYSTEM

of innovation (NSI) (Freeman, 1988; Nelson, 1993) is the starting point of the present inves-

tigation. It is a complex institutional setting that char-acterises modern capitalist economies, involving a

diversity of actors (firms, universities, research insti-tutes, government, financial agencies and legal

framework) a division of labour among them, and in-formation channels that link them together. Within

such an institutional setting, the relationships required

to exchange and combine knowledge and experience

between the actors are a crucial factor differentiating

developed and non-developed NSIs). Among them, those between universities and public research insti-tutes (public research organisations (PROs)) and

firms stand out as they work in a complementary way, bringing benefits to both academy and industry

(Mowery and Sampat, 2005). From the perspective of

most developed industrial countries, these relation-ships are a key, dynamic component of the NSIs, es-pecially in the form of two-way links (Narin et al., 1997; Pavitt, 1991; Rosenberg, 1990). They thrive in

academic environments that value the so-called ‘en-trepreneurial role’ of the universities beyond the tradi-tional focus on higher education, production of

scientific knowledge, and provision of critical stances

towards society (Narin et al., 1997; Meyer-Krahmer

and Schmoch, 1998; Cohen et al., 2002; Mowery and

Sampat, 2005; Mazzoleni and Nelson, 2007). Although they vary according to the industrial

sector, information and knowledge produced by and

T

A C Fernandes (corresponding author) is at UFPE, Depto. Ciên-cias Geográficas, Av. Acad. Hélio Ramos s/n, CFCH 60. andar, Recife, PE, 50740-520, Brazil; Email: [email protected]. B Campello de Souza is at Rua Gervásio Campelo nº 102, Prado, Recife, Pernambuco, 50.720-180, Brazil; Email: [email protected]. A Stamford da Silva is at Rua Esmeraldino Bandeira, nº 105, Apto. 603, Graças, Recife, Pernambuco, 52.011-090, Brazil; Email: [email protected]. W Suzigan is at DPCT-IG/Unicamp Caixa Postal 6135 13083-970 Campinas, SP Brazil; Email: [email protected]. C V Chaves is at Rua Profº Antônio Aleixo, 300/1301, Lourdes 30.180-150, Belo Horizonte, Minas Gerais, Brazil; Email: [email protected], and [email protected]. E Albuquer-que is at Cedeplar-FACE-UFMG (University of Minas Gerais), Gabinete 3069, Av. Antônio Carlos, 6627 Belo Horizonte, MG CEP 31270-901 Brasil; Email: [email protected].

See acknowledgements on p 497.

Academy–industry links in Brazil

Science and Public Policy August 2010 486

accumulated within PROs are important inputs for industrial innovation (Rosenberg, 1982). Benefits include providing knowledge complementary to that available internally in the firm and different ap-proaches for solving technological problems by al-lowing access to specific laboratories and highly skilled research personnel, and also by supplying skilled human resources, producing positive effects on product development and productivity (Rosen-berg and Nelson, 1994; Arvanitis et al., 2008). It has also been recognised that these benefits increase along with the firm’s absorptive and financial capacities to explore them (Bierly et al., 2009). For PROs, linkages with industry may also bring impor-tant benefits for the academy, leading to academic publications, verification of hypotheses, testing of

theoretical arguments, opportunities to access knowledge produced by industry, verifying knowl-edge under production in academic environments, and entry into additional funding schemes for aca-demic research (Meyer-Krahmer and Schmoch, 1998; Welsh et al., 2008).

Benefits for academy and industry are also reported

as complex and multifarious in their more advanced

form – the US case (Klevorick et al., 1995; Cohen et al., 2002) – where bi-directional channels are fre-quent. This leads to the idea that in developing economies PRO-industry (PRO-I) interactions may not only be less frequent (Cassiolato et al., 2003; Suzigan and Albuquerque, 2009), but also concen-trated in supposedly less virtuous channels such as consultancy, materials tests, and training, flowing in one direction from the PRO to industry (Arza, pp 473–484, this issue). Such features help understand the deficiencies of their NSI, particularly because in immature NSIs PRO-I links play an even more im-portant role. Academic institutions are important for firms and governments from the early stages of de-velopment, no matter how primitive such institutions may be. They produce solutions to local problems and emerging demands, act as ‘antennas’ for local firms to access knowledge and new technologies from more developed countries, and help local firms to build the in-house research competencies they still lack. As global market pressures push firms in de-veloping countries to improve their innovative ca-pacity, PRO-I interactions are ways of assessing academic resources that reduce the time needed to enter a better international division of labour without forgoing traditional functions and social needs (Albuquerque, 1999; Suzigan et al., 2009).

This role of PRO-I interaction makes clear that in-stitutions from immature NSIs engage in interactions in different ways from those of developed NSIs thus showing different characteristics in terms of size and quality of university education and research, and firms’ involvement with research and development (R&D) activities (Rappini et al., 2009). PRO-I (and also inter-firm) links are limited in less-developed economies due to long lasting historical decisions in favour of technology imports, rather than domestic production, due to the late industrialisation and de-velopment of institutions (Suzigan and Albuquerque, 2009). This led to limitations on collaboration and can be seen as one of the consequences of the un-derdeveloped nature of countries at the periphery of capitalism (Furtado, 1986), a category that includes the four countries involved in this special issue. In this way, the role of the PRO in NSIs varies accord-ing to socio-economic development, with strong his-torical roots, having very specific patterns of change over time and space, which are difficult to construct (Rosenberg, 1982; Mokyr, 1990). It is also worth considering that different motivations push each ac-tor towards interacting. As long as the PRO-I inter-actions in developing economies are relatively few

Ana Cristina de Almeida Fernandes is an architect. She ob-tained a PhD in economic geography from the University of Sussex, UK. She is an associate professor at the Federal University of Pernambuco, Brazil where she is a member of the Post-graduate Program in Geography and the Post-graduate Program in Therapeutic Innovation. Her main re-search and teaching interests include: geography of innova-tion, regional innovation systems, urban and regional development, urban economics, and geography of health.

Bruno Campello de Souza is a psychologist. He is a joint professor at the Department of Business Administration, Federal University of Pernambuco, a member of the Gradu-ate Program in Business Administration and of the Gradu-ate Program in Cognitive Psychology of the same university. His research interests include: innovation, acad-emy–industry interactions, organisational consultancy, hu-man resources management, and the human and social impacts of technology.

Alexandre Stamford da Silva is an engineer, joint professor at the Federal University of Pernambuco, Department of Economics, and a member of the Graduate Program in Economics of the same university. His research interests include: innovation, academy–industry interactions, eco-nomic growth, mathematical models, game theory, and the economics of health.

Wilson Suzigan is an economist, a full professor at the Sci-ence and Technology Policy Department, Unicamp (State University of Campinas). He is the editor of the Brazilian Journal of Innovation (Revista Brasileira de Inovação). His main research areas include: economic history, industrial development, industrial and technology policy, economics of innovation, science and technology indicators, local sys-tems of production and innovation.

