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Social Network Analysis of Social Capitalin Collaborative PlanningLynn A. Mandarano aa Department of Community and Regional Planning , TempleUniversity , Ambler, Pennsylvania, USAPublished online: 06 Feb 2009.

To cite this article: Lynn A. Mandarano (2009) Social Network Analysis of Social Capital inCollaborative Planning, Society & Natural Resources: An International Journal, 22:3, 245-260, DOI:10.1080/08941920801922182

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Social Network Analysis of Social Capitalin Collaborative Planning

LYNN A. MANDARANO

Department of Community and Regional Planning, Temple University,Ambler, Pennsylvania, USA

Social capital is an important primary outcome of collaborative planning and isdeemed a precursor to arriving at successful collaborative planning outcomes suchas more effective collective action and both individual and social benefits. Althoughcommonly used definitions of social capital stress the importance of social networks,recent scholarly research tends to overlook the importance of understanding howcollaborative efforts influence the formation of new relationships and the structuresof these relations (social networks) and in turn how these influence success. Thisarticle documents the application of social network analysis methods in the evalua-tion of a collaboration’s effectiveness at building social capital, the structures ofthese relations, the factors that influenced positively and negatively their formation,and finally, the influence of the social networks on realizing successful outcomes.

Keywords collaborative planning, environmental planning, national estuaryprogram, social capital, social network analysis

Social capital is an important outcome of collaborative planning and isdeemed a precursor to collaborative planning success. Social capital is viewed bysome researchers as an early outcome of successful consensus building and anenabler of such mid- and long-term outcomes as shared information, reducedconflict, and new collaborative efforts (Innes et al. 1994; Innes and Booher 1999).Wondolleck and Yaffee (2000) claim that social capital in the form of new relation-ships can facilitate information sharing to arrive at mutual understanding leading tomore effective decision making, more efficient coordination, and increased capacityto respond to future challenges. Finally, Rohe (2004) envisions social capital as amodel: Civic engagement begets new relationships, new relationships lead to greatertrust, and trust leads to more effective collective action as well as individual andsocial benefits.

Since the term’s rise to contemporary usage, social capital has been the subject ofrediscovery and redefinition by economists, sociologists, and others (Putnam 2000).Before we can claim to account for the benefits of social capital, we must agree on

Received 28 March 2007; accepted 6 September 2007.Address correspondence to Lynn A. Mandarano, PE, PhD, Department of Community

and Regional Planning, Temple University, 580 Meetinghouse Road, Ambler PA 19002,USA. E-mail: lynn.mandarano@temple.edu

Society and Natural Resources, 22:245–260Copyright # 2009 Taylor & Francis Group, LLCISSN: 0894-1920 print=1521-0723 onlineDOI: 10.1080/08941920801922182

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how we define it and measure it. Coleman defines social capital ‘‘as a variety ofentities with two characteristics in common: They all consist of some aspect of asocial structure and they facilitate certain action of individuals who are within thestructure’’ (Coleman 1988, S98). Putnam defines social capital as the ‘‘connectionsamong individuals—social networks and the norms of reciprocity and trustworthi-ness that arise from them’’ (Putnam 2000, 19) and ‘‘that enable participants to actmore effectively to pursue shared objectives’’ (Putnam 1995, 664–665). Both Cole-man and Putnam agree that social networks are the infrastructure of social capital.

Although Coleman and Putnam stress the importance of social networks intheir definitions of social capital, research on the relationship between collaborativeplanning and social capital has overlooked the importance of understanding thestructure of new relationships formed. For example, researchers have documentedthe effectiveness of using social capital as a criterion to evaluate the success of col-laborative environmental planning (Connick and Innes 2003; Innes and Connick1999; Margerum 2002). And others have evaluated the influence of collaborativeplanning on establishing such specific aspects of social capital as trust (Lubell2007), norms of reciprocity (Lubell 2004), or trust and norms (Weber, Lovrich,and Gaffney 2005). Leach and Sabatier (2005) in a meta-analysis of watershed part-nerships evaluated the correlation between interpersonal trust, and new relationshipsand improved understanding. And only one study evaluated the social structure ofcollaborative partnerships: Schneider et al. (2003) compared management structuresof 12 National Estuary Program (NEP) partnerships and 10 non-NEP estuary part-nerships. While these studies make important contributions to knowledge of therelationship between social capital and successful collaborative planning, they shedlittle insight on the structures of such social capital and how the structure of socialnetworks facilitate or constrain collective action.

