Social capital is an important resource that can be mobilized for purposive action or competitive gain. The distribution of social capital in social–ecological systems can determine who is more productive at extracting ecological resources and who emerges as influential in guiding their management, thereby empowering some while disempowering others. Despite its importance, the factors that contribute to variation in social capital among individuals have not been widely studied. We adopt a network perspective to examine what determines social capital among individuals in social–ecological systems. We begin by identifying network measures of social capital relevant for individuals in this context, and review existing evidence concerning their determinants. Using a complete social network dataset from Hawaii’s longline fishery, we employ social network analysis and other statistical methods to empirically estimate these measures and determine the extent to which individual stakeholder attributes explain variation within them. We find that ethnicity is the strongest predictor of social capital. Measures of human capital (i.e., education, experience), years living in the community, and information-sharing attitudes are also important. Surprisingly, we find that when controlling for other factors, industry leaders and formal fishery representatives are generally not well connected. Our results offer new quantitative insights on the relationship between stakeholder diversity, social networks, and social capital in a coupled social–ecological system, which can aid in identifying barriers and opportunities for action to overcome resource management problems. Our results also have implications for achieving resource governance that is not only ecologically and economically sustainable, but also equitable.
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Although Marin et al. (2012) applied an ego-network approach, identifying the ties of fisher organization leaders, their analysis was focused on social capital implications for the organizations, rather than the individual actors.
The disagreement about whether social capital accrues at the individual level, the group level, or both is discussed in detail by Portes (1998) and Borgatti et al. (1998). Burt (2005; 1997; 2000) and Lin (1999a) are arguably the most heavily cited proponents of social capital being assessed at the individual level, while Putnam (2001) famously describes social capital at the level of whole communities and even countries. Following Burt (1992, 1997; 2000, 2005), Lin (1999a, 2001), Lin et al. (2001), Wellman (2001), and many others, our study focuses on social capital at the individual level analyzed from a network perspective. However, as the two meanings of social capital (i.e., collective vs. individual benefit) are highly related, we also follow the view that social capital accrues to, and is important for, both individuals and communities (see, Narayan and Pritchett 1999; Fafchamps and Minten 2002; Adger 2003).
Although we ground our analysis in the theory of social capital following the work of Burt (1992, 1997; 2000, 2005), Lin (1999a, 2001), Lin et al. (2001), Wellman (2001). and others, it is important to keep in mind that the precise definition of social capital is often ambiguous, if not contested (Dasgupta and Serageldin 2000; Durlaf 2002; Portes and Landolt 2000). Thus, an analysis of the broader concept of social capital comprising diverse social phenomena might also explicitly measure aspects of trust, norms, and reciprocity. It is also important to note that the network effects we discuss hold true independent of the social capital framework.
The entire dataset includes information from 145 fishers out of an estimated total of 159, five of which are considered isolated and not generally active in the HLF (Barnes-Mauthe et al. 2013). Of the 145, socio-demographic data on five fishers was incomplete. They were therefore excluded from this analysis.
Although there is some debate over whether strong or weak ties are more beneficial (Granovetter 1983) and thus better represent social capital, weak ties are known to hamper the transfer of complex, tacit knowledge (Hansen 1999), which is essential for understanding and adapting to the conditions in dynamic, complex social–ecological systems (Berkes et al. 2003). Weak ties are also less likely to be useful in situations where information is valuable and the costs of providing it are high (Brass et al. 2004), which is typically considered to be the case in competitive fisheries (Dreyfus-Leon and Gaertner 2006; Wilson 1990). Moreover, as argued by Burt (1992), the social capital benefits of weak ties originally described by Granovetter (1973), i.e., access to diverse information and resources at different scales and control over their flow, are more accurately captured by measures that explicitly take into account tie diversity (e.g., ties that bridge/link) and access to structural holes, which we capture through measures of brokerage (i.e., efficiency, bridging and linking factors). The assumption that strong ties are more beneficial in this context for the measure of average tie strength is thus well justified.
The majority of HLF fishers sell their fish at the Honolulu Fish Auction, which automatically charges 2 cents per pound of fish to all sellers.
Mailo and Johnson (1998) applied a step-wise regression in this analysis, and among recreational mackerel fishers, they did test years of experience and found it to be an important predictor, along with percent income from king mackerel and age. They also examined simple correlations between indegree centrality and organizational affiliations and measures of income among commercial shrimp fishers, where they found organizational affiliation and indegree centrality to have a significant relationship.
The title or role of an individual in previous studies is somewhat broadly interpreted here. For example, in the research on Kenyan fishers, fishers’ occupation (referred to here as title) was distinguished by their gear type (Bodin and Crona 2011; Bodin and Crona 2008; Crona and Bodin 2010, 2006); in the UK Peak District National Park study (Prell et al. 2008; Prell et al. 2009; Prell et al. 2011), roles were distinguished by stakeholder categories (e.g., agriculturalist vs. conservationist); and in the study from a Mexican forest community, roles were distinguished by land ownership (García-Amado et al. 2012).
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Funding for this research was provided by the U.S. National Science Foundation Grant #GEO-1211972 and the Pelagic Fisheries Research Program. We are grateful to the participants of the Networks and Natural Resource Management session at the International Network for Social Network Analysis Sunbelt 2014 meeting, and particularly Örjan Bodin, Christina Prell, and Steve Borgatti, who provided invaluable feedback on this work. We thank all fishers who have been involved in this ongoing project, Joey Lecky for his graphic design expertise, Minling Pan of the NOAA Pacific Islands Fisheries Science Center for her support, and three anonymous reviewers for their thoughtful and constructive comments.
The authors have obtained formal, written permission from the copyright owners to use Fig. 2 in this manuscript.
All research conducted in this analysis complied with current laws and ethical standards of the U.S.
Conflict of interest
The authors declare that they have no conflict of interest.
Shawn Arita—The views expressed herein are the authors’ and do not represent those of the U.S. Department of Agriculture (USDA) or the Economic Research Service.
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Barnes-Mauthe, M., Gray, S.A., Arita, S. et al. What Determines Social Capital in a Social–Ecological System? Insights from a Network Perspective. Environmental Management 55, 392–410 (2015). https://doi.org/10.1007/s00267-014-0395-7
- Social networks
- Social capital
- Human capital
- Ethnic diversity, Stakeholder attributes
- Natural resource management