Catari Vilela Chaves is a full-time professor at PUC Minas (Catholic University of Minas Gerais). She has Master’s and PhD degrees in economics from Cedeplar, University of Mi-nas Gerais (UFMG). Her research interests include: indus-trial economics and economics of technology. At present she has a research grant to study productivity on industrial technology development.

Eduardo da Motta e Albuquerque is an associate professor of economics at UFMG, Belo Horizonte, Brazil. He teaches economics of science and technology, contemporary politi-cal economy and comparative industrialisation, in both un-dergraduate and graduate courses. He obtained a PhD in economics from the Universidade Federal do Rio de Janeiro in 1998. His research interests include: national systems of innovation and development, catching up processes, and contemporary political economy.



and concentrated in less demanding channels, draw-ing on Arza (pp 473–484, this issue), the present paper claims that not only the particular channels through which knowledge flows between firms and PRO matter, but also the relative importance of dif-ferent channels of interaction for each agent’s bene-fits. This paper focuses on this topic.

As developed by Arza (pp 473–484, this issue), under specific conditions some channels lead to a better balance of benefits and costs resulting from interaction. As such, bi-directional channels, where knowledge flows in both directions, imply interac-tion between skilled researchers and innovative firms leading to virtual cycles of knowledge creation and diffusion. Other benefits can be expected from channels that only involve one-way flows of intel-lectual resources from one agent to another, mostly from PROs to firms. In this way, bi-directional channels involve more knowledge-intensive interac-tions, yielding stronger benefits for both the firms and the researchers. Nevertheless, different motiva-tions influence the agents’ choices of channels, ac-cording to their different expectations, so that other channels may be preferred.

Adding to Arza (pp 473–484, this issue), it is ex-pected that the benefits from interaction also differ between universities and research institutes given their specific roles in the NSI. This paper also un-derstands that interactions between firms and PROs are complex and change according to a country’s development and history, constituting a dynamic process that reflects the structural heterogeneity of the NSI. Thus, at the periphery of capitalism, they present particular characteristics that should cer-tainly be taken into account in theoretical debate and policy-making. Concerning the latter, this is particu-larly relevant given that investment in R&D has in-creased in recent years as part of the development of Latin American countries (Science and Development Network, 2009). That investment may not yield the expected returns if the particularities of PRO-I links with respect to the relationship between channels and benefits are not well understood, as it seems to be the case in Brazil. In this way, differences in the channels of interactions used by the interacting agents matter for the benefits available to each. It follows that this concerns policy-making because it may better inform which knowledge channels to fos-ter through incentives when targeting specific bene-fits. For instance, it may be more effective for Brazilian innovation policy to stimulate cooperative R&D projects between firms and PROs rather than patents and licensing. Understanding the benefits as-sociated with each channel of PRO-I interaction may provide an excellent instrument for public policy by allowing information about the particular responses of each agent to specific incentives.

This paper aims to contribute to the production of such information from the Brazilian case. In order to explore which channels of interaction lead to which benefits for firms and PROs (universities, on the one

hand, and research institutes, on the other), we have distinguished four types of channels according to the direction of knowledge flows and to the agents’ mo-tivations to engage in interaction. Concerning one-way knowledge flows, mostly from PROs to indus-try, there are three channels:

a traditional channel which refers to the classical forms of PRO-I links such as publications and conferences;

a services channel for the exchange of scientific and technological services for money; and

a commercial channel dealing with commerciali-sation of technologies developed by PROs’ own research efforts (such as patents, start ups and incubators).

As for two-way knowledge flows where both agents provide intellectual resources, there is:

a bi-directional channel which includes joint and contract R&D projects and R&D networking stimulated by long-term linkages oriented towards knowledge creation and innovation for PRO and industry, respectively.

Regarding benefits, two groups are considered: benefits perceived by firms, oriented to either (i) short-term production activities (tests, quality control, contact with university students for future recruitment) or (ii) long-term innovation strategies; (complementary and substitute research, improved ability to obtain access to technological informa-tion); and benefits perceived by PRO oriented to in-tellectual results (inspiration for future research and new collaboration projects), as opposed to economic results (access to additional financial resources and research inputs).1 The data comes from two surveys run in 2008 and 2009 of research group leaders and firms from several Brazilian states. The results pre-sented here are preliminary and form part of a broader effort to understand NSIs in less-developed countries.2

This paper is divided into five sections, besides this introduction. The second section presents a brief overview of the context within which Brazilian PRO-I interactions occur. The third section describes the methodology, the construction of the database and the model developed to analyse it. We then discuss the results and finally summarise the main conclusions.

PRO–I interactions in Brazil

Studies based on survey data to assess PRO-I links in Brazil are scarce. Recent literature draws on in-terviews and information released by government, universities and firms to focus on the university sys-tem (Rapini, 2007), university–industry–government relations (Dagnino and Velho, 1996), or, more



generally, science and technology interactions (Bernardes and Albuquerque, 2003). The govern-ment survey on industrial technology is the main source of data for studies of inter-sector differences in interaction and science and technology indexes (Fapesp, 2010).

A pioneering study of university–industry interac-tions in the state of Minas Gerais (Rapini et al., 2009) highlights the dual role of universities in their interactions with firms: the substitution for and complementing of firms’ own R&D. It also shows a marked heterogeneity of interactions between indus-trial sectors and science and engineering fields, which is typical of Brazil. Successful cases like the steel industry, petrochemicals, aircraft, and agro-industries are the fruit of long-term processes of capability building and learning to interact by both research institutions and production units.

Other possible successful cases were restricted by the late development of PROs, which affected their capability building in several science and engineer-ing fields. The first Brazilian university was founded in 1934, although some higher education schools had been established in the 19th and early 20th cen-turies. Until the 1950s engineering courses remained limited to mining, metallurgy, agronomy and con-struction. The teaching of graduate courses did not start until the period of the late 1960s and early 1970s and only thereafter was teaching linked to re-search activities in prominent Brazilian universities. Public research institutes developed relatively earlier to attend to local social and economic demands mostly from agriculture and public health, but those related to industrial activities were only founded from the 1950s onwards (Suzigan and Albuquerque, 2009).

In a similar way, late and structurally restricted industrialisation created a poor pattern of demand from industries. Until the 1960s light industrial con-sumer goods still represented over 50% of the Brazilian manufacturing production. After that, in-dustrial policies stimulated a more diversified indus-trial structure, but failed to promote endogenous technology development, favouring instead the import of technology, thus weakening the domestic industry learning process. Currently, Brazilian in-dustry is internationally competitive in commodities like steel, cellulose and paper, food products, and in some manufactured goods, the most notable example being aircraft. In all of them there is a long history of interactions involving PROs, firms and govern-ment. A desirable spreading of success stories de-pends on public policies to stimulate the capability building and interactions between PROs and firms.