This article documents a novel application of social network analysis methods inthe evaluation of the relationships between participants in a collaborative planningprocess. Social capital is assessed in terms of new relationships formed, networkstructures, factors that influenced the formation of new social ties, and influenceof the structure of the social networks on realizing successful outcomes. The casestudy evaluates a regional collaborative environmental partnership formed throughthe National Estuary Program and focuses on the Habitat Workgroup—one ofseveral working groups of the New York–New Jersey Harbor Estuary Program(NYNJ HEP). The findings demonstrate that social network analysis provides usefultools for evaluating the effectiveness of collaborative planning at building socialcapital in the form of new relationships and the structures of these relationships.Furthermore, the study reveals how internal and external factors influenced theparticipants’ capacity to build dense social networks.

Research Framework—Social Network Analysis

Social network analysis was initially developed in the 1930s by anthropologists seek-ing to understand social life within communities. By the 1970s, political analystsstarted to use and improve upon these methods to understand the social dynamicsof actors engaged in shaping national public policy (Laumann and Knoke 1987;Marin and Mayntz 1991; Knoke et al. 1996; Marsh 1998) and more recently regionalenvironmental policy (Sabatier and Jenkins-Smith 1993; Schneider et al. 2003;Laumann and Pappi 1976; Scott 1991; Heinz et al. 1993). Such social network

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analyses included assessments of indirect relationships and direct relationshipslinking individuals to one another.

To develop a picture of the general social structure of a community of actors,network analysts study the attributes of individuals to uncover indirect relationships.Indirect relationships may include common attributes such as occupation, religion,education, memberships, friendship ties, interests, and attendance at events(Laumann and Pappi 1976; Heinz et al. 1993) This research has shown how similaror dissimilar attributes such as status (Knoke et al. 1996), interests (Sabatier andJenkins-Smith 1993), beliefs (Laumann and Pappi 1976; Heinz et al. 1993), andbehaviors (Laumann and Pappi 1976) influence the formation of consensual rela-tionships and serve as general indicators of the strength of repellant or attractantrelationships between the two individuals or organizations. Moreover, Laumannand Pappi (1976) have demonstrated that one can distinguish the social structureof a population by estimating and mapping the relative proximity of actors usingsimilarities or dissimilarities of a population’s attributes.

Over the last two decades researchers have developed a framework to evaluatethe networks of direct relationships among organizations. The organizational stateperspective seeks to understand how participants use direct relationships (i.e., thetransfer of information, aid, power, money, and other resources between organiza-tions) to influence the decision-making process (Knoke et al. 1996). In The Organiza-tional State (Laumann and Knoke 1987) the authors identify three types of socialnetworks significant in interorganizational relations. These include informationexchanges; resource transactions such as the exchange of information money andauthority; and boundary penetration, which they characterized as the shared useof personnel. They also developed methods to assess these interorganizational rela-tionships in order to reveal the social network structure, which can be used to ‘‘betterunderstand how the overall configuration facilitates or constrains the actions oforganizational participants’’ (Laumann and Knoke 1987, 226). It is important tonote that many collaborative planning processes share the same characteristics ofdecision-making processes evaluated using the organizational state framework. Suchcharacteristics include effectively lacking the full support of legal regulations, adiversity of organizations from government and society, blurred lines of authority,and interorganizational influences and power relations (Laumann and Knoke 1987).

