Do linkages between farmers and academic researchers influence researcher productivity? The Mexican case

Research Policy 40 (2011) 932–942

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Do linkages between farmers and academic researchers influence researcher productivity? The Mexican case夽 René Rivera-Huerta a , Gabriela Dutrénit a,∗ , Javier Mario Ekboir b , José Luis Sampedro c , Alexandre O. Vera-Cruz a a Postgraduate program in Economics and Management of Innovation at Metropolitan Autonomous University, Campus Xochimilco, Calzada del Hueso 1100, Col. Villa Quietud, Coyoacán, Mexico City, CP. 04968, Mexico b Institutional Learning and Change Initiative (ILAC), Via dei Tre Denari 472/a 00057 Maccarese (Fiumicino), Rome, Italy c Department of Institutional Studies, Metropolitan Autonomous University, Campus Cuajimalpa, Pino 33-A, Lomas Quebradas, La Magdalena Contreras, Mexico City, CP 10000, Mexico

a r t i c l e

i n f o

Article history: Received 22 December 2009 Received in revised form 2 March 2011 Accepted 2 May 2011 Available online 8 June 2011 Keywords: University-farmer interactions Research productivity Agricultural sector

a b s t r a c t A growing body of literature is focusing on how the collaborations researchers engage in affect their productivity. Most authors have focused on linkages among academic researchers, measuring productivity by the number of papers published in ISI journals. In contrast, the impact of interactions between academic researchers and the business sector on research productivity has been less analyzed. The aim of this paper is to analyze how broadly defined research productivity (papers, new recommendations and new techniques) in agriculture-related fields is affected by the nature of academy-farmers interactions. This latter was approached through two dimensions: the breadth of linkages and their intensity, measured by the duration, in two different modalities (R&D activities and consultancy). Based on original micro data obtained through a survey of researchers working in universities and PRCs in agriculture-related fields, we built three models, one per output, to identify the effect of the nature of interactions on research productivity. The models were estimated with a negative binomial distribution using Maximum Likelihood estimators. We found a positive relationship between interaction with farmers and publishing of papers when interactions are carried out through the R&D modality. We also found that the impact of the nature of interactions on research productivity differs according to the type of research output. The impact is broader in the case of new recommendations than in the other outputs. The production of new recommendations is positively influenced by both the breadth of linkages and their duration through both modalities of interaction (R&D and consultancy). © 2011 Elsevier B.V. All rights reserved.

1. Introduction In the last three decades, universities and public research centers (PRCs) have been urged to generate knowledge that could have direct economic or social impacts; this trend has been particularly strong in some developing countries, where policy makers have explicitly demanded their contribution to development (Arocena and Sutz, 2001). It has also been argued that

夽 This paper is part of a research project “Mejorando la Administración del Conocimiento en el Sistema de Innovación Agropecuario Mediante el Fortalecimiento de las Capacidades de las Fundaciones Produce, el SNITT e Institutos de Investigación” carried out in UAM and funded by SAGARPA-CONACYT (Project num. 2006-C01-48511). ∗ Corresponding author. Tel.: +52 55 5483 7100; fax: +52 55 5483 7235. E-mail addresses: [email protected] (R. Rivera-Huerta), [email protected] (G. Dutrénit), [email protected] (J.M. Ekboir), [email protected] (J.L. Sampedro), [email protected] (A.O. Vera-Cruz). 0048-7333/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.respol.2011.05.001

linking researchers from universities, we would further add from PRCs, with the business sector would foster the emergence of new ideas that could result in innovations with commercial or social value. This argument was supported by the experiences of Silicon Valley in California and Route 128 in Boston (Florida, 1999). Governments incited this more applied role for universities and PRCs by implementing different types of programs to foster closer linkages with the business sector (e.g. contract research, consultant relationships, technological transfer and jointventures) (Mowery et al., 1996; Vinding, 2004; Patel and Calvert, 2003). This new role, however, has not been accepted by the whole scientific community (Florida, 1999; Hicks and Hamilton, 1999). In fact, critics contend that growing ties with the private sector could distort the original purpose of universities and PRCs, particularly the former, if their researchers become more concerned with sponsored research, licensing their technology and creating spin-off companies to raise money, than with conducting basic research and preparing highly trained professionals. The argument

R. Rivera-Huerta et al. / Research Policy 40 (2011) 932–942

concludes that in the long run, stronger linkages could be more costly than beneficial (Florida, 1999). A growing body of literature is focusing on how the collaborations researchers from public research organizations engage in affect their productivity. Most authors have focused on linkages among academic researchers, measuring productivity by the number of papers published in ISI journals.1 They found that academic collaborations increased research productivity (Bozeman and Lee, 2003; Crane, 1972; Defazioa et al., 2009; Zuckerman, 1967; Perkmann and Walsh, 2009). In contrast, the impact on research productivity of collaboration/interaction between academic researchers and the business sector has been less explored. In this line, Perkmann and Walsh (2009) report that closer interactions with non-academic partners allow researchers to explore new ideas and projects, which could have impact on research productivity. Czarnitzki et al. (2009) found that such collaborations impact negatively on research productivity, measured by papers. They assert that co-patenting with industry shows weak negative effects on publication counts, but stronger negative effects on publication quality measured through both weighted journal publications and the number of forward citations. On one hand, the focus on scientific papers to analyze research productivity is narrow. The essence of a researcher working in public research organizations need not be to publish papers per se but to produce and communicate knowledge through different mechanisms. Researchers generate a variety of research products, some of them largely aimed at the scientific community and others seeking to address social or economic issues (Perkmann and Walsh, 2009). This later adopts the form of different outputs, which are particularly important in the agricultural sector. For instance, our data shows that researchers in this sector produce three main types of outputs: 22.9% produce only papers, 23.7% only new recommendations and techniques, and 65.6% more than one output, in which the 53.4% produce papers and other outputs. Hence, an analysis of the papers does not allow for a broader measure of research productivity. On the other hand, the relationship between research productivity and the nature of interactions with non-academic partners has been barely studied. This paper contributes to the debate by exploring how broadly defined research productivity in agriculture-related fields is affected by the nature of interactions between academic researchers and farmers.2 This paper is based on original micro data obtained through a survey of researchers working in universities and PRCs in agriculture-related fields in 2008.3 We built three models, one per output, to identify the effect of the nature of interactions on research productivity. The models were estimated with a negative binomial distribution using Maximum Likelihood estimators. Following to this introduction, the paper has five sections. Section 2 reviews the particularities of agricultural research. Section 3 reviews literature on academy-business sector linkages, research collaboration and research productivity. Section 4 describes the data and main variables. Section 5 presents the methodology and