However, a poor pattern of demand from industry is reflected in the few PRO-I interactions in sectors that are considered strategic for improving the nation’s technological skills (Rapini et al., 2009). An important reason for that is the country’s late in-dustrialisation combined with extreme income con-centration, inadequate inward orientation, and many

years of high inflation, all of which led to a deeply rooted culture of importing technological packages. Therefore, it is not surprising that most of the know ledge and technological capabilities in the country are concentrated in PROs. Recent public policies have targeted such interactions, enhancing the evolu-tionary process and driving the creation of scientific institutions ‘ahead of demand’. This has been the case with information and communication technolo-gies, computer sciences and computer engineering.

On the other hand, despite recent advances, Brazil still lags behind with respect to its NSI. Differently from South Korea, the Brazilian path is slow and shows a relatively small trade-off between scientific and technological production, as Ribeiro et al. (2009) demonstrate. The scientific side of the system has improved considerably but the mismatch with production technology is representative of a less-developed national socio-economic formation. Fur-thermore, it is understood that a NSI constitutes a precondition for overcoming underdevelopment, as it produces solutions to problems faced by the economy and society (Suzigan et al., 2009).

Method and econometric models

Databases

Production of this paper’s database followed a two-step process. First, a data set was built with the re-search groups that declared having any sort of rela-tionship with firms to the 2004 census of the Directory of Research Groups developed by CNPq.3 Only 2,151 out of 19,470 groups declared that they interact with firms and other organisations. These in-teractive research groups stated that they have asso-ciations with 3,875 production units (firms and organisations). Secondly, two surveys were carried out. One was designed for the research groups, which correspond to the PROs in this paper, and the other for the firms mentioned by the former. The PROs survey used an online form (developed in co-operation with the IDRC project) applied to the uni-verse of interactive groups in 2008, with a return rate of 46.7% (1,005 groups located in 25 Brazilian states and the Federal District). Nevertheless, 114 responses were not used due to either processing problems (50) or incomplete information (64). The survey of firms focused on industrial firms, exclud-ing state agencies, municipalities, ministries, secre-tariats, non-governmental organisations and all types of associations. These totalled 1,688 firms to which, in 2009, an online form was sent to be filled out by the individual in charge of the interaction with the academy. This produced a total of 326 forms (a 19.3% return rate), out of which eight forms were incomplete, leading to a total of 318 observations.

The surveys that allowed the production of the present paper’s data involve several aspects of PRO-I interaction as experienced in Brazil, with variables



including types of interaction, benefits and difficul-ties experienced, results obtained, and information channels for knowledge exchange, most measured along a Likert 1–4 scale.

Interactive research groups in Brazil The 1,005 re-search groups that answered the questionnaires are distributed in eight major fields of knowledge ac-cording to the CNPq classification, but have been grouped here into five major fields (see Table 1). The majority of the groups within the survey were either from engineering (32.14%) or the biological and health sciences (22.00%).

The PRO survey includes several aspects of inter-actions with firms organised into five sets of questions regarding types of linkage, results of the interaction, benefits for the research group, difficul-ties experienced, and channels of information for transferring knowledge from group to firm.

Regarding benefits, answers refer to the question of the form sent to the leaders of the research groups4 which deals with the degree of importance they place upon their interaction with firms.5 Bene-fits from interaction are presented in Table 2, having been classified as intellectual benefit (IB) and eco-nomic benefit (EB). For descriptive purposes, only the answers in the categories ‘moderately important’ and ‘very important’ are considered.

All eight benefits were considered to be ‘moder-ately’ or ‘very’ important by more than half of the

respondents, ‘new research projects’, ‘exchange of knowledge or information’, and ‘ideas for new PRO-I cooperation projects’ being the three highest rated.

Thus, answers to the researcher’s questionnaire in-volving channels of information have been grouped in accordance with the classification proposed by Arza (pages 473–484, this issue): bi-directional (VB), commercial (VC), services (VS) and tradi-tional (VT) channels. Table 3 shows the results in terms of this classification and the degree of impor-tance assigned by the leaders.

The results indicate a clear alternation of tradi-tional, ‘bi-directional’ and ‘service’ channels among the ten top-rated items. Commercial channels, in turn, emerge only at the 11th position (patents), and then occupy the last three. This suggests that the same research group may consider both ‘traditional’ and ‘bi-directional’ interactions to be of similar im-portance, valuing the academic side of the interac-tion as part of its learning activities, not just as knowledge creation.

Interactive firms in Brazil

Table 4 shows the characteristics of the 325 respon-dent firms6 in terms of size and origin of capital, displayed in decreasing order of the number of firms and size of the establishment.

Large firms prevail in the survey (202), followed by small (77) and medium (46) firms. In three cases, the majority of the capital deployed was of private, domestic or mixed (private, domestic, and/or for-eign) origin. Public capital was only important for large firms (17).

The distribution of the firms’ ISIC code, revision

4,7 showed a predominance of food production

(9.54%), followed by pharmochemicals (8.00%), chemical production (7.38%), gas and electricity

(7.08%), scientific research and development activi-ties (7.08%), manufacturing of computer, electronic

and optical products (6.77%), and computer pro-gramming, consultancy and related activities (6.46%).

A descriptive analysis of the interactions from the viewpoint of the firms was also done, again

Table 1. Number of research groups per major field of knowledge Brazil, 2008

Groups Major field of knowledge

N %

Rank

Engineering 323 32.14 1st

Biological and human sciences 221 22.00 2nd

Agricultural sciences 200 19.90 3rd

Exact and earth sciences 158 15.72 4th

Humanities 103 10.25 5th

Total 1.050 100.00

Source: Adapted from Rapini et al. (2009)

Table 2. Two main types of benefits and degree of importance of relationships with firm: intellectual and economic

Moderately and very important Item Type of benefit

N %

Rank

New research projects IB 863 85.87 1st

Exchange of knowledge or information IB 822 81.79 2nd

Ideas for new U/PRI-I cooperation projects IB 820 81.59 3rd

New social networks IB 727 72.34 4th

Reputation IB 710 70.65 5th

Materials received (inputs) for research EB 705 70.15 6th

Funds EB 702 69.85 7th

Share equipment/instruments EB 542 53.93 8th

Notes: Percentages refer to total of 1,005 researchers who responded U/PRI-I university/public research institute–industry

Source: Brazil Survey (2009)



considering only the answers ‘moderately’ and ‘very important’. Benefits were classified into benefits for the firm’s production activities and benefits for the firm’s innovative activities (see Table 5).

The two most important benefits were connected to production activities, such as ‘perform tests nec-essary for your products/processes’ (63.19%) and ‘to use resources available at universities and public labs’ (61.66%). Benefits related to innovative activi-ties ‘technology transfer from the university’ come in third place (60.12%).

Concerning the channels of interaction, the same typology used for the research group leaders was applied (VB, VT, VC, and VS).

