This was written as a response paper for a course on social networks. We were asked to write three essays critiquing network research in our area of interest at the micro (people), meso (organization/community/infrastructure), and macro (nation scale) levels.

Generally, we chose essays that used structural network analysis themselves; in this case, I chose a paper that adopted a network (or relational) way of looking at the world, but didn’t use these formal methods. Structural network methods are a set of (mostly) quantitative approaches that (as their name implies)  describe the structure of relationships (ties) between different actors (nodes) or the position of a particular actor within this structure.

For more on network analysis, here’s a pretty good simple overview of some basic concepts.

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Unraveling Agro-food Network(s)

Drawing inspiration from Granovetter’s (1985) seminal work on embeddedness, food systems researchers in the late 1990s began to integrate economic, social, and political approaches to food systems into a network-based ontology.  Rather than look at global food systems as structurally ossified “regimes,” linear commodity chains, or markets made up of rational, disconnected actors, researchers re-imagined food systems as complex webs of actors linked by social, political, economic, and physical ties. Despite the popularity of the network metaphor, there are still few examples of researchers employing formal network methods to describe the structure of agro-food networks.

Raynolds’ (2004) is no exception. Her study of organic agro-food networks falls within a family of research that has blossomed in the last decade, which focuses on “alternative” agrifood networks (e.g. local and regional, fair trade, artisanal, etc).  She employs commodity network analysis to examine consolidation in global organic networks focusing on network governance, or the mechanisms that underlie the development of network ties. She demonstrates that certification standards play a major role in determining and maintaining an inequitable structure of relations between organic food actors in periphery and core (South-North) nations, but stops short of explicitly specifying and measuring this structure. Finally she observes that there is a “bifurcation” in organic agro-food networks between this “globalized system of formally regulated trade” and networks based in “alternative movement conventions,” and suggests that these alternative networks may offer opportunity to upend the reproduction of traditional South-North inequities, as well as inequities between large and smaller scale firms (Raynolds, 2004:725).

By design, the commodity network approach looks at multiple dimensions of global organic networks simultaneously. It describes social, political, cultural, and economic ties. Nodes aren’t limited to one type, but at times are hemispheres, at times, nations, firms, and individual consumers. The boundaries of analysis shift at times from a North-centered organic processing and distribution network to a movement network of consumers directly connected to local farmers to a global exchange between North-South nations.  What might we might learn by focusing in and using formal network methods to measure the observable interactions between a specified set of actors? In the following paragraphs, I unravel three of the many networks that Raynolds (2004) invokes, specify the nodes, ties, and boundaries, and use her analysis to make guesses at network measures like degree, density, and centrality. Then I describe how network analysis might be used specifically to add depth to Raynolds’ final conclusion about the “bifurcation” between mainstream and alternative organic agro-food networks.

The main thrust of the argument takes place at the macro-level, looking at the relationships between periphery-core nations, specifically between Southern countries (especially in Latin America) and Northern markets (especially in the United States, Canada, Western Europe, and Japan). In this network, the nodes are countries, the ties are imports and exports, and the boundaries are (mostly) limited to Latin America and the major markets described above. From this, we can infer that Northern countries will tend to have higher in-degree centrality than Southern countries (hence their “core” status).  Raynolds also describes a robust “inter-core” trade “dominated by US exports to Europe and Japan, trade between European nations, and exports from Australia, New Zealand, and South Africa to the top markets” (p. 725). Considering this, and that products might flow more than one step (e.g. organic tomatoes produced in Chile, processed and canned in the US, and sold in Japan; peanuts grown and shelled in Canada, included in mueslix in Germany, shipped to the UK), we might meaningfully measure betweenness and closeness centrality. This might help to identify particular Latin American countries as “bridges” that are serving as a gateway between Southern producers and Northern markets; certain Northern countries (the US, for example) with high betweenness scores might also be brokers with more power to set the global organic agenda. These measures would require data measuring the flow of some subset of organic products (all edible organic products, organic fruits and vegetables, all processed products etc.) between each country dyad. With this data, we could also compare a network of actual trade with a network of trade that we might estimate based on a gravity model based on “distance” as measured by cost of transport between countries, and “size” as measured by number of organic hectares, length of growing season, and total population. The differences between the actual and estimated networks would shed light on political, social, and cultural structures that intervene in the network. Though the data required for this analysis would not be easy to compile, it might be possible to get at by combining a variety of sources and using estimates, and the result could a more nuanced view of North-South organic agro-food trade dynamics.