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discusses the main findings. Section 6 contains the final reflections and policy implications. 2. Particularities of agricultural research in Mexico Patterns of research and innovation vary across sectors (Malerba, 2005; Pavitt, 1984). A great deal of variation also characterizes agricultural research, which includes disciplines such as chemistry, soil sciences, engineering, plant breeding, biotechnology, entomology, agronomy, veterinary sciences, animal production, ecology, and fisheries and forest management. Social sciences also have agriculture-related subdisciplines. Agricultural research products that are available to developing countries’ farmers are generated in four types of institutions: private firms, international research institutes, universities and PRCs from developed countries, and domestic universities and PRCs institutions. In general, private firms conduct research on products that can be commercialized, including agrochemicals, equipment and improved seeds of certain crops. The most important international research institutes belong to the Consultative Group on International Agricultural Research (CGIAR).4 These institutes are mandated to generate research results that can contribute to poverty alleviation in developing countries and, in general, they make those results freely available. Their activities include researching on a large number of staples and high value crops, livestock, fisheries, market development, irrigation management, forestry and sustainable use of natural resources (Ekboir, 2009). Several of these institutes actively collaborate with Mexican universities and PRCs. The research of universities and PRCs in developed countries focuses on a large variety of topics covering commodities and high value crops, often in partnership with private firms (Fuglie et al., 1996). Most of their research results are available to Mexican researchers and farmers, sometimes for a fee and other times for free. In the case of Mexico, agricultural research is conducted in three types of institutions: ‘general’ universities and PRCs, sectoral universities and PRCs, and other mostly local organizations such as technological universities and institutes that also research non-agricultural topics or conduct other types of activities (e.g. extension). The first group is conformed by large federal universities and PRCs; they have a well diversified research portfolio that includes several disciplines such as chemistry, physics, medicine, biology, social sciences and humanities. Usually, their research related to agriculture covers science-intensive topics such as biotechnology and biology and is largely oriented towards fundamental questions.5 Sectoral universities and PRCs work only on topics closely related to agricultural production, such as agronomy, crop rotations and plant breeding, but they do little work on post-harvest and transformation of agricultural products.6 Additionally, these institutions specialize mainly on certain traditional products (e.g. cereals, oilseeds, cattle, hogs, and milk), which are traded in relatively known and stable markets (Ekboir et al., 2003). Finally, the other local organizations have small research programs or may have a diversified research portfolio; their activities related to agriculture deal only with post-harvest issues and/or

1

Institute of Scientific Information. The term agriculture is used in a generic sense and includes, in addition to crop and plant research, studies of livestock, aquaculture, forestry, and other scientific disciplines (e.g. biology, biotechnology and physics) that generate research outputs that can be used by farmers or other stakeholders related to the agricultural sector. 3 In this paper we refer almost indistinguishable to researchers working either in universities or in PRCs. We are aware that these institutions may differ in relation to their role in the national innovation system and the knowledge production process, among others characteristics. However, in the Mexican case, researchers of both institutions receive a set of common incentives that contribute to explaining how they tend to interact. In this sense, we generically refer to these researchers as academic researchers. 2

4 The CGIAR is a coalition of about 60 international donors and governments that support 15 international agricultural research centers. 5 The largest ‘general institutions’ are the Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV), Universidad Nacional Autónoma de México (UNAM) and Universidad Autónoma Metropolitana (UAM). 6 The most important sectoral PRC is the National Institute for Forestry, Agricultural and Livestock Research (INIFAP); other relevant sectoral universities are the Colegio de Posgraduados, Universidad Autónoma de Chapingo and Universidad Antonio Narro.

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processing of agricultural products. The research of these last two institutions can be characterized as problem-solving oriented research.7 Two agencies fund a great deal of the agricultural research in Mexico: CONACYT and the produce foundations (PF). CONACYT is the national science and technology council. The PF are farmermanaged foundations that administer public resources to fund agricultural research, extension and innovation projects.8 Both agencies fund short to mid-term projects (Ekboir et al., 2009); the main difference is that CONACYT funds “more fundamental questions” while the PF fund “more applied” projects. An important incentive that affects the focus of the research between fundamental questions and problem-solving oriented ones comes from the National Researchers System. This program supports the formation, development and consolidation of a critical mass of high-level researchers, mainly inside the public system. It gives researchers pecuniary and non-pecuniary (status and recognition) compensations based on the quantity and quality of their research, measured mainly by academic publications. Researchers from both universities and PRCs may receive this incentive. 3. Academy-business sector linkages and research productivity The productivity of academic researchers has been found to depend on institutional and organizational factors, including communication patterns, the degree of freedom to define personal research agendas, the recognition of the department in which the researchers work, the access to human resources, costly instruments and unique materials, institutional mobility, the size of research teams and the recruiting policies of academic departments (Hicks and Hamilton, 1999; Bozeman and Lee, 2003; Heinze et al., 2009). Ben-David (1960) argued in favor of other factors, such as the diverse degrees of competitiveness of the research environments, the academic system of the countries, the creation of jobs and facilities specialized in scientific research, and the introduction of large-scale systematic training. Henderson and Cockburn (1996) found that size (large research efforts) and scope (diversified programs) influence research productivity. Other factors that have been found to influence the productivity of researchers are age, sex, rank and status. In this line, Bozeman and Lee (2003) argued that older scientists, or at least those who had longer careers, had more time to develop their ‘scientific and technical human capital’ and to build up their professional networks; therefore, it was not possible to distinguish the effect of age from career length when analyzing the impact of collaborations on productivity. Levin and Stephan (1991) reported the presence of life-cycle effects, concluding that the expectation that ‘the latest educated are the most productive’ is not generally supported by the data. Simonton (2003) and Weisberg (2003) noticed that researchers’ productivity peaked about 10 years after they obtained their doctoral degree, generally around their 30s or 40s, and that in the last decades of their scientific life, their productivity was about half the rate observed in their peak years. González-Brambila and Veloso (2007) found that age does not have a substantial impact on the output of Mexican researchers. Collaboration among different types of agents is often viewed as a positive factor for knowledge creation and problem-solving (Heinze et al., 2009). The literature about the effects of collaboration

7 For the econometric analysis, in Section 5, this paper classifies these institutions in four types: general universities, general PRCs, sectoral universities and PRCs, and other local institutions. 8 The PF are coordinated by COFUPRO, a national organization, who also manages some regional funds.