For the interactions of firms with universities (Table 6), the traditional channel ‘publications and

reports’ (69.63%) stands out, followed by the bi-directional ‘joint or cooperative R&D projects’ (68.10%), and again by the traditional ‘recently hired graduates with advanced degree’ (62.88%). The commercial channels ‘patents’ (33.13%) and ‘licensed technology’ (32.82%) occupy the 10th and 11th positions, respectively, while those labeled as commercial stayed in the bottom three positions.

For the firms’ answers regarding the research in-stitutes (Table 7), unlike the case of the answers to the universities, the channels that occupy the top two positions are the bi-directional ‘joint or cooperative R&D projects’ (58.90%) and the traditional ‘publi-cations and reports’ (58.28%).

Model

General aspects

A common regression model was adopted, as sug-gested by Arza (pp 473–484, this issue), comprised of two linear equations, one for the research groups and the other for the firms. Its dependent variables are the benefits and its independent ones are the channels of interaction and the characteristics of the groups and firms.

Researchers (1)

Firms (2)

assuming that E(ε1,i ε2,i) = 0. Here, the above equations were estimated sepa-

rately through ordinary least squares, heteroskedastic

robust standard errors in the case of the firms (but not in the case of the universities and research institu-tions, see section on ‘Selection bias’). The variables

definitions and main results are presented below.8

Table 3. Channels of interaction and degree of importance of PRO-I interaction (data from research groups leaders)

Moderately and very important Item Type of channel

N %

Rank

Interactions resulting in publications VT 753 74.93 1st

Research contract VB 752 74.83 2nd

Conferences and seminars VT 747 74.33 3rd

Staff training VS 713 70.95 4th

Joint or cooperative R&D projects VB 709 70.55 5th

Informal information exchange VS 663 65.97 6th

Recently hired graduates with advanced degree VT 586 58.31 7th

Temporary personnel exchanges VS 534 53.13 8th

Individual consulting VS 524 52.14 9th

Engagement in networks with firm VB 462 45.97 10th

Patents VC 431 42.89 11th

Science and/or technology parks VB 403 40.10 12th

Incubators VC 399 39.70 13th

Licensed technology VC 388 38.61 14th

Firm is spin-off from a PRO VC 373 37.11 15th

Notes: Percentages refer to total of 1,005 researchers who responded VB = bi-directional, VC = commercial, VS = services, VT = traditional Source: Brazil Survey (2009)

Table 4. Characteristics of firms in terms of size and capital origin

Number of firms Size of firm Origin of capital

N %

Private national 69 89.61 Mixed 7 9.10

Public national 1 1.30

Small (less than 40 employees)

None 0 0.00 Sub-total 77 100


None 2 4.35

Medium (40–116 employees)

Public national 1 2.17 Sub-total 46 100


Public national 17 8.42

Large (above 116 employees)

None 2 0.99

Sub-total 202 100

Total 325


iiii RChBR ,111

iiii FChBF ,222



Research groups

According to the theoretical model proposed, two types of researcher’s benefits have been identified: economic and intellectual. Even though it can be as-sumed that both types of benefits for researchers and both for firms are somehow correlated, we estimate these equations separately. Therefore, Equation (1) was estimated for each benefit independently. Table 2 summarises the proposed classification.

The proposed ad hoc grouping of benefits from the PRO-I interaction taken from the researcher’s perspective seems to be in agreement with the re-sults of both a factor analysis and a cluster analysis of such variables, both yielding results which clas-sify the benefits into two sets which correspond very

closely to the ‘economic’ and ‘intellectual’ catego-ries, thereby validating the chosen dependent vari-ables.

The independent variables related to channels of interaction for knowledge transfer were grouped into four categories according to the type of interaction (VB, VC, VS, and VT). Such indexes were normal-ised from a 1–4 scale to one covering the range 0.25–1, and measure the importance of each mode of interaction (see Table 3).

Regarding this classification, factor analysis and cluster analysis seem to point to significantly different groupings, specifically, ‘innovation and entrepreneurism’ (science and technology parks, in-cubators, spin-offs, participation in networks, pat-ents, and technology licensing), ‘services’ (research

Table 5. Classification of two kinds of successful goals/benefits of interactions and degree of importance of interaction with PROs

Moderately and very important Item Type of benefit

N %

Rank

Perform tests necessary for products/processes BEN ACT PROD 206 63.19 1st

Use resources available at universities and public labs BEN ACT PROD 201 61.66 2nd

Technology transfer from university BEN ACT INN 196 60.12 3rd

Obtain technological/consulting advice from researchers and/or professors in solving production-related problems

BEN ACT PROD 194 59.51 4th

Contract research complementary research by universities and public labs

BEN ACT INN 190 58.28 5th

Contract research substitutive of in-house research by universities and public labs


Augment firm’s limited ability to find and absorb technological information


Obtain information about engineers or scientists and/or trends in R&D in field


Make earlier contact with outstanding university students for future recruitment

BEN ACT PROD 121 37.12 9th

Help with quality control BEN ACT PROD 91 27.91 10th

Notes: Percentages refer to total of respondents (326 firms) BEN ACT PROD = benefits for firm’s production activities, BEN ACT INN = benefits for firms innovative activities


Table 6. Channels of interaction and degree of importance of university–industry relationship to innovative activities of firms


N %

Rank

Publications and reports VT 227 69.63 1st

Joint or cooperative R&D projects VB 222 68.10 2nd

Recently hired graduates with advanced degree VT 205 62.88 3rd


Public conferences and meetings VT 199 61.04 5th

Contracting research with public institutes VB 178 54.60 6th

Consulting with individual researchers VS 170 52.15 7th

Participation in networks with public research institutes VB 158 48.47 8th

Science and/or technology parks VB 119 36.50 9th


Licensed technology VC 107 32.82 11th



Firm is owned by a public institute VC 50 15.34 14th

Firm is a spin-off of a university/research institution VC 50 15.34 15th

Notes: Percentages refer to total number of respondents (326 firms) Key as in Table 3




contracts, R&D agreements, consultancy, personnel training, internships, and hiring recent graduates), and ‘publishing’ (academic publications and meet-ings). This indicates that there is some discrepancy between the ad hoc classification and the ex post facto findings (see section on ‘Creating the mod-els’). However, to make comparisons with other countries in this special issue possible, we use the ad hoc classification.

Table 8 shows the description for the control variables which comprise the Ri vector.

Firms

The classification of the types of benefits from the perspective of the firms was done as for the of re-search group leaders, with two large groups: benefits derived from production activities and from innova-tive activities (see Table 5).

Regarding this classification, factor and cluster analysis seem to point to significantly different groupings, specifically: ‘technology’ (technology transfer from the university, advice on technology or consulting from researchers/professors for the solu-tion of production-related problems, which increase the firm’s ability to find and absorb technology in-formation), ‘future’ (information regarding future trends in R&D in scientific fields, early contact with talented university students for future recruitment, help in quality control), and ‘resources’ (hiring addi-tional research necessary for innovative activities, hiring research that the company cannot undertake, using resources from university and research labs, execution of tests that are necessary for the firm’s products and processes). Again, this indicates the ex-istence of some discrepancy between the ad hoc classification and the ex post facto findings (see section on ‘Creating the models’).