Raynolds (2004) also considers a meso-level network of organic agro-food firms. In this case, the nodes are all organic firms (including farmers, aggregators, distribution companies, processors, and retailers) and the ties could be any type of business relationship (e.g. sales between firms). Raynolds describes a change from a “loosely coordinated local network of producers and consumers to a globalized system of formally regulated trade which links socially and spatially distant sites of production and consumption” (p. 725). The trend is towards greater spatial distance between nodes (which would not necessarily be captured in the network I specified above), and also towards consolidation: in network terms, a decrease in the overall size of the network and increased density. Howard (2009) documents this trend in his visualization of consolidation in the North American organic industry, but his study also does not employ formal network measures. Again, data is difficult to obtain on relationships between organic firms, especially given such a broad boundary; however, it is possible to limit the boundary to a particular commodity or limit the type of firm (e.g. only farmers and distributors) to get at a particular aspect of this broader network. This would make it possible to identify more “powerful” firms, not just in terms of endogenous characteristics like size, but also in terms of their position in the network.

Finally, operating beside the macro and meso-level networks is a micro/meso-level network of movement actors and industry groups that shape and challenge norms within the organic movement as well as certification standards. This network could be operationalized as a two-mode network of organic food movement organizations, industry groups and policy-making bodies like the USDA tied by common individuals (e.g. Deputy Secretary Kathleen Merrigan of the USDA formerly staff at the National Sustainable Agriculture Coalition, or former president of the Organic Farming Research Foundation currently head of an organic department at USDA); it could be a two-mode network of movement organizations tied by association with broader trade associations or participation in specific political campaigns; or it could be a network of social movement organizations tied by some other indicator of collaboration. Specifying and mapping these relationships would allow us to see more clearly which agencies, industry groups, and movement organizations occupy more influential positions in the network. If we notice particular clusters of groups, we might look to see if shared norms exist within these clusters and if they specific capacity for collective action. We might also be able to characterize more clearly the “conflict” that Raynolds (2004) describes between movement actors and industry groups in determining certification standards. If we were able to measure this over time, we might also see whether movement advocates like Fred Kirschenmann (2007), who have advocated for a more harmonious marriage between the organic industry and the organic movement into a more integrated organic community, have had any effect.

The article ends by reasserting this conflict between two parts of the organic agro-food network: the one that is governed by organic certification standards driven by commercial and industrial conventions that privilege economies of scale and efficiency, versus the one that is governed by domestic and civic conventions of trust, tradition, and overall good to society. Raynolds (2004) bases this on her observations of “alternatives” to “mainstream” organic networks that represent the “theoretically important […] contested terrain negotiated within and between commodity networks” (p. 738). This dichotomization of movement-based “alternative” networks versus “mainstream” or “industrial” networks is typical of contemporary food systems studies, yet little research has been done to examine these supposedly different networks systematically to compare their structures and ask whether they are really as “bifurcated” as theory assumes.

To systematically analyze this assumed separation, we might choose a particular organic product within a given geography that we believe has strong “mainstream” and “alternative” networks of production and consumption; say, for example, organic berries in the Pacific Northwest which might be produced by small local farms and sold at Farmers Markets and through Community Supported Agriculture schemes or produced in Latin American countries, imported, and sold at larger retailers. We could set the nodes as all firms that participate in production, aggregation, processing, and sale of the particular product, and stipulate ties as total volume of transactions between firms. The data could be collected through a mix of interviews, publicly available data, and estimates based on observations. With this data, we could do a better job answering questions like: Are “mainstream” and “alternative” networks really so bifurcated, or do firms actually overlap (we might expect, for example, some overlap in mid-sized producers who sell both at farmers markets and to larger supermarkets)? If two separate cliques of firms do emerge, are they different structurally: More or less dense? More or less centralized? Which firms have power in each clique? Are norms really different in each clique? How so?