on research productivity has focused on collaborations among academic researchers. However, despite the many reasons to expect collaborations to increase research productivity, this link is not obvious (Bozeman and Lee, 2003; Lee and Bozeman, 2005). Collaborations between universities and firms have been analyzed both from the perspectives of both agents, at individual and organizational levels, but, as argued by Bozeman and Lee (2003), the empirical evidence, in particular that related to collaboration patterns and publishing productivity, is scant. The impact of academic collaboration and academy-business sector collaboration differs. In this direction, Rijnsoever et al. (2008) found that while collaboration among researchers is related to the development of academic careers, science–industry collaborations are not. According to these authors, all levels of network activity within the scientific community are positively related to each other; particularly academic rank and networking activity are strongly related, but non-academic interactions show no relationship with academic rank. Some scholars reported that the productivity of researchers who establish or join firms, measured by the quantity and quality of their publications, might increase (Zucker and Darby, 2007; Lowe and González-Brambila, 2007). Czarnitzki and Toole (2009) reported that the production of scientific papers increased when researchers returned to the university after they spent time in the private sector, in relation to those without this characteristic. In contrast, other studies argued that university researchers who created or joined firms reduced their research productivity (Stern, 2004; Toole and Czarnitzki, 2009; Goldfarb, 2008). Czarnitzki and Toole (2009) differentiate between the effect of these collaborations on publications count and quality, being more negative on the latter. Perkmann and Walsh (2009) argued that while collaborations with non-academic partners may not result in direct academic benefits (e.g. journal publications), they often yielded indirect benefits that could, in turn, enhance the academic productivity of researchers. These benefits resulted from being exposed to a broader set of ideas and problems than those encountered while conducting only curiosity-driven research. The literature on academy-business sector linkages has broadly recognized that researchers have motivations to interact and that they obtain a set of benefits from interactions. Benefits differ according to the modalities of interaction, as some of them include knowledge flows in both directions (e.g. joint/collaborative R&D) while others imply a unilateral provision of intellectual resources from researchers to the business sector (e.g. consultancy, training) (Arza, 2010). In this line, Perkmann and Walsh (2009) found that if the interactions assume the modality of joint R&D, it often results in academic publications, which are of particular interest for researchers, while other modalities of collaboration with more practical objectives, such as contract research and consultancy, lead to publications only if researchers make efforts to exploit collaboration with research purposes. Motivations and perceived benefits are associated with acquiring additional funding for the laboratories and exchanging knowledge (Meyer-Krahmer and Schmoch, 1998), securing funds for research assistants and laboratory equipment, gaining insights for their own academic research, testing applications of a theory and supplementing funds for their own academic research (Lee, 2000), acquiring a new perspective to approach the productive sector’s problems and the possibility to shape the knowledge that is being produced by the academy. Dutrénit et al. (2010) found that Mexican researchers value the most bi-directional modalities of interaction (joint and contract research), which provide intellectual benefits such as obtaining inspiration for future scientific research. In the case of the agricultural-biotechnology sector, Welsh et al. (2008) found that the increase of contacts between researchers and farmers is perceived as an important benefit.

R. Rivera-Huerta et al. / Research Policy 40 (2011) 932–942

This paper draws on these streams of literature to analyze the impact of academy-business sector interactions on research productivity in agriculture-related fields. The nature of interactions that researchers of universities and PRCs establish with farmers is explored, including number of linkages, modalities of interactions and duration of the interaction. Research productivity is broadly defined to include different outputs.

4. Data and variables 4.1. Main features of the database This paper is based on original survey data collected from Mexican researchers at the end of 2008. The data was obtained through a survey of two partially overlapping groups of researchers who work in public universities and PRCs on topics related to agriculture. The first and main group is integrated by researchers who in the last decade have received at least one grant from COFUPRO or the PF to develop research oriented towards solving specific problems of the agricultural sector.9 The second group is composed by researchers in agricultural sciences belonging to the National Researchers System. Even though this group is small, it could be used as a kind of control for those with specific incentives to publish and whose research is not necessarily oriented towards problems. Overall, our dataset contains 262 observations. The survey was carried out through a phone questionnaire. Among other questions, the researchers were asked to identify how many of the three main types of outputs (papers published on ISI journals, new recommendations and new techniques) they produced in the three years previous to the survey; they were also asked to identify different modalities of interactions with farmers. Table 1 describes the main characteristics of the sample. 61.1% of researchers have a Ph.D., 32.4 a master and 6.5 only a bachelor/engineering degree, reflecting the hiring practices of Mexican agricultural universities and PRCs as well as of general and local institutions. Only one quarter of the sample belongs to the National Researchers System (23.6%). This percentage is lower than in the main general institutions without distinguishing by discipline (i.e. 34.0 in IPN, 33.6 in UAM, 97.3 in Cinvestav). 93.5% of the sample has received research funds from COFUPRO or the PF, showing that they develop problem-oriented research. The research activities they develop are largely applied in agricultural sciences, as 79.4% of the researchers describe their research as oriented towards problem-solving in those fields and 15.3% as basic problems; 5.3% pertain to social sciences arenas applied to the agricultural sector. Taking into account the type of institution where they are affiliated, half of the sample belongs to sectoral universities and PRCs (INIFAP, Colegio de Posgraduados and Universidad de Chapingo), which work mostly on agricultural production issues. 34.7% of the researchers belong to general universities (UNAM, UAM and CINVESTAV), which develop research in diverse areas of knowledge; the agriculture-related research carried out by these institutions includes science-intensive areas such as biotechnology and biology. A small group of researchers (6.9%) works in general PRCs, which develop research in various fields of knowledge and also agricultural research. This type of institution has a diversified research portfolio, but their research related to agriculture activities are associated only with post-harvest and processing of agricultural products. Finally, 7.6% of the researchers belong to other local institutions not covered fully in any of the above classifications.

9

The rate of answer for this first and main group was 46%.

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Table 1 Researchers’ characteristics. Concept Ph.D. Master Bachelor/engineering Memberships NRS Yes Funding by COFUPRO and PF Yes Types of research Basic agricultural science Applied agricultural science Social sciences applied to the agriculture sector Sectoral Universities Type of institution and PRC General universities General PRC Other institutions Linkages 1–9 farmers 10 and 39 farmers 40 or more farmers No collaboration Papers and others Research outputs New recommendations and others New techniques and others Only papers Only new recommendation Only new techniques Papers, new recommendations and new techniques Degree

a

Researchers Percenta 160 85 17 62 245 40

61.1 32.4 6.5 23.7 93.5 15.3

208

79.4

14

5.3

133

50.8

91 18 20 150 47 47 18 200 155

34.7 6.9 7.6 57.3 17.9 17.9 6.9 76.3 59.1

137

52.3

60 19

22.9 7.3

11 68

4.2 26.0

All percents calculated on the total researchers: 262.