The channels of interaction, from the viewpoint of the firms, have been classified and normalised in an identical fashion to that of the research group leaders (see Table 6).

With respect to this classification, factor analysis and cluster analysis seem to point to significantly different groupings, specifically, ‘university entre-preneurism’ (patents, technology licensing, intern-ships, science and technology parks, incubators, spin-offs, university-owned company), ‘direct scien-tific communication’ (publications and reports, con-ferences and meetings, informal exchange of information, hiring of individuals with a degree or a higher qualification), and ‘services’ (consultancy, research hired from the university, research done jointly with the university, participation in networks involving universities). This also indicates the exis-tence of some discrepancy between the ad hoc clas-sification and the ex post facto findings (see section on ‘Creating the models’).

Table 7. Channels of interaction and degree of importance of research institute–industry interaction to innovative activities of firms


N %

Rank

Joint or cooperative R&D projects VB 192 58.90 1st

Publications and reports VT 190 58.28 2nd

Public conferences and meetings VT 182 55.83 3rd


Contracting research with public institutes VB 165 50.61 5th

Recently hired graduates with advanced degree VT 150 46.01 6th

Consulting with individual researchers VS 147 45.09 7th

Participation in networks with research institutes VB 143 43.87 8th


Science and/or technology parks VB 105 32.21 10th Licensed technology VC 103 31.60 11th


Firm owned by public institute VC 66 20.25 13th


Firm is spin-off from a university VC 46 14.11 15th

Notes: Percentages refer to total number of respondents (326 firms) Key as in Table 3


Table 8. Variables for characteristics of researchers

Variable Description

Gender Sex (male = 0, female = 1)

Group_cap Weighted average of level of degree held by members of group

Group_size Number of individuals in group

Age_index Researcher’s quadratic age (i.e. squared distance to sample mean)

Pasteur Classification according to ‘Pasteur quadrant’ where: ‘social sciences’, ‘human sciences’, and ‘literature, linguistics, and arts’ are ‘0’; ‘exact sciences and geosciences’ are ‘0.2’; ‘biological sciences’ are ‘0.4’; ‘health sciences’ are ‘0.6’, ‘agrarian sciences’ are ‘0.8’; and ‘engineering’ is ‘1.0’

Students Percentage of students in group (doctorate, master’s and graduate)

Source: adapted from Arza and Vazquez, pp 499–511, this issue



For firms’ benefits and channels, again, in order to make comparisons with other countries in this special issue possible, we use the ad hoc classifica-tion. The control variables for the benefits equation for the firms, which comprise the Fi vector, are described in Table 9.

Evidence from estimates

Creating the models

The present paper includes regression models using the ad hoc groupings of dependent variables (bene-fits) and some of the independent variables (chan-nels of interaction). Such constructs will be used to favour comparisons with other countries in this special issue. The findings show, for the Brazilian data, the results of the general model proposed by Arza (pp 473–484, this issue).

Selection bias

The sample used in the investigation included only re-searchers already engaged in PRO-I interactions in

2004, generating a selection bias with specific impacts

on both data analysis and the interpretation of results. This is the reason for the Heckman correction

(Heckman, 1979), which could not be deployed here. Assuming that behaviour is motivated by reward,

the bias would favour group leaders whose interac-tions with firms are seen to be advantageous, exclud-ing many who perceive them otherwise. This is confirmed by the findings that both intellectual and economic benefits indexes displayed a power prob-ability density function favoring higher levels (on a scale of 0–1, 73.28% of the economic benefits and 85.75% of the intellectual benefits were above 0.50). These distributions were not Gaussian, and linear regressions with them as dependent variables yielded non-Gaussian, heteroskedastic, distributions. Usually this would require corrections to the linear model (e.g. generalised linear models).

The bias favours certain values for the variables and/or indexes, and specific relationships between

them. In the case of the researchers, this seems to emerge as robust linear models with coefficients and probabilities that seem practically invariant to method, with estimates using multiple linear regres-sion, general linear models, and even generalised linear models (with different link functions and de-pendent variable distributions) yielding nearly iden-tical results. Therefore, a general linear model was used to favour comparability with the other models. Such robustness is essentially artificial, existing mainly due to strong bias.

The above considerations suggest that the results found are largely the consequence of the researchers favouring PRO-I relationships, and should be inter-preted as such. Findings about the relationship in more general terms would require a database includ-ing both group leaders that interact with firms and those who do not.

As for the firms, the dependent variables for pro-duction and innovation in the linear models had Gaus-sian distributions and were homoskedastic. This

allows the use of the general linear model, and sug-gests that, for this particular group, the selection bias

might not have been as strong as in the case of the

researchers, perhaps allowing wider interpretations.

Benefits for researchers

Table 10 shows the linear models for the intellectual benefits and the economic benefits for group leaders in the universities and also for those in the research institutes.

Before interpreting the estimates for the universi-ties and the research institutes separately, it is worth mentioning that the four linear regressions obtained presented a high degree of statistical significance, but explained only 11–36% of the variance of the dependent variables (adjusted R-squared). While still explaining a relevant portion of the phenomena, they did not account for most of the variability of the benefits.

Pro-industry interactions as experienced by univer-sities The bi-directional channels were the most in-tensely associated with positive economic and intellectual benefits, the former more than the latter. This may be due to the fact that only interactive groups were polled, where such channels are easier to effect and more prone to yield beneficial results. Such a disposition would impact the economic bene-fits more than the intellectual ones because eco-nomic incentives are powerful motivators, but one cannot promptly discard a role for the intrinsic at-tributes of such interactions. The traditional and ser-vice channels were also positively associated with both types of benefit though more strongly with the intellectual benefits than with the economic ones. This pattern was to be expected in the traditional in-teractions, given that they constitute the more canonic activities where universities excel. The ob-servation of a similar pattern when it comes to

Table 9. Variables that affect researchers’ benefits from collaboration

Variable Description

INN_PRODPROC Dummy that equals one when firm achieved innovations in products and processes

DEC_EMPLOYEES Deciles based on employment

NETWORK_AC_GOV Dummy that equals one when firm accesses public funds

SECTOR_IA Percentage spent in R&D in sector in 2009

Source: author’s own elaboration, from Arza and Vazquez, pp 499–511, this issue



services suggests that such activities are usually done in a way that, from the university’s perspec-tive, favours intellectual benefits.

The commercial channels did not show any sig-nificant association with economic benefits, and a negative one for the intellectual channels. Given the interactions involved, this may occur because the technology transfer mechanisms have only recently begun to be explored by the universities, so that few concrete results have been obtained, and perhaps at the cost of more productive opportunities in research and education (a form of ‘competitive inhibition’).

Almost all of the characteristics of the research groups themselves showed no association with any type of benefit, save only for a modest negative rela-tionship with the age of the group’s leader, perhaps indicating a tendency for the younger generations to have a more favourable view of PRO-I interactions. Otherwise, it seems that effectively engaging in the interaction is more relevant than the attributes of the group.