Study Context

The U.S. Environmental Protection Agency (U.S. EPA) National Estuary Program(NEP), authorized by Congress in 1987, is an ecosystem-based collaborative plan-ning program. NEP legislation encourages the landscape of federal, state, and localagencies and regional stakeholders to form collaborative partnerships and to assumelocal responsibility for the planning and management of an estuary’s ecologicalintegrity. While the NEP is a voluntary policy, upon acceptance into the NEP, eachpartnership is required to establish a Management Conference consisting of a PolicyCommittee, a Management Committee, a Citizens Advisory Committee (CAC), anda Science and Technical Advisory Committee (STAC). The Management Conferenceis responsible for managing the activities that led up to the publication of each pro-gram’s Comprehensive Conservation Management Plan (CCMP) and the implemen-tation of CCMP recommendations. In 1988, the U.S. EPA approved the nominationof the NYNJ HEP and approved NYNJ HEP’s CCMP in 1996.

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The collaborative partnership formed by the NYNJ HEP spanned the multipletiers of government, policy arenas, and jurisdictions and engaged regional stake-holders within the Management Conference and workgroups. Within the NYNJHEP, the organizations represented in the policy and management committees werethe region’s most prominent environmental agencies and stakeholder organizations.1

A former Management Conference representative noted that the composition of thePolicy Committee was unique to the National Estuary Program: At the time it wasformed, it was an exception to have CAC and STAC representation on the PolicyCommittee. Each of the committees that comprised the Management Conferencehad established structures; for example, the Management Committee had 19 desig-nated positions—fixed organizational representation, which allowed for fluctuationof individual participation due to for example retirement.

By comparison, participation in the Habitat Workgroup was more robust: Meet-ings engaged more participants and a more diverse participation. Unlike the fixedorganizational representation found in the Management Conference, participationin the Habitat Workgroup was open and nonstructured except for the position ofChair. According to the workgroup’s meeting minutes, 262 individuals participatedin the Habitat Workgroup between the years 1998 to 2002. While governmentalagencies were the majority at 54%, there was notable participation from nongovern-mental organizations at 24% and unaffiliated (including citizens) at 11%. The distri-bution of stakeholders in the Habitat Workgroup mirrors Lubell’s (2005) assessmentof stakeholders in 20 NEP estuaries.

The Habitat Workgroup also differed from the other workgroups active at thetime of this research in that it was not guided by regulations but by the outcomesof collaboration. The pathogens, nutrients, and toxics workgroups’ activities weredirected by such national and state regulations such as Clean Water Act require-ments for each state to establish total maximum daily loads (TMDLs) for pathogens,nutrients, and toxic chemicals. Finally, the Dredged Material Management Work-group served as an education forum and was not active at implementing activitiesas were the other active workgroups. This was primarily due to the fact that theNYNJ HEP was not successful at developing a dredged material managementchapter for the CCMP. In contrast to the three routinely active workgroups (patho-gens, nutrients and toxics), the Habitat Workgroup’s initiatives lacked supportof regulations directing habitat acquisition and restoration. Instead, the HabitatWorkgroup’s direction was guided by the participants’ priority ranking of actionitems identified in the habitat chapter of the CCMP. In summary, the HabitatWorkgroup exhibited all of the defining characteristics of an organizational state.

Research Methods

This case study employed qualitative and quantitative methods including eliteinterviews, a survey instrument, and quantitative data analysis using social networkanalysis methods. Identifying the Habitat Workgroup elites involved three stepsthat were derived from sample selection methods used in social network analysis.First, I identified the total population of individuals who were involved in theHabitat Workgroup using archived Habitat Workgroup meeting minutes datingfrom August 1996 to December 2002. As noted earlier, this resulted in a list of262 individuals. In step two, I sorted the more consequential actors from the listof 262 individuals assuming higher frequency of attendance as an indicator of

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consequential participation. The results revealed that approximately 18% of the totalpopulation had participation rates ranging from 25 to 94% and more than 30%of the population had attended one meeting only. The population having a 25%minimum participation rate became the core sample. However, if organizationshad more than one representative within the core sample, the individual with thehighest participation rate became the primary contact.