Referring to linkages with farmers, more than half of the researchers interact with a small number of farmers (between 1 and 9), 17.9% interact with a medium number (10–39), and the same percentage interacts with a large number (40 or more). Finally 6.9% of respondents have chosen a more academic career path and do not have any collaboration. Concerning to the outputs, researchers produce different types of outputs and their importance differs. The principal research outputs in the three years previous to the survey were scientific papers, new recommendations and new techniques. If we consider the production of one output regardless of whether the researchers produce another one, 76.3% produce papers, 59.1 new recommendations and 52.3 new techniques. The greater importance of the papers is consistent with both the scheme of incentives generated by the National Researchers System and the specific institutional incentives, which consider the number of published papers as one of the main indicators of research productivity, affecting the researcher’s income. Considering exclusively the production of one research output, 22.9% published only papers, 7.3 only new recommendation and 4.2 only new techniques. Only 26.0% of the researchers produce the three types of outputs. Therefore, given the heterogeneity of researchers’ production, publishing is a narrow approach to proxy research productivity, at least in this sector.

4.2. The variables Table 2 contains the definitions and the descriptive statistics of the dependent, main independent and control variables used in the econometric model to test how the productivity of agricultural researchers is influenced by the nature of the interactions with farmers. The variables are explained below.

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Table 2 Definition of variables and main descriptive statistics. Variable

Description

publishing new recommendations

Number of papers published in the three years previous to the survey Number of new recommendations produced in the three years previous to the survey Number of new techniques produced in the three years previous to the survey Number of years between obtaining the highest academic degree and the year of the survey The square of time degree Dummy variable with value 1 if the researcher belongs to a general university Dummy variable with value 1 if the researcher belongs to a general PRC Dummy variable with value 1 if the researcher belongs to a sectoral PRC or university Dummy variable with value 1 if the researcher belongs to other institutions The number of researchers that participate in the research team, regardless of their academic degree The square of research team Dummy variable that takes value 1 if the researcher hold a PhD degree Dummy variable that takes value 1 if the researcher belongs to NRS Dummy variable that takes value 1 if the researcher perceive grants from PF and do not belong to NRS Dummy variable with value 1 when the researcher conducts basic research Dummy variable with value 1 when the researcher conducts applied research Dummy variable with value 1 when the researcher conducts social science research Dummy variable taking value 1 if researchers do not have any year of interaction through R&D modality Dummy variable taking value 1 if researchers have a time of interaction through R&D modality greater than zero but lower than the median Dummy variable taking value 1 if researchers have a time of interaction through R&D modality superior to the median. Dummy variable taking value 1 if researchers have a time of interaction through consultancy modality lower than the median Dummy variable taking value 1 if researchers have a time of interaction through R&D modality superior to the median. A dummy variable with value 1 if the researcher reports no linkages with farmers and 0 otherwise A dummy variable with value 1 if the researcher reports between 1 and 9 interactions with farmers. Dummy variable with value 1 if the researcher reports an intermediate number of linkages (10–39) A dummy variable with value 1 if the researcher reports a large number of linkages (40 or more).

new techniques time degree sqr time inst 1 inst 2 inst 3 inst 4 research team sqr research phd degree NRS PF basic science applied science social science time rd1 time rd2 time rd3 time consult1 time consult2 link 0 link 1 link 2 link 3

4.2.1. Dependent variables Our dependent variable is research productivity, measured by different products generated during the period of 2006–2008. Researchers in agriculture related fields generate different outputs, which, in the case of our study, were grouped into three categories: papers in scientific journals, new recommendations, and new techniques. • Papers in scientific journals per researcher (publishing): This variable measures the number of papers published per researcher in the analyzed period. • New recommendations per researcher (new recommendations): This variable measures the number of new recommendations per researcher in the analyzed period. This product includes novel ways of using known inputs or crops, such as new ways to apply fertilizer. • New techniques by researcher (new techniques): This variable measures the number of new techniques introduced by a researcher in the analyzed period. This product includes new inputs, new equipment or substantially new ways of using known inputs, such as no-till practices.10

10 No-till is an agricultural technique in which seeds are planted in undisturbed soil. It is a complete agronomic package that requires adapted techniques for all stages of the plants’ cycle: soil preparation, planting, pest and weed management, crop rotations, fertilization, and harvesting. In traditional planting, on the other

Mean

Median

S.D.

Min

Max

3.69 1.65

3 1

3.85 2.21

0 0

16 12

1.26 11.98

1 10

1.91 8.35

0 1

12 52

212.93 0.35 0.07 0.51

100 0 0 1

302.38 0.48 0.25 0.50

1 0 0 0

2704 1 1 1

0.08 6.32

0 0

0.27 11.50

0 0

1 86

171.74 0.61 0.24 0.94

0 1 0 1

708.57 0.49 0.43 0.25

0 0 0 0

7396 1 1 1

0.15 0.79 0.05

0 1 0

0.36 0.41 0.23

0 0 0

1 1 1

0.36

0

0.48

0

1

0.20

0

0.40

0

1

0.44

0

0.50

0

1

0.58

1

0.50

0

1

0.42

0

0.50

0

1

0.07

0

0.25

0

1

0.57

1

0.50

0

1

0.18

0

0.38

0

1

0.18

0

0.38

0

1

In all cases, we analyzed the number of research outputs but not their quality or relevance.11 4.2.2. Main independent variables The main goal of the present work is to measure the impact of researchers-farmers linkages on research productivity, where the former determine the latter. It is difficult to measure the nature of these linkages, as it is a complex activity; hence this paper approaches the nature of linkages through two variables. • Breadth of linkages. This variable is proxyed through the number of linkages with farmers reported by each researcher and is composed by a set of dummy variables to identify whether the number of linkages was nil, low, medium or high. In this way, link 0 identifies those researchers with zero linkages; and so on.12 In the econometric models link 1 is taken as the omitted category. • Intensity of linkages. This variable measures the interaction time between researchers and farmers through two different modalities of linkages: (i) Interaction through R&D, which is usu-

hand, the soil is reduced to a fine powder through intensive plowing and harrowing before planting. 11 There are other outputs like new plant varieties (seeds or plants developed to express a particular property, such as higher yields or resistance to a disease), which were not included in this analysis. 12 This classification arises from the way the observations of the variable were grouped in scatter plot.

