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Distinguishing patterns of learning and inclusion through the dynamics of network formation in developing agricultural clusters

Distinguishing patterns of learning and inclusion through the dynamics of network formation in developing agricultural clusters

Ramirez, Matias, Bernal, Paloma, Clarke, Ian, Hernandez, Ivan and Rotolo, Danielle (2014) Distinguishing patterns of learning and inclusion through the dynamics of network formation in developing agricultural clusters. In: Geography of Innovation 2014, 23-25 Jan 2014, Utrecht, The Netherlands. (Unpublished)

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A significant development in the economies of a number of less developed Latin American countries over the past decade has been the mushrooming of export and commodity-based agricultural clusters in hitherto economically underdeveloped regions, many of which are dominated by small-scale agricultural production. A consequent challenge for policy makers is to develop strategies that offer small-scale local producers opportunities to upgrade into more specialised higher value-added activities (Schmitz & Nadvi
1999, Gibbons 2001). We discuss three types of networks based on different degrees of network cohesion and roles of strategic actors. Debates on how to create more efficient production for small-scale agricultural producers emphasize the social, as much as the technological features of small-scale production. Two key issues emerge. The first of these relates to local participation, civic engagement and social capital, which are seen as a precursor to achieving some degree of coordinated action by local actors (Mansuri and Rao 2013). This discussion extends beyond simple connectivity, to the recognition of the need to “embed” groups that instil social norms on their members and can hold people accountable (Tsai 2007). Secondly, there is a focus on the role strategic actors play in learning processes at local cluster level. Within the innovation
literature, lead firms are the central unit for the implementation of new technologies, and it is assumed that, through a variety of mechanisms, good practices can be disseminated to other local actors. By contrast, within the development literature, the role of key firms has often been balanced through a discussion of the benevolent and malevolent effects of the capture of resources by local elites (Abraham and Platteau 2004, Rao and Ibanez 2005).
The motivation for this paper is to understand the dynamics of local participation in processes of learning with different lead actors as opportunities for export and/or commodity production within clusters emerge. We conceptualize this dynamic using social network analysis (SNA), the analytical approach of which is grounded in social capital theory and is built around the twin concepts
of network cohesion (the degree of mutual socialization) and the position that focal actors hold within a network (Burt, 1992; Gargiulo & Benassi, 2000). Hence, the structural features of the networks provide insights into the connectedness of actors, while the position of individual actors in the network provides measures of the diverse resources some strategic organisations have at
their disposal through the connections they have access to (Carpenter et al. 2012). This allows us to make some predictions regarding how different patterns of connectivity, cohesion and centrality of key actors in the cluster can influence learning, diffusion of knowledge and the degree of dependence some organisations have over others in these emerging clusters. The empirical data was gathered firstly through two surveys of producer organisations in two “emerging” agricultural clusters, the
Palm oil cluster in Colombia (22 organisations) and the mango cluster in Northern Peru, (26 organisations), both of which contain large numbers of small-sized producers and have experienced rapid growth in the recent period but demonstrate quite different network structures. From these two case studies, we extrapolate three types of network structures that have quite different patterns of inclusion, lead organisations and knowledge diffusion dynamics. Taking these three as separate case-types, a series of interviews
were then undertaken with local producers, service organisations and policy makers to provide further depth to our analysis of these networks. The Figure 1 shows each of these variables on different measurement planes. The dominance of focal actors is measured by the degree centrality (Freeman 1979). For cohesion we look at the K-core measure of sub-clusters (Doreian and Woodard 1994). The degree of learning we look at the in-degree for knowledge from outside the cluster. We firstly identify a network with one highly dominant producer organisation surrounded by small-scale producers. In this case, network theory suggests that networks will be highly asymmetric, i.e. most vertices have a limited activity and a few vertices have a very strong ability to acquire external knowledge and develop network ties (Coward and Jonard 2009). In these circumstances, inequality will be high and can be locked-in, as a large firm will benefit directly from new opportunities and there exist vast differences in capabilities and weak resources of small producers. Here inclusion in learning is likely to occur primarily through a
process of “meme transmission” i.e. imitation and a paternalist relation. Secondly, we identify a network with few lead producer organisations, and learning as well as diffusion of knowledge rely upon an ecosystem of service organisations coordinated by producer associations. In this case, high cohesion and the establishment of a cooperative infrastructure of endogenous institutions through which common pool resources and technology can be managed is critical. Networks based on cooperative infrastructures
are more likely to use the network as a space to learn and provide new practices, rather than a simple information transmission mechanism. Finally, we distinguish a network that resembles Schumpeterian characteristics, where a number of firms both compete and cooperate and have access to external networks. In this case, the network is defined by the need for access to markets and commercial rather than cooperative prerogatives. The above distinctions emphasize that inclusive processes of learning requires an understanding of the nature of the local networks, that incorporate the role of local leaders, the nature of connectivity and the existence of cooperative institutions. These features will have important implications for guiding the intervention of policy makers.

Item Type: Conference or Conference Paper (Paper)
Additional Information: [1] Presented by Matias Ramirez at Geography of Innovation 2014, held 23-25 January 2014, Utrecht University, The Netherlands. [2] ABSTRACT from full paper: This paper analyses inclusion of small-scale and micro farmers in agricultural clusters in commodity production through the dynamics of network formation through an analysis of two emerging clusters. We find that small farmer participation encourages both greater network connectedness and the search for outside knowledge. Also, that in the more developed clusters, communities of producers based strongly on solidarity and social capital exist alongside networks that prioritize brokerage and one-to-one links. Building institutions that bridge these may be the key to combining growth with social inclusion.
Uncontrolled Keywords: learning, inclusion, clusters
Subjects: H Social Sciences > HD Industries. Land use. Labor
H Social Sciences > HM Sociology
Faculty / Department / Research Group: Faculty of Business > Department of Systems Management & Strategy
Related URLs:
Last Modified: 07 Aug 2018 10:14
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None

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