Pro-industry interactions as experienced by re-search institutes Here, the bi-directional channels were important for intellectual benefits, but not for economic benefits, whereas the services channel were shown to be relevant for economic benefits, and only marginally so for intellectual benefits. This points to a distinction between both types of interac-tion and their effects, contrary to what happened

with the university-based research groups. The tradi-tional and commercial channels, on the other hand, showed no impact.

Research institutes in Brazil do not have the same traditions as the universities, the former being more goal-oriented than the latter, which are much wider ranging in their activities. This specificity might lead the research institutes to consider services as simply services, unlike the universities that try to add re-search and education opportunities to whatever they engage in. Indeed, the pressure to produce academic knowledge (e.g. ‘publish or perish’) is much weaker in the research institutes, but the pressure to effec-tively contribute to economic improvements is higher.

As in the case of the universities, in the research institutes the absence of a relationship between the profile of the research groups and the benefits from the PRO-I interactions may occur because, in emerging NSIs, actually engaging in such interac-tions matters more than specific characteristics of the researchers.

Benefits for firms

Table 11 show the linear models obtained for the in-tellectual and economic benefits of the firms as a function of their interactions with the universities and also with research institutions.

Table 10. Model estimation on benefits for researchers (BRi = Chiα+Riδ+εi)

Universities Research centres Variable Parameter

BEN_ECO BEN_INT BEN_ECO BEN_INT

β 0.1279 0.2278 0.0764 −0.1383 VT

F 14.163*** 43.477*** 0.563 1.533 β 0.1092 0.2444 0.3533 0.2502

VS F 7.258*** 35.222*** 8.483*** 3.539* Β −0.0723 −0.1495 0.0787 −0.1717

VC F 2.429 10.060*** 0.301 1.191 β 0.4939 0.3262 0.1849 0.3946

VB F 86.686*** 36.601*** 1.491 5.645** β −0.0444 −0.0396 −0.1028 −0.0367

Gender F 2.383 1.838 1.381 0.146 β 0.0190 0.0181 −0.1262 −0.0532

Group_cap F 0.303 0.268 1.246 0.184 β 0.0014 0.0325 0.0055 −0.1233

Size F 0.002 1.211 0.003 1.354 β −0.0835 −0.0600 −0.0031 −0.0089

Age F 8.410*** 4.205** 0.001 0.007 β −0.0024 −0.0469 −0.1086 −0.0513

Pasteur F 0.007 2.566 1.218 0.226 β 0.0246 0.0233 −0.1870 −0.0563

Students F 0.515 0.445 2.655 0.200 β 0.1965 0.3097 0.5088 0.7077

Cons F 12.032*** 35.231*** 8.176*** 22.726***

N 789 789 102 102

F(conj.) 46.13 42.16 4.55 2.25

Prob > F <0.01 <0.01 <0.01 0.02

R-squared 0.3722 0.3515 0.3333 0.1982

Adjusted R-squared 0.3642 0.3431 0.2601 0.1101

Note: *** p<0.01; ** p<0.05; * p<0.1



The four linear regressions obtained displayed high statistical significance, but explained only 24–40% of the variance of the dependent variables (ad-justed R-squared). While managing to explain a relevant portion of the phenomena, they did not ac-count for most of the variability of the benefits.

Interactions with university researchers as experi-enced by firms Estimates show that in the firms’ interactions with researchers from the universities, the bi-directional channels stand out, particularly re-garding innovative benefits, but also for productive ones. As in the case of the researchers, this may be due to the selection bias as much as to the nature of these interactions. The traditional channels are also positively associated with both types of benefit, here in equal amounts. The relationship to the innovative benefits seems obvious, given the activi-ties involved. However, the association with the productive benefits suggests the practical potential of knowledge when applied to production.

The services channels were positively associated with productive benefits, but not to innovative ones, suggesting that, for the firms, this type of interaction is focused on the tasks involved, simply leading to specific practical results.

As in the case of the interactions as viewed by the university researchers, the absence of impact of the

commercial channels on both types of benefits may be a reflection of the low level of maturity of the NIS in question.

There was no association between the amount of investments in R&D and the innovative benefits, and a negative, marginally significant, association be-tween having produced innovations in the previous three years and such benefits. This shows that ob-taining innovation from the PRO-I interaction is not a consequence of the concrete measures taken by the firm to achieve innovation in general. Assuming that the number of employees in a firm is a proxy for in-vestment capacity, the fact that it was not associated with any of the benefits is not a simple matter of the availability of resources. Therefore, it seems that the generation of innovative benefits is relatively inde-pendent of the specific actions taken by the firms.

The lack of an association between implementing innovations in previous years and obtaining produc-tive benefits may be due to the same type of dynam-ics as in the case of the innovative benefits. In the light of this, and given the current status where any active attempts on behalf of the firms to obtain inno-vation seems futile, any spending of resources for that goal is ultimately a waste that may compete with more effective approaches, which could explain the negative association between expenditure in R&D and production benefits.

Table 11. Model estimation of benefits for firms (BFi = Chiα+Fiδ+εi)

Universities Research centres Variable Parameter

BEN_ECO BEN_INT BEN_ECO BEN_INT

β 0.1495 0.1479 0.0705 0.0840 VT F 5.303** 5.886** 0.742 1.099 β 0.1906 0.0474 0.2521 0.0207 VS F 6.456** 0.453 7.681*** 0.054 β 0.1428 0.0426 0.1219 0.0328 VC F 4.429 0.448 2.273 0.171 β 0.1660 0.4453 0.0894 0.3981 VB F 4.277** 34.922*** 0.892 18.468*** β −0.0472 −0.0822 −0.0620 −0.0846 inn_prodproc F 0.901 3.099* 1.381 2.688 Beta −0.0743 −0.0269 −0.0880 −0.0395 dec_employees F 2.283 0.339 2.763* 0.581 β −0.1858 −0.1806 −0.2109 −0.2018 network_ac_gov F 6.898*** 7.395*** 7.919*** 7.561*** β −0.1290 −0.0653 −0.1409 −0.0765 sector_ia F 7.292*** 2.123 7.679*** 2.359 β 0.3013 0.2587 0.4187 0.4104 Cons F 63.004*** 42.238*** 152.593*** 122.572***

317 317 317 317 N F(conj.) 17.49 24.42 12.14 14.14

<0.1 <0.1 <0.1 <0.1 Prob > F R-squared 0.3389 0.4172 0.2624 0.2930

Adjusted R-squared 0.3195 0.4001 0.2408 0.2723

VT β 0.1495 0.1479 0.0705

F 5.303** 5.886** 0.742

VS β 0.1906 0.0474 0.2521

F 6.456** 0.453 7.681***

Note: *** p<0.01; ** p<0.05; * p<0.1



The negative association between access to public funding and the productive or innovative benefits could indicate that such funds are oriented to firms that would not engage in PRO-I interactions on their own initiative. In such a situation, a relatively weak desire to engage in PRO-I interaction could lead to limited results, aside from the above interpretations regarding the lack of impact of the specific activities being funded.