R. Rivera-Huerta et al. / Research Policy 40 (2011) 932–942

ally motivated by long-term targets of knowledge creation by researchers and its partners from the business sector; it allows knowledge flows in both directions. This variable is measured through the following groups of indicators: time rd1 is a group of researchers that has not interacted or has interacted less than a year with farmers; time rd2 includes those researchers that have a number of years of interaction equal o lower than the median; and finally, time rd3 comprises those researchers with an interaction duration greater than the median. (ii) Interaction through consultancy, which is associated with the provision of scientific and technological services in exchange for money; knowledge flows mainly from Universities and PRCs to the business sector, in this case the farmers. This variable is constructed in a similar way to the previous modality, but in this case, only two categories are recognized: time consult1 is a group of researchers with no year of interaction through consultancy and time consult2 groups those researchers that have a time of interaction superior to the median. In the econometric models time rd1 and time consult1 are taken as the omitted category. The relationship between the nature of linkages with farmers and the production of each type of product may differ. In order to write a paper, researchers mostly need to conduct experiments or collect information. They could do this without interacting with farmers. There are different perceptions about the impact of strong interactions on research productivity. For some authors, strong interactions may help researchers to focus their research, experiment with alternative approaches and identify new research questions (Perkmann and Walsh, 2009). In contrast, a common belief among agricultural researchers is that stronger interactions with farmers hamper the researchers’ ability to publish; this belief is also shared by several authors analyzing other knowledge fields (Florida, 1999; Hicks and Hamilton, 1999). Therefore, following this common belief, we expect a negative correlation between breadth of linkages and productivity measured by ISI papers. The literature refers to differences in researchers’ benefits according to the modality of linkages (Perkmann and Walsh, 2009; Arza, 2010), thus we expect a positive correlation between interaction time through R&D and productivity measured by papers, and a negative one in the case of interaction time through consultancy. In contrast, to develop a recommendation or a new technique, researchers need to understand the complex production processes into which they hope the knowledge will be integrated. They can gain this understanding following different paths: the researchers can be farmers themselves, they can interact with farmers or, if the production process is relatively stable and well known (such as planting cereals and oilseeds), they can read printed materials or talk to other researchers. But the odds that the recommendations or the techniques will be adopted increase when researchers develop them under farmers’ production conditions, in other words, interacting actively with farmers. Therefore, we expect a positive correlation between breadth of linkages with farmers and issuance of new recommendations and techniques. In these outputs, both modalities of linkages may contribute to their production; hence we expect a positive relationship between interaction time through R&D and consultancy and the production of these technical outputs.

4.2.3. Control variables Our estimations use several control variables to avoid unobserved heterogeneity of the sample. • Experience (time degree). This variable is measured as number of years between the achievement of the highest academic degree by researchers and the year of the survey. As in mincerian human

•

•

•

•

•

•

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capital models, we also consider the quadratic expression of experience (sqr time).13 Academic collaboration (research team). This variable is measured as the number of members that are part of the research team in which the researcher is involved. In addition, to take into account a potential concave relationship between research productivity and the size of the research team, we also included the square of research team (sqr research). Last academic degree. Most studies of research productivity have focused only on researchers with doctoral degrees. However, in Mexico, the academic degree of the researchers’ population is not homogenous since the practices of universities and PRCs include hiring researchers with other academic levels (master and first college degree). To control such heterogeneity we use a dummy variable with value 1 if the individual holds a PhD degree and 0 otherwise (phd degree). Type of institutions. Four types of institutions are classified (see a more complete explanation in Section 2): inst 1 is a group of general universities; inst 2 is a group of general PRCs; inst 3 includes sectoral universities and PRCs; and finally inst 4 includes other local institutions. We have created a set of dummy variables to identify if a researcher belongs to each type of institutions. In the econometric models inst 1 is taken as the omitted category. Incentives for researcher performance (NRS). We use a dummy variable to identify individuals who belong to the National Researchers System. Researchers funded by COFUPRO and PF (PF). Our database is formed by researchers from two overlapping groups, members of the NRS and researchers funded by COFUPRO and PF. Hence, to observe if there are significant differences between the output productivity of these two groups, we have introduced a dummy variable to identify those researchers that only receive grants from PF and do not belong to NRS. Type of research. This is a relatively arbitrary classification to identify the research characteristics of individuals in our sample and is based on a combination of the knowledge area and the research goals of researchers. Our study recognizes three types of research, each of them identified by a dummy variable: (i) basic agricultural science (basic science); studies oriented towards the analysis, from different scientific perspectives, of the agrarian process or topics related to them but without any direct technical application; (ii) applied agricultural science (applied science); the objective of researchers in this sub-group is to develop an applied product and, therefore, their research could be characterized as problem-solving oriented. (iii) Social sciences applied to the agriculture sector (social science); researchers in this category are oriented towards the characterization and understanding of socio-economic problems that impact on productivity and on farmers’ welfare. In this variable applied science was taken as the omitted category.

4.3. Correlation among variables Since a high percentage of researchers in our sample produce more than one output, it would be interesting to analyze the correlation among the different dependant variables used in this analysis (e.g. publishing, new recommendations and new techniques). Table 3 reports standard pairwise correlations of our dependent variables, showing a negative even if not statistically significant relationship between academic outputs (publishing) and the more technical-

13 Most studies of research productivity have focused on researchers with doctoral degrees, and some have found that research productivity (measured by publications) increased over time, reaching a peak between 8 and 10 years after the researcher finished his/her studies (Weisberg, 2003; González-Brambila and Veloso, 2007).

1 −0.2186* 1 −0.1270* −0.1270* 1 −0.2329* 0.0823 0.0219 1 0.2222* −0.2402* 0.0475 0.0074 1 −0.4401* −0.0393 −0.1352* −0.0581 −0.0082 1 −0.0331 −0.0392 0.071 0.0697 −0.0669 0.0216 1 −0.1009 0.0814 −0.0333 −0.0633 −0.0314 −0.0602 −0.0878 1 −0.1036 0.0626 −0.0243 0.1393* 0.0063 −0.1122 0.0424 0.1232* 1 −0.4731* 0.1382* −0.1323* 0.0382 −0.0039 −0.0594 0.0618 −0.0029 −0.1901* 1 0.4261* −0.2103* 0.1648* 0.0157 0.1422* −0.0825 0.0351 0.0621 −0.1367* −0.1571* 1 0.1493* −0.026 −0.0307 0.0158 −0.0129 −0.0326 0.1138 −0.0087 −0.023 −0.0672 0 1 0.8792* 0.2497* 0.0378 −0.1601* 0.0474 0.0097 −0.0004 0.0526 0.0147 0.0096 −0.0857 −0.1021 1 −0.0793 −0.0591 −0.0358 −0.0924 0.0174 −0.0021 −0.0044 0.0011 −0.1094 0.0735 0.0924 0.0154 −0.022

Significant at 5% or better. *

8 7 6 5 4 3

Table 4 Correlation between independent variables.