To date, food systems researchers have not yet embraced structural network analysis despite a network-based ontology that recognizes the relational aspect of both industrial and alternative food chains. For one thing, as in the case of the above examples in the organic agro-food sector, data can be difficult to collect. In network analysis, missing data has particularly strong negative consequences on the statistical validity of the data. Even where it is possible to collect data, social network methods can seem inaccessible and overly technical. Yet these methods have the potential to bring more clarity to specific questions about how global, organizational, and individual actors connect to one another to both uphold and upend our current systems of producing, processing, distributing, selling, and consuming food.

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Works Cited

Granovetter, M. 1985. “Economic action and social structure: the problem of embeddedness.” American journal of sociology 481–510.

Howard, Philip. 2009. “Consolidation in the North American Organic Food Processing Sector, 1997 to 2007.” International Journal of Sociology of Agriculture and Food, 30.

Kirschenmann, Fred. 2007. “Guest Feature: Beyond Organic, What’s Really At Stake?”

Raynolds, Laura T. 2004. “The Globalization of Organic Agro-Food Networks.” World Development 32(5):725-743. Retrieved April 17, 2012.

I sometimes wonder whether academic writing has any purpose other than to 1) exclude and create a class of “experts” that have legitimacy and power (check out this great TED talk on when experts are warranted and when they’re dangerous) and 2) to obscure fuzzy thinking in jargon so that it can’t be exposed as such.

I’ve been reading William Zinsser’s classic On Writing Well and trying to apply it to my own writing (it’s a process…. :/)  I find that I’m sometimes able to translate ideas I come across in academia and bring them to everyday conversations, but more often than I’d like (and especially when I’m still working out an idea, or still unclear) I slip into using big words to say not much of anything.

Why do I (why do we as scholars) write the way I (we) do? How might changing the way I (we) write change the way I (we) think?

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FoodLab Detroit is a network of entrepreneurs supporting one another in developing businesses with a “triple-bottom-line” (social environmental and financial). FoodLab was founded at a small informal gathering of peers in January 2011; by August, the group had grown to nearly 30, adopted a charter, and created a steering committee. As of February 2011, we have 73 entrepreneurs on our online listserve and engage in a broadening portfolio of activities including regular meetings and networking events, business planning workshops, coordination of shared use kitchen space and other resources, and advocacy through speaking events and engagement with community partners. The organization participates in Detroit’s good food movement, a meta-movement comprised of diverse sub-movements united by the recognition that “today’s food and farming economy is ‘unsustainable’ – that it can’t go on in its current form much longer without courting a breakdown of some kind.” (Pollan, 2010). The organization is also a part of a movement to “reimagine” the city of Detroit. FoodLab members grapple with questions about GHGs and energy use, how to support local growers and connect people to their food, but also how to create more living-wage jobs, use vacant space, rebuild neighborhoods, connect an otherwise largely segregated city, and build a more participatory, responsive, and democratic community.

Our vision is to build a “network of thriving, diverse locally-owned food production, processing, and retail businesses that contribute to the well-being of our communities and are collectively committed to increasing healthy, green, fair, and accessible food options in Detroit area.” In service of this vision, we support nascent entrepreneurs in order to help them develop socially and environmentally conscious businesses that can be sustained over time. One of our primary strategies is helping to build relationships between entrepreneurs themselves and entrepreneurs and relevant external stakeholders and service providers. As FoodLab grows, how can research help us design more effective activities within this broad strategy?