Because routine participation alone was not reflective of an organization’s orindividual’s consequence in a population, the final step to identify Habitat Work-group elites included verification using key informants. To this end, I solicited inputfrom the current Chair of the Habitat Workgroup, the Director of Harbor EstuaryProgram, and, at their suggestion, a representative from the New York State Depart-ment of State (NYSDOS). Each of the key informants reviewed the core sample toverify the sample of consequential participants and to identify influential actors whowere active from the workgroup’s initiation in early 1990 through 1996, which pre-dated the records available. This resulted in a 22-person Habitat Workgroup elitesample. The distribution of organizations in the sample closely resembles that ofthe Habitat Workgroup’s total population discussed earlier except for the lack ofparticipants from academia, private business, private citizens, and potentially otheraudiences. The nonrandomness of sample selection biased these results. BetweenApril and July 2003, I interviewed 18 of the 22 individuals from the Habitat Work-group elite sample.

Empirical data for the Habitat Workgroup network analysis were collectedthrough a survey instrument. The first part of the survey prompted respondents toprovide information on general attributes of the individual (i.e., education, field ofstudy, length of participation) and organization. The second part of the surveywas based on social network analysis data collection methods that solicited informa-tion on interests. This section of the survey prompted respondents to indicate his=herorganization’s level of involvement in a range of harbor related interests.2

The final section of the survey was based on Laumann and Knocke’s (1987)organizational state data collection methods and was designed to capture data onthe direct interorganizational relationships in the form of pairwise exchanges ofinformation, resources, and funds. Each respondent was prompted to identify theorganizations its organization engaged in the exchange of information, resources,and funds with respect to habitat-related issues. The respondents provided informa-tion on the quality of these linkages, such as the direction of the communication(we initiate, they initiate, or two-way exchange). The respondents also identifiedhow communications between organizations changed (increased, remained thesame, decreased, or no exchanges) as a result of their participation in the HabitatWorkgroup.

Data analysis3 included the use of UCINET 6 (Borgatti, Everett, and Freeman1999), a software program for social network analysis. Given next are briefdescriptions4 of the software applications and other methods employed in this study.The methods are presented in the order that they are introduced in the following section.

. The SIMILARITIES application calculates the similarities of attributes (i.e.,religion, meeting attendance, organizational interests) between each pair of actorsin the community. The application undertakes iterative correlations of each pairof actors for each attribute and generates a matrix of similarity coefficients anda scatter plot, a two-dimensional graph theoretic display. The scatter plot reveals

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the relative proximity of actors based on their similarities. The closer organiza-tions appear in the plot the greater their similarity.

. The HIERARCHICAL CLUSTERING application is another method to calcu-late similarities. This analysis starts with all the actors in a universal set and thensuccessively evaluates the similarities between the actors generating smaller andsmaller subsets of like actors. The output is presented in graphic and tabular for-mats, which reveal to what extent a network is partitioned into groups of actorswho share unique characteristics (i.e., similar communication patterns).

. DENSITY calculates of the total number of actual ties between organizations inthe network divided by the total number of possible ties. If a community has anetwork density of 100% then all of the community’s members have formed directrelationships with all other members. Network density is an indicator of the com-munity’s social capital.

. Change in communication patterns is a longitudinal assessment and is the calcula-tion of the number of actual ties between actors that respondents indicated chan-ged as a result of their participation in the Habitat Workgroup divided by thetotal number of all possible ties. The resulting value is an indicator of the impactthe collaborative process had on building social capital: new and improvedrelationship.

. The CIRCLE GRAPH application generates a simple representation of a networkstructure. In the circular graph all actors appear along the perimeter and the dis-tances between actors is not significant because it is held equal and constant.MULTIDIMENSIONAL SCALING (MDS) develops a map of the networkstructure based on the community’s similarities or differences. In MDS displaysthe distances between actors are significant and represent the relative proximitiesof actors based on the degree of similarity or difference (Laumann and Pappi1976). In both types of graphs the direct relationships are shown as lines connect-ing actors. In the diagrams that follow the lines between actors include arrowsindicating the direction of communication. Both types of graphs reveal networkstructure, as well as facilitating understanding and interpreting the results of othersocial network analyses.

. CENTRALITY calculates each actor’s network density (e.g., the number of ties)and reveals the extent to which each actor was successful at developing ties withothers in the community. For this study, the highest possible degree of centrality isseven: there are eight actors in the network and one actor only can be linked toseven other actors. Centrality is an indicator of each actor’s social capital.