We estimated separate ‘productivity’ functions for each of the three outputs on which information was collected. As mentioned above, the dependent variables are the number of papers published in ISI journals, new recommendations issued and new techniques developed in the three years prior to the survey. Therefore, we have count data that requires the use of different econometric techniques from those based on standard Gaussian linear regression models. Problems arising when applying OLS regression to count data are well known (Cohen et al., 2003). The residuals are not normally distributed; therefore inferences about individual predictors and overall prediction may well be biased when OLS regression is applied. Predicted scores can be below of zero. Moreover, the regression coefficient from OLS regression applied to count data may be biased and inconsistent (Long, 1992). The present model was estimated with a negative binomial distribution using Maximum Likelihood estimators. We chose this distribution over a Poisson function because the latter imposes a constant variance, and, as proved by a likelihood ratio test for over-dispersion, the variance of our outputs far exceeds the mean (Encaoua et al., 2006; Lanjouw and Schankerman, 1999). Table 5 shows the results for five different specifications estimated for each output mentioned above (i.e. publishing, new recommendations and new techniques). Columns 1, 6 and 11 report the results for our preferred model (baseline specification), which includes all the proxies concerning the nature of linkages (link, time rd and time consult) and all the control variables explained in Section 4.2. Models 2, 3, 4 and 5 are alternative specifications for each output, where some variables are excluded since their high correlations (see Table 4) would generate a multicollinearity problem. For this reason, in model 2 (columns 2, 7 and 12) sqr time, sqr research, NRS and FP were dropped. Model 3 re-includes sqr time (columns 3, 8 and 13). Model 4 re-includes sqr research (columns

9

5. Econometric analysis

1 −0.2919* −0.0938 0.0029 −0.1600* −0.0804 0.0815 −0.1975* −0.0715 −0.1224* 0.1634* −0.0363 −0.0645 0.1023 0.1620*

10

11

12

13

14

15

oriented outputs (i.e. new recommendations and new techniques), indicating a scarce impact of one output on the production of the others. However, the correlation between recommendations and new techniques is positive and significant. This fact could be due to a couple of factors: (a) it is plausible that the determinants of both technical outputs are similar, and (b) an output can induce the introduction of the other, for example, a new technique (e.g. no till technique) could imply a change in the use of fertilizing (e.g. new recommendation). Table 4 reports the correlation among LHS variables. Several correlation coefficients are significant, however they are far from strong, reducing therefore the suspicion of presence of multicollinearity. It may imply that those variables explore different dimensions of the nature of academy-farmer linkages. The largest correlations, as expected, are between both time degree and research team and their respective quadratic expression (0.93 and 0.87). The correlation between other control variables is also relatively strong. For instance, NRS is positively correlated with PhD (0.42), and negatively correlated with FP (−0.47); and phd degree is negatively correlated with time degree (−0.40).

1 −0.2758* −0.0781 0.0569 0.0595 0.0931 0.0618 0.0715 0.1784* −0.0645 0.1297* −0.0274 0.0114 −0.0141 −0.0877 −0.0877

Significant at 5% or better.

1 −0.0819 0.1712* −0.1177 −0.1338* −0.0828 −0.3729* −0.1288* 0.0124 −0.1488* 0.075 −0.1480* 0.1131 0.0767 0.011 0.04 −0.0263

18

1

17

1 0.5317*

16

*

1 −0.0193 −0.0007

New techniques

2

Publishing New recommendations New techniques

New recommendations

1

Publishing

1 0.9360* −0.0863 0.2140* −0.1391* −0.1487* −0.0904 −0.4059* −0.1270* 0.0068 −0.1519* 0.0719 −0.1436* 0.1350* 0.0854 0.0224 0.0417 −0.0097

Table 3 Correlation between dependent variables.

1

R. Rivera-Huerta et al. / Research Policy 40 (2011) 932–942

(1) time degree (2) sqr time (3) inst 2 (4) inst 3 (5) inst 4 (6) research team (7) sqr research (8) phd degree (9) NRS (10) PF (11) basic science (12) social science (13) time rd2 (14) time rd3 (15) ti−me consult2 (16) link 0 (17) link 2 (18) link 3

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Table 5 Negative binomial estimations (robust). Publishing

time degree sqr time inst 2 inst 3 inst 4

sqr research phd degree NRS PF basic science social science time rd2 time rd3 time consult2 link 0 link 2 link 3 Constant Observations L-ps. likelihood Wald chi2 Prob > chi2

New techniques

Model 2

Model 3

Model 4

Model 5

Model 1

Model 2

Model 3

Model 4

Model 5

Model 1

Model 2

Model 3

Model 4

Model 5

1 0.049 (2.33)* −0.001 −1.57 0.18 0.62 −0.442 (3.11)** −0.086 −0.37 0.035 (3.67)** 0 (3.23)** 0.656 (3.77)** 0.53 (4.19)** 0.141 0.81 0.157 1.15 −0.617 (2.17)* 0.165 0.99 0.228 (1.66)+ −0.034 −0.26 −0.067 −0.27 −0.044 −0.26 0.154 0.86 0.033 0.11 262 −585.225 136.02 0

2 0.013 1.46

0.919 (2.14)* 0.286 1.53 −0.111 −0.31 0.012 (1.89)+

−0.017 −0.09

−0.013 −0.07

14 0.035 1.42 0 0.81 0.037 0.1 0.289 1.38 −0.023 −0.07 0.039 (2.54)* 0 (1.69)+ −0.039 −0.21

15 0.037 1.49 0 0.84 −0.039 −0.1 0.264 1.28 −0.055 −0.17 0.043 (2.82)** 0 (1.95)+ −0.003 −0.02

0.158 1.16 −0.788 (2.85)** 0.17 1.01 0.282 (2.10)* −0.071 −0.53 −0.025 −0.1 −0.012 −0.07 0.061 0.34 0.184 0.83 262 −591.136 111.57 0

−0.051 −0.21 −0.215 −0.71 −0.214 −0.88 0.418 (2.34)* 0.283 (1.77)+ −0.442 −0.95 0.036 0.2 0.461 (2.42)* −0.326 −1.2 262 −436.127 51.54 0

1.433 (3.28)** −0.013 −0.06 −0.311 −1.01 −0.227 −0.94 0.355 (2.03)* 0.295 (1.88)+ −0.44 −1.03 0.023 −0.13 0.423 (2.24)* −1.768 (3.50)** 262 −430.606 64.78 0

11 0.038 1.56 0 0.89 0.005 0.01 0.274 1.36 −0.11 −0.34 0.04 (2.74)** 0 (2.01)* 0.158 0.81 −0.64 (2.55)* 0.134 0.27 0.078 0.33 −1.098 (2.98)** −0.272 −1.01 0.295 1.46 0.133 0.76 0.443 1.54 −0.175 −0.76 0.424 (1.89)+ −0.831 −1.49 262 −382.840 55.1 0

0.011 −0.03 0.256 1.27 −0.086 −0.26 0.02 (2.83)**

0.046 0.27

10 0.021 1.03 0 0.76 −0.992 (2.31)* 0.288 1.55 −0.066 −0.18 0.036 (2.68)** 0 (2.30)* 0.093 0.54

13 0.035 1.4 0 0.82 0.006 0.02 0.247 1.22 −0.079 −0.25 0.02 (2.86)**

0.042 0.25

9 0.017 0.81 0 0.68 −0.888 (2.08)* 0.314 (1.65)+ −0.061 −0.17 0.028 (2.00)* 0 1.64 0.022 0.13