This essay describes how Davidsson and Honig’s (2003) study of nascent entrepreneurs legitimizes FoodLab’s focus on relationship-building and offers suggestions on how network analysis (to which the authors allude) could provide even more nuanced and useful guidance. I begin with a summary of the authors’ findings on the effects of social capital on early-stage entrepreneurs; I go on to examine how their approach, despite its focus on the importance of relationships and its informal invocation of networks, differs from network analysis; I conclude with examples of how FoodLab could use network analysis to inform what structures of relationships are most beneficial to the development of early-stage social entrepreneurs, and how different activities might foster particular types of structure.

Social capital as a predictor of entrepreneurial “success”

How do different sorts of non-financial capital affect entrepreneurial success? Specifically, Davidsson and Honig (2003) measure the extent to which an individual’s stock of human and social capital can predict three stages in the entrepreneurial process: 1) whether or not she engages in nascent activities; 2) the frequency of her “gestation” activities (e.g. writing a business plan); and 3) first sale or profitability of the business. We will focus primarily on their findings related to social capital.

Researchers collected data from 380 nascent entrepreneurs and 608 non-entrepreneur control participants via an initial phone interview, then followed up with entrepreneur-participants after 6, 12, and 18 months to gauge the life of the venture over time. The data supported the claim that individual social capital is strongly associated with reaching all three stages in the entrepreneurial process (see Figure 1). While the authors characterized certain measures as “strong” versus “weak” ties (e.g. family bonds versus business network bridges) and acknowledged the theoretical difference between the two, data did not support claims about the effect of one type of tie versus another on the entrepreneurial process. Overall though, social capital did appear to explain a greater percentage of entrepreneurial success human capital (including formal education and attendance at business classes), especially when it came to achieving a first sale or profitability. Specifically, the authors found that

• Having parents in business, being encouraged by friends or family, or having close friends or neighbors in business increased the likelihood that someone would become a nascent entrepreneur.
• Being a member of a business network, contact with an assistance agency, being a member of a startup team, being encouraged by family or friends, having close friends or neighbors in business, and being married increased the rate at which entrepreneurs engaged in gestation activities.
• Finally, only one variable reliably predicted whether or not an entrepreneur would achieve sales or profitability within the 18-month study: whether or not the entrepreneur participated in a business network.

The authors highlight that connection with an entrepreneurial assistance agency did not necessarily correlate with whether an entrepreneur made an initial sale or achieved profitability within 18 months. Based on this, they argue,

[Social] relations are more important than maintaining contact with assistance agencies, or even in taking general business classes. […] The facilitation and support of business networks and associations may provide the most consistent and effective support for emerging businesses. […] Furthering our understanding of these specific nascent networks and learning how best to facilitate them represents an important activity for future entrepreneurship research. (P. 324-325)

As a nascent network, FoodLab implicitly recognizes the value of social capital and relationship-building. If Daviddson and Honig’s (2003) findings help to justify our existence and general approach, can they also lend more specific insight into how we should design and structure our activities?

Social capital as an generalized individual attribute versus specific relationship

While this particular study suggests that various relationships, and business networks in particular, can play an important role in entrepreneurial emergence, it does not explain the process by which these networks have an effect. The business network, and the social capital it represents, is a black box. In order to understand how a business network affects an entrepreneur (and not just that it does somehow) we would need first to understand the specific kinds of social capital or ties that are created in the context of a business network, and also to re-imagine social capital as a structure of relationships rather than an endogenous characteristic of an entrepreneur (e.g. membership versus non-membership).

Neal (forthcoming) points out that describing a network as such without engaging in network analysis tells us “very little about what networks are or how they work, frequently because they do not identify exactly who or what is connected or in what ways” (p. 5). In the case of Daviddson and Honig (2003) we see that entrepreneurs who belong to a business network are more likely to achieve a sale or profitability. The authors assume that this is because participation in a business network affords entrepreneurs with more bridging (rather than bonding) capital which Granovetter (1973) and others have suggested is important for the diffusion of new ideas and innovation. This assumption may be true, but there also may be instances in which networks (or dense clusters within networks) provide entrepreneurs with the bonding or “strong” ties that might foster exchange of resources or reinforce norms of behavior (e.g. calculated risk-taking or opportunism) that increase the likelihood of success. Rather than make a priori assumptions, we could define and measure specific ties within a network in order to understand more clearly how the nature and structure of relationships surrounding an entrepreneur contribute to success. Some specific examples of the sorts of ties that might develop within a business network include advice-giving, information or opportunity sharing, business partnership or collaboration, emotional support, inspiration, motivation, or emulation.