Finally, it is important to discuss several assumptions made. Because the surveyprompted respondents to indicate the direction of communications not merely thepresence of routine exchanges, the raw data set included responses between pairs oforganizations that did not match. For example, the U.S. EPA Regional Officeresponded that it initiated resource exchanges with the Port Authority of New Yorkand New Jersey (Port Authority) and the Port Authority responded that it did notexchange resources with the U.S. EPA. In such situations, I chose the surveyresponse that corresponded to the higher level of communication. Two assumptionsground the decision for choosing the higher level of communication. First, therespondent may not have been fully aware of his=her organization’s communica-tions patterns, in which case the respondent would indicate a lower quality direc-tion. Second, the term ‘‘routine and regular’’ is open to interpretation and if

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a respondent used a strict interpretation then the respondent would indicate a lowerquality response.

Results

General Social Structure—Similarities of Interests

Figure 1 highlights the social structure of the Habitat Workgroup based on similarlevels of involvement in harbor related interests. This two-dimensional map of thegeneral social structure was generated using the SIMILARITIES application. Thecloser organizations are to one another in Figure 1, the greater is the similarity oforganizational interests. Then hierarchical clustering was applied to the same dataset to reveal if organizations fell into to clusters of shared interests. The results ofthe clustering analysis indicate that within one step from the base set of all actors,

Figure 1. Social structure: proximity by mutual interest. This figure is a mathematicallyderived representation of the proximity of actors. What counts is the relative distance betweenpoints not the corresponding coordinates, which are arbitrary and normally have no interpre-table meaning (Laumann, 1976). Acronyms: Coalition for the Bight (CfBight), EnvironmentalProtection Agency (EPA), Fish and Wildlife Service (FWS), Natural Resource DefenseCouncil (NRDC), New Jersey Department of Environmental Protection (NJDEP), New YorkCity Department of Parks and Recreation (NYCParks) NY=NJ BayKeeper (BayKeeper),New York State Department of Environmental Conservation (NYSDEC), New York StateDepartment of State (NYSDOS) and Port Authority of New York and New Jersey (PortAuthority). Source: Graphic generated using network analysis software: UNINET 6.0(Borgatti, Everett, and Freeman 1999).

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the Port Authority splintered off into a group of one. This is because the PortAuthority was the only organization that was not directly involved in watershedplanning. At the second step, the remaining organizations were subdivided intotwo groups: six in the upper hemisphere and three in the lower hemisphere. Theorganizations loosely scattered in the upper hemispheres had only two commoninterests: land conservation and funding environmental projects. In contrast, organi-zations that shared a much larger set of interests were tightly spaced in the lowerhemisphere. In the succeeding steps of clustering, the organizations in the upper por-tion of the graph formed subgroups more quickly than the three organizations in thelower portion of the graph. The smaller group remained cohesive longer in the clus-tering analysis because the group members shared more interests and common levelsof involvement.

The results of these applications indicate that the vast majority of these organi-zations within the Habitat Workgroup ranged from being loosely to tightly linked bycommon interests. This level of connectivity should facilitate the formation of newrelationships. However, the formation of social ties with the Port Authority, the soleoutlier, could prove difficult for this community because its constituents will need tobridge the gap in interests, potentially competing interests, in order to find commonground and to form meaningful relationships.

Social Network Structures and Formation of New Relationships

Information Exchange NetworkThe interorganizational information exchange network is remarkable, having a veryhigh network density: 95% of all possible links were present. Equally as important isthe fact that respondents indicated that as a result of their participation in the Habi-tat Workgroup, 79% of the information exchange relationships improved (20%remained the same and 2% did not exist). The circular graph shown in Figure 2 high-lights the comprehensiveness and cohesiveness of information exchanges betweenorganizations. There are only three exchanges between pairs of actors that are notmutual: the U.S. EPA and Port Authority, Natural Resource Defense Council(NRDC) and New York State Department of Environmental Conservation (NYS-DEC), and New York City Department of Parks and Recreation (NYCParks) andNew Jersey Department of Environmental Protection (NJDEP).