−0.233 −1.55 0.149 1.08 −0.755 (2.72)** 0.179 1.06 0.302 (2.21)* −0.08 −0.6 −0.047 −0.19 −0.011 −0.07 0.081 0.45 0.428 1.51 262 −590.627 117.15 0

6 0.022 1.07 0 0.81 −0.961 (2.21)* 0.292 1.6 −0.1 −0.27 0.033 (2.55)* 0 (2.25)* 0.174 0.97 −0.267 −1 1.224 (2.49)* −0.016 −0.07 −0.36 −1.19 −0.214 −0.88 0.375 (2.12)* 0.277 (1.73)+ −0.411 −0.94 0.033 0.19 0.39 (2.12)* −1.555 (2.82)** 262 −429.965 63.27 0

12 0.019 (1.78)+

0.926 (5.47)**

5 0.052 (2.38)* −0.001 (1.70)+ 0.223 0.8 −0.453 (3.19)** −0.173 −0.77 0.03 (3.18)** 0 (3.04)** 0.862 (5.01)**

8 0.017 0.77 0 0.67 −0.914 (2.12)* 0.279 1.48 −0.107 −0.3 0.012 (1.92)+

0.929 (5.40)**

4 0.053 (2.44)* −0.001 (1.73)+ 0.203 0.72 −0.447 (3.16)** −0.17 −0.76 0.033 (3.71)** 0 (3.58)** 0.88 (5.17)**

7 0.005 0.55

.158 0.55 −0.49 (3.44)** −0.21 −0.89 0.012 (2.16)*

3 0.051 (2.32)* −0.001 (1.65)+ 0.143 0.51 −0.502 (3.58)** −0.218 −0.95 0.012 (2.18)*

−0.02 −0.09 −0.977 (2.65)** −0.241 −0.94 0.324 1.61 0.18 1.03 0.444 1.47 −0.172 −0.73 0.512 (2.17)* −0.755 (2.45)* 262 −386.612 47.82 0

0.601 1.33 0.046 0.2 −1.027 (2.81)** −0.261 −1 0.279 1.37 0.187 1.08 0.45 1.52 −0.181 −0.77 0.492 (2.07)* −1.336 (2.51)* 262 −385.602 49.41 0

0.177 1.26 −0.773 (2.81)** 0.195 1.15 0.252 (1.84)+ −0.049 −0.37 0.011 0.04 0.003 0.02 0.094 0.51 0.458 (2.05)* 262 −595.279 90.64 0

0.162 1.17 −0.773 (2.89)** 0.166 0.98 0.237 (1.75)+ −0.062 −0.47 −0.031 −0.12 −0.004 −0.02 0.084 0.46 0.285 1.27 262 −593.814 102.39 0

−0.041 −0.17 −0.211 −0.72 −0.23 −0.94 0.386 (2.13)* 0.31 (1.93)+ −0.402 −0.85 0.052 0.28 0.466 (2.45)* −0.183 −0.71 262 −437.124 49.53 0

−0.044 −0.18 −0.21 −0.71 −0.233 −0.96 0.385 (2.13)* 0.311 (1.95)+ −0.41 −0.87 0.052 0.29 0.458 (2.43)* −0.253 −0.91 262 −436.987 50.51 0

−0.01 −0.04 −0.925 (2.41)* −0.24 −0.93 0.295 1.48 0.218 1.23 0.463 1.55 −0.149 −0.61 0.521 (2.21)* −0.582 (2.15)* 262 −387.841 42.05 0

−0.02 −0.09 −0.924 (2.40)* −0.248 −0.97 0.291 1.47 0.22 1.25 0.459 1.53 −0.159 −0.66 0.512 (2.19)* −0.678 (2.24)* 262 −387.630 41.79 0

R. Rivera-Huerta et al. / Research Policy 40 (2011) 932–942

research team

New recommendations

Model 1

Robust z statistics in parentheses (absolute value). + Significant at 10%. * Significant at 5%. ** Significant at 1%.

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R. Rivera-Huerta et al. / Research Policy 40 (2011) 932–942

4, 9 and 14) and finally, in model 5 FP was re-introduced (columns 5, 10 and 15). It is interesting that none of the main independent variables, that is to say, those that describe the nature of linkages, has a negative and significant impact on researchers’ productivity measured by publications. Moreover, we found a positive and significant relationship between publication productivity (publishing) and the proxy to strong intensity of linkages through R&D modality (time rd3). A similar association is found between new recommendation productivity (new recommendations) and this proxy of intensity of linkages (time rd3). The fact that only the most intensive relationships are those statistically significant in the case of publishing stresses the importance of long-term relationships for researching, allowing researchers to go into farmers’ problems in depth. Concerning new techniques, we found a different result; even though, in this case, the impact of time rd is positive, it is not significant, implicating that new techniques variable is not affected by the intensity of R&D linkages (time rd). The empirical evidence on the association between intensity of linkages through consultancy modality (time consult1) and publications productivity is negative even if not significant. This result is expected since most of consultancy activities are mainly technical ones, where the flows of knowledge are unidirectional and, therefore, can hardly contribute to the generation of new scientific knowledge, which is highly beneficial for researchers. In the same line, we have found a positive and significant relationship between productivity of new recommendations (new recommendation) and the development of consultancy activities (time consult). This result may be related to the technical nature of the latter. Finally, neither intensity of linkages measured through R&D nor through consultancy has an impact on the production of new techniques. Our evidence shows that the breadth of linkages (link) does not have any statistically significant influence on publishing productivity. Therefore, it is possible to state that only those linkages characterized by intensive relationships (above the median) in the R&D modality (time rd) foster publishing productivity. These results are in line with the literature that discusses the researchers’ long-term expected benefits of linking with the business sector (Perkmann and Walsh, 2009; Arza, 2010; Lee, 2000; Dutrénit et al., 2010). The proxy to breadth of linkages (link) has a positive and significant effect only on those products characterized by technical orientation. Thus, even though there is no statistically significant difference between the productivity of those researchers without linkages (link 0) and those that have a scarce number of linkages (link 1 and link 2), there is a statistically significant difference between link 1 and those researchers that have numerous linkages (link 3). Even if at the moment we do not have a full explanation for this result, it seems to suggest that the production of new techniques or new recommendation is influenced by linkages but only when the breadth reaches a threshold, which is a point of inflexion where a large number of linkages start being significant. Unfortunately, our data does not allow us to go in depth with this explanation. Overall, our results reject the extended idea that academicbusiness sector linkages have a negative impact on researchers’ productivity in the case of the agricultural sector. The evidence shows that our results differ by type of output. Concerning publishing – the most usual measure of research productivity – our results show that whether the breadth of linkages is extensive or linkages are characterized by intensive relationships, linking has a positive impact on researcher’s productivity or, in the worst-case scenario, linkages do not have any statistically significant impact. The following paragraphs briefly discuss other interesting findings. Concerning papers (publishing), column 1, indicates that, in accordance with (Henderson and Cockburn, 1996; Levin