This approach would also shift focus from the individual to relationships as the unit of interest, and imagine social capital as a structural pattern of relationships (many or few, dense or thinly spread, reciprocal or not?) rather than an individual characteristic (does someone have it, or not?) Traditional approaches that emphasize personal attributes have a number of drawbacks. For one, they “treat each social system member as an astructural independent unit” which “assume(s) random linkages,” whereas in reality, relationships are not random (Wellman & Berkowitz, 1988, p. 31). Entrepreneurs may exhibit homophilic tendencies along characteristics like race, gender, and  age, as well as industry and level of experience. An emphasis on categorical attributes also creates false groups (for example, people who belong to a business network versus those that don’t) and ties these categories to certain outcomes.

These groupings do not get at the root of the matter. FoodLab entrepreneurs will not succeed because they belong to FoodLab, but because of the specific patterns of relationships they might build as a result of membership. The categorical approach may be expedient, but has less explanatory power and may lead to false conclusions. By actually measuring an entrepreneurial network, we can understand how certain types of social capital as evidenced in particular structures of relationship might facilitate diffusion of information versus actual adoption of new practices (Neal et al., 2011). By comparing more than one network, comparing the structure of an informal social network with an intentional business network, or comparing the effect of different activities in the same network over time, we might understand what types of programs and activities foster what type of social capital to what ends.

Future research: social capital and the individual, social capital and the group

In order to inform FoodLab’s strategic direction, we need to know more about:

1. How different network structures (aka types of social capital) lead to different outcomes. [IND VAR: network structure, DEP VAR: entrepreneurial outcomes]
2. How different activities lead to different network structures (aka types of social capital) [IND VAR: activities, DEP VAR: network structure)

In part one, we might ask questions like, do we prefer a more densely clustered or more loose network? Do reciprocal relationships matter? What effect do bridging versus bonding ties have on outcomes? In part two, we might ask things like: how does operation of a listserve versus in-person meetings affect the structure of relationships that form within FoodLab? How does intentional recruiting of diverse participants affect our network structure?

Borgatti (1998) gives some suggestions on using network measures to describe an individual’s social capital. He suggests looking at network size, density, heterogeneity, compositional quality, effective size, constraint, closeness, betweenness, and eigenvector values. In this case, we would measure the ego-networks of various entrepreneurs and see how they correlate to various entrepreneurial outcomes. However, Borgatti (1998), in line with Coleman (1988) recognizes that social capital doesn’t only belong to an individual, but can be construed as a public good external to the individual and contained within the broader group.

[My thinking on the exact measures I might look at is still in the very very baby stages... as my prof Zach Neal pointed out, there's no point throwing all these measures around if they aren't getting at something we care about (in his words, they need theoretical grounding.. in my words, they need a grounding in measuring some value we care about)]

Entrepreneurism is seen as a uniquely individual pursuit. Why might we be interested in measuring the overall social capital within a group of entrepreneurs? For one, Coleman (1988) argues that even when social capital doesn’t accrue immediate or apparent benefits to the individual, it can benefit a community as a whole by increasing the stock of overall obligation, expectation, and trust, thus facilitating future interactions. Measuring the effect of relationships on an individual entrepreneur’s success would not account for this.