Interactions with researchers from research institutes

as experienced by firms The interaction with the re-search institutes as experienced by the firms showed a

dynamic analogous to the experience of the research-ers themselves, i.e., a positive association between

services channels and productive benefits, and an-other one between bi-directional channels and inno-vative benefits. One might assume that the same

governing forces were involved in both cases, that is, the greater goal-orientation of the research institutes as compared to the universities, as expected.

Nevertheless, the associations between a firm’s characteristics and the benefits experienced by them from the PRO-I interaction with researchers from the research institutes showed a pattern similar to that of the interaction with researchers from the universi-ties. This may indicate a similar scenario concerning the difficulty in actively obtaining innovative and productive benefits from PRO-I interaction which can be credited to the already mentioned historical attitude of Brazilian firms towards favouring technology imports.

Conclusion

The evidence obtained tends not only to corroborate the notion by Suzigan et al. (2009) that PRO-I inter-actions yield both intellectual and economic results, but also that bi-directional channels are particularly relevant for Brazil, yielding both innovative and productive benefits for the firms and intellectual and economic benefits for the universities. As for the in-teractions between the firms and research institutes, bi-directional channels are the most important in terms of intellectual benefits for the researchers and in terms of innovative benefits for the firms. These findings seem to confirm the dual role of the univer-sities, versus a more focused one for the research in-stitutes and have turned out to be more optimistic than expected (Arza, pp 473–484, this issue).

Service channels (which include consulting) were found important in terms of both intellectual and economic benefits for university researchers and in terms of economic benefits for research institute re-searchers. One can speculate that services are impor-tant channels for university researchers because they can provide inputs for new research projects, scien-tific papers and graduate projects.

On the other hand, services have also turned out to be important for firms in terms of productive

benefits. Linked to the negative correlation between investment in internal R&D and productive benefits for the firms, this finding may indicate that the latter have limited expectations from interactions with both universities and research institutes, in spite of the great importance of the bi-directional knowledge flux. Meanwhile, commercial channels (including patents and licensing) seem to have low importance, either bringing no benefits or even losses to re-searchers (especially those from universities) and only leading to productive benefits for the firms.

These findings are relevant to the initial hypothe-sis and indicate that, in immature NSIs as in Brazil, and perhaps its Latin American neighbours, PRO-I interactions, when occurring at all, allow for some knowledge exchange between the parties involved and yield important intellectual and innovative bene-fits. Additionally, they seem to suggest that only re-cently have researchers begun to value such interactions, as shown by the positive association with younger researchers. The same may be happen-ing with the firms.

These results are not surprising considering Bra-zil’s late industrialisation with nearly 200 years of

importing technology consolidating a cultural pattern

of dispensing with R&D. This would explain why

there are so few interactive research groups in the

CNPq Directory and innovative firms in the Brazilian

economy, as noted by Albuquerque et al. (2008). But there are signs that some cultural transformations

regarding innovation are underway in the country. Nonetheless, the results must be interpreted in

light of a selection bias that excluded PROs and firms not engaged in PRO-I interactions, represent-ing only a partial picture of the phenomena. Yet the model proved sound as an instrument to study PRO-I interaction in Brazil. However, future investiga-tions should include multivariate explorations of the grouping of variables into more ‘natural’ clusters, plus additional variables that might contribute to sta-tistical models with greater predictive power, as well as a representative sample with stakeholders that in-teract and those who do not.

Regarding policy implications, the research results

suggest that policy-makers should consider, on the

one hand, the specificities of research institutes vis a

vis universities and, on the other, the negative correla-tion between investment in internal R&D and produc-tive benefits for the firms in order to enhance the

effectiveness of policies. One may say that somehow

benefits of public expenditure in innovation are not turning into innovation, as would have been expected, and this is particularly important for developing

countries where catching up measures to reduce the

time lag require efficient policy instruments. Since

disappointing results weaken innovation policies

within the fierce competition for the public budget, policy instruments must be more effective by target-ing the causes of this inefficiency. Results from other

contributions of our research project (Pinho et al., 2007; Ramalho and Fernandes, 2010) suggest that this



problem may result from either the fact that Brazilian firms may have been employing part of the

money provided by innovation policies for other ac-tivities for which they do not obtain credit (this is par-ticularly relevant in countries like Brazil where

interest rates are high), or the fact that, for multi-national firms, interaction with Brazilian PROs is not as relevant as the one carried out at their headquarters. Both of these are problems related to the particular

features of immature NISs which are still not targetted

by innovation policies in developing countries.

Acknowledgements

This research is part of an international research project titled ‘Inter-actions between universities and firms: searching for paths to sup-port the changing role of universities in the South’, developed under

the umbrella of the Catching up project. It was sponsored by the

International Development Research Centre (IDRC), Canada. The authors wish to thank the following organisations for

funding the surveys: CNPq (Interações de Universidades e Institutos de Pesquisa com Empresas no Brasil (Processo: 478994/2006-0)); IDRC (Interactions between universities and firms: searching for paths to support the changing role of uni-versities in Latin America); Fapesp (Interações de Universidades/ Instituições de Pesquisa com Empresas Industriais no Brasil (Processo 2006/58878-8)); and Fapemig (Oportunidades ao Desenvolvimento Sócio-Econômico e Desafios da Ciência, da Tecnologia e da Inovação em Minas Gerais (CEX-1735/07)).

Notes

1. The cases of Argentina (Arza and Vazquez, pp 499–511), Costa Rica (Orozco and Ruiz, pp 527–540) and Mexico (Dutrénit et al., pp 513–526) presented in this special issue share the same conceptual framework and methodology.

2. This research compares PRO-I interactions of 12 countries from Latin America, Asia and Africa. The questionnaires which were used were discussed between all the national teams.

3. CNPq (National Council of Technological and Scientific Devel-opment) has gathered and organised information related to re-search activities in order to produce the Directory (see <http://www.cnpq.br/gpesq/apresentacao.htm>). The latter in-cludes references from all the existing research groups in the country and lists several of their specific characteristics. The Directory’s definition of research group is a hierarchical set of researchers, students, and personnel with specific technical-scientific competencies that perform scientific research in a specialised field, sharing or not physical space and resources. By the time this paper was produced, six censuses were avail-able: 1993, 1995, 1997, 2000, 2002 and 2004. The first one yielded 99 research institutions and 4,402 groups. In 2004, the number of universities and public research institutes jumped to, respectively, 375 and 19,470.

4. ‘Find below a list of benefits derived from interacting with firms. Classify them in accordance with the degree of importance they have meant to your group’s research activities.’

5. Alternative answers were: 1 = not important; 2 = slightly impor-tant; 3 = moderately important; 4 = very important.

6. One firm did not answer this question. 7. ISIC stands for International Standard of Industrial Classifica-

tion of All Economic Activities (ISIC), developed by the UN as a standard way of classifying economic activities. The ISIC code brings together production units that produce the same type of goods or service or those that make use of similar processes.