and Stephan, 1991) but contrary to (Bozeman and Lee, 2003), Henderson and Cockburn (1996) and Levin and Stephan (1991) but contrary to Bozeman and Lee (2003), publishing follows a lifecycle pattern where researchers become more productive as they consolidate their careers but at a decreasing rate. The marginal significance of the coefficients, though, does not allow for a strong claim. Interestingly, this quadratic pattern is not observed in any other specification of the other outputs. As expected, there are strong institutional effects, i.e. the institutional affiliation matters in relation to the type of outputs elaborated and their productivity. Researchers ascribed to sectoral universities and PRC (inst 3), specialized in agriculture sciences, are more likely to produce less papers than those affiliated to general universities (inst 1), as a negative and significant effect was found. It could be a consequence of a differentiated incentive structure where publishing is not valued as much as other factors (e.g. seniority, specific solution of problems and so on). Additionally, but in the same line, relatively few researchers of these institutions belongs to National Researchers System, partly because their limited production of papers. Finally, it is important to consider that these institutions conduct more applied work including more extension-like activities than basic researching directed to generate more scientific knowledge. According to model 4, researchers belonging to these sectoral institutions tend to produce more new recommendations than researchers of general institutions. In contrast, researchers belonging to general PRCs (inst 2) tend to produce less new recommendations than those belonging to general universities, which suggest that specialization differs between general universities and PRCs. As it was previously mentioned in this section, to evaluate a potential multicollinearity problem, different specifications were estimated, in which the potential problematic variables were excluded, and re-introduced in consecutive specifications. Table 5 shows that the results of the alternative specifications (models 2, 3, 4 and 5) do not present significant changes with regard to the baseline model. In fact, concerning publishing, there is no significant changes regarding our preferred model (model 1), and concerning the other technical outputs (new recommendations and new techniques) there are no significant differences either.

6. Conclusions The analysis of research productivity has attracted the attention of several researchers, who have identified the linkages that researchers establish with other academics partners and with the business sector as important factors influencing research productivity. While in general the literature agrees on the positive impact of academic collaborations on research productivity, there is no such consensus on the effect of academy-business sector interactions; even more, the empirical evidence on this relationship is scant and contradictory. This paper contributes to this debate by exploring how broadly defined research productivity in agriculture-related fields is affected by the nature of interactions between academic researchers and farmers. Most empirical analyses have used a rather narrow definition of research productivity, i.e. numbers of papers published in scientific journals. We found that this definition is a poor indicator of productivity; researchers generate a variety of products, some of them oriented towards moving the scientific frontier, and others seeking to solve production problems. In this sense, different types of research should be measured by the specific outputs they produce. This paper has distinguished three research outputs in agriculturerelated fields: papers in scientific journals, new recommendations and new techniques.

R. Rivera-Huerta et al. / Research Policy 40 (2011) 932–942

The nature of interactions that researchers of universities and PRCs establish with farmers was approached through two dimensions: breadth of linkages (number of linkages with farmers) and intensity of linkages (duration of two different modalities: R&D and consultancy). We did not find a negative relationship between interaction with the business sector and publishing of papers; hence our results do not support the argument that such interactions negatively impact publishing (Czarnitzki et al., 2009; Stern, 2004; Toole and Czarnitzki, 2009; Goldfarb, 2008). Moreover, we found a positive relationship between the duration of the linkages through R&D and the production of papers. In other words, as linkages are built through the R&D modality, and these activities last for the long-run, this has a positive impact on the publishing of papers. This result reinforces the point raised by several authors that researchers are more motivated to interact with the business sector if they perceive they are getting long-term intellectual benefits, rather than shortterm benefits (Perkmann and Walsh, 2009; Arza, 2010; Dutrénit et al., 2010). Referring to the impact of the nature of interactions on research productivity, we found that this differs according to the type of research output. The impact is broader in the case of new recommendations than in the other outputs, as it includes both breadth and intensity of linkages. The production of recommendations is positively influenced by both the number of linkages (breadth) and the duration of these linkages through both modalities of interaction—R&D and consultancy. In other words, when interactions are carried out with a large number of farmers and are developed for the long-run through R&D and consultancy, more new recommendations are produced. We also found a scale effect related to the impact of the linkages’ breadth on the technical outputs, i.e. the production of new techniques and new recommendation is influenced by the number of linkages but only when it reaches a threshold (in our case it is 40 farmers). Other results are related to the impact of academic collaboration on research productivity and the incentives that affect the behavior of researchers in Mexico. It was found that academic collaboration plays an important role in the productivity of agricultural research for all products, and not only for papers as reported in the literature (Bozeman and Lee, 2003; Crane, 1972; Defazioa et al., 2009; Zuckerman, 1967; Rijnsoever et al., 2008; Czarnitzki and Toole, 2009). The National Researchers System only stimulates the production of papers and no other technical products (new recommendations and new techniques), which is a disincentive for the academy-business sector interaction. Beyond the national incentives, there are differences in the likelihood of production of different outputs according to the type of existing institutions. Researchers ascribed to general universities tend to produce more papers, given the other variables, than those ascribed to other institutions (e.g. sectoral institutions), while no differences in the productivity of the new recommendations were observed according to the type of institutions. It seems that in sectoral universities and PRCs there are few institutional incentives to produce papers, but those researchers from any institution who decide to produce new recommendations, do so for particular reasons independently of the existing institutional incentives. Our findings have important policy implications. In many developing countries, Mexico included, incentives offered to researchers are based mostly on the number of papers published in ISI journals. While this fosters an increase in the number of publications, it does not favor the development of solutions to problems faced by firms, society or, as in our case, farmers. Many of the policies to stimulate academy-business sector interaction promote a greater focus on the needs of producers, which would lead to the production of technical products. This does not seem appropriate in order to encourage researchers to establish links with the business sector.

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Policy makers must recognize that researchers, particularly in the agriculture sector, tend to produce scientific and technical outputs. As it was found in this paper, if the nature of the linkages is characterized by relationships with a greater number of farmers and developed through procedures that allow more intense and lasting relationships, researchers will increase their production of both scientific and technical products. The R&D modality seems to be the most appropriate in order to encourage researchers to produce both types of outputs. Hence, the results suggest that it is necessary to stimulate a link of a more intense nature (e.g. R&D), which allows for long-term linking, thus ensuring the production of more papers as well as of products aimed at specific problems of the farmers. This nature of the interaction also contributes to strengthening the innovation system. Our results are preliminary in nature. Our future research will explore in more detail different issues that emerge from this paper, such as the relationship between intensity of the interactions and research productivity, and to what extent differences in the profile of researchers lead to different types of research outputs, and if these profiles require different measures of research productivity.

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