Also, in the case of FoodLab, we are interested not only in supporting the individual success of businesses in creating social, environmental and financial value, but also in fostering a set of shared norms (e.g. a commitment to social and environmental values). We recognize the advantage of fostering closure within the group to facilitate trust, but also in bridging between otherwise disparate clusters of entrepreneurs (and entrepreneurial allies) to encourage innovation and facilitate more effective collective action. Students of the food movement in the US have commented on this bridging capacity as one of the major strengths of the movement (Hassanein, 2003; Starr, 2010). This shift from considering individual outcomes to outcomes for communities or groups marks a significant difference between traditional entrepreneurship and social enterprise, or enterprise in the service of social change (Thekaekara & Thekaekara, 2006). Systematically measuring FoodLab’s network and linking structural methods to outcomes for individual entrepreneur and for the broader community will be useful in articulating and demonstrating our value to members and supporters, as well as in guiding the mix and design of programs and activities.

Works Cited

Borgatti, S.P., C. Jones, and M.G. Everett. 1998. “Network measures of social capital.” Connections 21(2):27–36.

Coleman, J.S. 1988. “Social capital in the creation of human capital.” American journal of sociology 95–120.

Davidsson, Per, and Benson Honig. 2003. “The role of social and human capital among nascent entrepreneurs.” Journal of Business Venturing 18(3):301-331. Retrieved February 12, 2012.

Granovetter, M.S. 1973. “The strength of weak ties.” American journal of sociology 1360–1380.

Hassanein, N. 2003. “Practicing food democracy: a pragmatic politics of transformation.” Journal of Rural Studies 19(1):77–86.

Neal, J.W., Z.P. Neal, M.S. Atkins, D.B. Henry, and S.L. Frazier. 2011. “Channels of Change: Contrasting Network Mechanisms in the Use of Interventions.” American Journal of Community Psychology 1–10.

Starr, Amory. 2010. “Local Food: A Social Movement?” Cultural Studies, Critical Methodologies, 490.

Thekaekara, M.M., and S. Thekaekara. 2007. Social Justice and Social Entrepreneurship: Contradictory Or Complementary? Skoll Centre for Entrepreneurship, Saïd Business School.

Wellman, B., and S.D. Berkowitz. 1988. Social structures: A network approach. Cambridge Univ Pr.

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[1] Including a control sample allowed researchers to test how human and social capital affected whether or not an individual engaged in entrepreneurship at all. The longitudinal design sidestepped the problem of “success bias.” Rather than only measure sustained or successful activity, the data also captured “efforts that fail or are abandoned at early stages,” and shed light on the effect of education and relationships at various stages of the start-up process (p. 311). These two features of the research design set this analysis apart from other research on the emergence of new enterprise, which generally employ cross-sectional data on early-stage businesses.

In response to a post by an inspiring friend:

I’ve been thinking about this a whole lot lately in the context of my own work and life here in Detroit. I moved here in part for a sense of *community* and connectedness and I find that many of the people close to me are drawn & remain in the city for that reason — and yet that interdependence, that rich social web, that “deep participation” is so complicated, and often a source of discomfort.

I wonder how to motivate and manage participation, collaboration, decision-making in “flatter” systems and networks…. how greater interdependence & “richness and diversity of one’s experiences and the strength of one’s social bonds,” while magical on the surface, can be exhausting in practice… the constant give/take/brokering of our values/needs/actions within our networks is a lot in itself. Given our technology as a species, we are no longer operating at the scale of tribes, so we’re negotiating an ever increasing number of connections at varying scales… not to mention the fact that different people are able/willing to “enroll” to different degrees and those who have stronger ties end up being asked to give more than they can sustain as individuals or businesses or organizations (e.g. studies on entrepreneurs with stronger family ties being alternately a blessing and a burden on the business)…

So I guess I just wonder how we deal with this complexity?

When we move out of more bureaucratic, hierarchical command-based approaches to leadership to more participatory, emancipatory, democratic, distributed/chaordic models … and when we move from linear, cumulative models of progress or development to a systems approach focusing on sustainability and resiliency, what are the new kinds of tools (technological, cognitive, emotional, social, political) that we need to manage these changes?

Network modeling? Systems analysis? Ethnography? Facilitative leadership skills?

Spirituality and religion!?