8. As, in the Brazilian case, the forms were only sent to interact-ing researchers and firms, the Heckman (1979) corrections could not be estimated.

References

Albuquerque, E 1999. National systems of innovation and non-OECD countries: notes about a rudimentary and tentative ‘typology’. Brazilian Journal of Political Economy, 19(4), 35–52.

Albuquerque, E, W Suzigan, S Cário, A C Fernandes, W Shima, J Britto, A Barcelos and M Rapini 2008. An investigation on the contribution of universities and research institutes for maturing the Brazilian innovation system: preliminary results. Paper presented at the 6th Globelics 2008 Conference, held 22–24 September, Mexico City, Mexico.

Arvanitis, S, N Sydow and M Woerter 2008. Is there any impact of university–industry knowledge transfer on innovation and pro-ductivity? An empirical analysis based on Swiss firm data. Re-view of Industrial Organization, 32(2), 77–94.

Arza, V and C Vazquez 2010. Interactions between public re-search organisations and industry in Argentina: analysis of channels and benefits for researchers and firms? Science and Public Policy, 37(7), 499–511.

Bernardes, A and E Albuquerque 2003. Cross-over, thresholds and the interactions between science and technology: lessons for less-developed countries. Research Policy, 32(5), 867–887.

Bierly, P III, F Damanpour and M Santoro 2009. The application of external knowledge: organizational conditions for explora-tion and exploitation. Journal of Management Studies, 46(3), 481–509.

Cassiolato, J E, H M H Lastres and M L Maciel eds. 2003. Sys-tems of Innovation and Development. Evidence from Brazil. Cheltenham, UK: Edward Elgar.

Cohen, W, R Nelson and J Walsh 2002. Links and impacts: the influence of public R&D on industrial research. Management Science, 48(1), 1–23.

Dagnino, R and L M S Velho 1996. University–industry–government relations on the periphery: the University of Campinas. Minerva, 11(1), 229–251.

Dutrénit, G, C De Fuentes and A Torres 2010. Channels of inter-action between public research organisations and industry and benefits for both agents: evidence from Mexico. Science and Public Policy, 37(7), 513–526.

Fapesp (Fundação de Apoio a Pesquisa do Estado de São Paulo) 2010 (forthcoming). Indicadores de Ciência, Tecnologia e Inovação em São Paulo – 2008. São Paulo, Brazil: Fapesp.

Freeman, C 1988. Japan: a new national system of innovation? In Technical Change and Economic Theory, G Dosi, C Freeman, R Nelson, G Silverberg and L Soete eds., pp 330–348. London: Pinter.

Furtado, C 1986. Teoria e Política do Desenvolvimento Econômico, 2nd edn. São Paulo, Brazil: Nova Cultural.

Heckman, J 1979. Dummy endogenous variables in a simultane-ous equation system. Econometrica, 47(1), 153–161.

Klevorick, A, R Levin, R Nelson and S Winter 1995. On the sources and significance of inter-industry differences in tech-nological opportunities. Research Policy, 24(2), 185–205.

Mazzoleni, R and R Nelson 2007. The roles of research at univer-sities and public labs in economic catch-up. Research Policy, 36(10), 1512–1528.

Meyer-Krahmer, F and U Schmoch 1998. Science-based tech-nologies: industry–university interactions in four fields. Re-search Policy 27(8), 835–851.

Mokyr, J 1990. The Lever of Riches: Technological Creativity and Economic Progress. Oxford, UK: Oxford University Press.

Mowery, D and B Sampat 2005. Universities in national innova-tion systems. In The Oxford Handbook of Innovation, J Far-gerberg, D Mowery and R Nelson eds. pp 209–239. Oxford, UK: Oxford University Press.

Narin, F, K S Hamilton and D Olivastro 1997. The increasing link-age between US technology and public science. Research Policy 26(3), 317–330.

Nelson, R ed. 1993. National Innovation Systems: A Comparative Analysis. New York, Oxford: Oxford University Press.

Pavitt, K 1991. What makes basic research economically useful? Research Policy, 20(2), 109–119.

Orozco, J and K Ruiz 2010. Quality of interactions between public research organization and firms: lessons from Costa Rican case. Science and Public Policy, 37(7), 527–540.

Pinho, M S, A C Fernandes and M R Côrtes 2007. Inovação e Inserção de Mercado nas Empresas de Base Tecnológica Brasileiras. In Proceedings of the XII Seminário Latino-



Iberoamericano de Gestión Tecnológica, held 26–28 Sept-ember, Buenos Aires. Website: www.oei.es/ctsieventos.htm>.

Ramalho, F S and A C Fernandes 2009. Efeitos Locais de Políti-cas Públicas Federais: Observações a partir da Lei de Infor-mática no Desenvolvimento do Setor de Software de Campina Grande, PB. Revista Brasileira de Estudos Urbanos e Re-gionais, 11(1), 105–125.

Rapini, M S 2007. Interação Universidade-Empresa no Brasil: evidências do Diretório dos Grupos de Pesquisa no Brasil. Estudos Econômicos, 37(2), 212–233.

Rapini, M, E Albuquerque, C Chaves, L Silva, S Souza, H Righi and W Cruz 2009. University–industry interactions in an imma-ture system of innovation: evidence from Minas Gerais, Brazil. Science and Public Policy, 36(5), 373–386.

Ribeiro, L C, E Albuquerque, L M Franco and I A Moura 2009. The scientific and technological trajectories of four Latin American countries: Mexico, Costa Rica, Argentina and Brazil (draft paper). Belo Horizonte, Brazil: Cedeplar, UFMG.

Rosenberg, N 1982. Inside the Black Box: Technology and Eco-nomics. Cambridge, UK: Cambridge University Press.

Rosenberg, N 1990. Why do firms do basic research (with their

money)? Research Policy, 19(2), 165–174. Rosenberg, N and R Nelson 1994. American universities and

technical advance in industry. Research Policy, 23(3), 323–348.

Science and Development Network 2009. Latin American S&T investment shows major growth. Available at <http://www. scidev.net/en/news/latin-american-s-t-investment-shows-major-growth.html>, last accessed 19 December 2009.

Suzigan, W and E Albuquerque 2009. The underestimated role of universities for development: notes on historical roots of Brazil-ian system of innovation. Paper presented at XV World Eco-nomic History Congress, held 3–7 August, Utrecht, The Netherlands.

Suzigan, W, M S Rapini and E Albuquerque 2009. A changing role for universities in the periphery: notes about a tri-continental research project (draft paper). Belo Horizonte, Bra-zil: Cedeplar, UFMG.

Welch, R, L Glenna, W Lacy and D Biscotti 2008. Close enough but not too far: assessing the effects of university–industry re-search relationships and the rise of academic capitalism. Re-search Policy, 37(10), 1854–1864.

Documents

Academy–industry links in Brazil: evidence about channels and benefits for firms and researchers