The high network density and extensive formation of new or improved relation-ships demonstrate that these organizations were able to develop this social capital bybuilding upon similar involvement in harbor related interests and overcoming differ-ences. This is particularly significant with respect to the Port Authority, which wasthe most extreme outlier within this community of actors and was not a participantin the Habitat Workgroup until mid-1999. In April 1999, the Habitat WorkgroupChair invited the Port Authority to attend the group’s next meeting to discuss itsComprehensive Port Improvement Plan, which documented the need to develop1,400 acres including wetlands at least 60 acres of which were included on theHabitat Workgroup’s priority list of sites for acquisition and restoration (HabitatWorkgroup 1999). In spite of these competing interests, within a relatively shortperiod of time, the collaborative process enabled Habitat Workgroup participantsto establish a dense network of information exchanges with the Port Authority. Thiswas achieved through engaging in more than 2 years of discussions related to tryingto resolve competing interests. One environmental nongovernmental organization

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(NGO) representative stated, with respect to building relationships with the PortAuthority, ‘‘What we got was a state-of-the-art discussion. Although there weremany competing issues, we all went through the same process and in the end, weunderstood each other.’’

Factors that enabled these organizations to form new and improved interorga-nizational relationships are based on their ability to create a consensus buildingforum and to benefit individually and collectively from informational resources(i.e., policy updates, project status, scientific knowledge, etc.) embedded in thegroup. The Habitat Workgroup exhibited what are believed to be important charac-teristics of a successful consensus building process such as authentic dialogue (dis-cussed earlier), clear agenda, frequent interaction, and committed participants(Wondolleck and Yaffee 2000; Innes and Booher 1999; Schneider et al. 2003). Whenthe Habitat Workgroup first met to initiate implementation of its CCMP identifiedaction items, its participants prioritized these actions and immediately began totackle high priority initiatives. Second, during the first few years the HabitatWorkgroup met monthly as a main group and monthly in self-selected subgroups,which reported updates back to the main group. Third, the average length of‘‘membership’’ is 10.22 years, which demonstrates a strong commitment to thegroup’s mission and respect of participating organizations.

Figure 2. Information exchange network: circle diagram. Acronyms: U.S. Environmental Pro-tection Agency (EPA), Natural Resource Defense Council (NRDC), New Jersey Departmentof Environmental Protection (NJDEP), New York City Department of Parks and Recreation(NYCParks) NY=NJ Baykeeper (BayKeeper), New York State Department of EnvironmentalConservation (NYSDEC), New York State Department of State (NYSDOS) and PortAuthority of New York and New Jersey (Port Authority). Source: Author generated graphicwith UCINET 6 (Borgatti, Everett, and Freeman 1999).

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Resource Exchange NetworkThe pattern of relationships in the resource exchange network is characterized asrobust with a network density of 75%. In fact, respondents reported that 47% ofthe resource exchange relationships increased (31% remained the same and 22%did not exist) as a result of involvement in the Habitat Workgroup. To shed morelight on this network structure, the data was further analyzed using MDS of similarresource exchanges, hierarchical clustering and centrality. The results of the first twoapplications are presented in Figure 3. The hierarchical clustering analysis revealedthat although concerns for the lower Hudson River estuary brought these organiza-tions together, the estuary acted as a divider. The first step of the clustering analysisidentified two distinct groups, indicated by the dashed line. One group includes theU.S. EPA, NYSDEC, NYSDOS, and NYCParks, and the other group includesNJDEP, NRDC, NY=NJ Baykeeper (BayKeeper), and Port Authority.5 The parti-tioning of the subgroups reflects the project area’s geography as the first group shareNew York based interest and the other New Jersey. This finding corroborates that ofGlaeser et al. (2002) who has found that physical distance deters the formation ofsocial capital. Finally, the centrality analysis highlighted why the U.S. EPA andNRDC appear in the center of this network structure. The two organizations devel-oped complete networks—they are connected to all other organizations. In addition,the NRDC shares more mutual ties with the NJ interest group than does the U.S.EPA, which explains why these two organizations with regional interests adhere todifferent geography-based clusters.

Figure 3. Resource exchange network. Acronyms: U.S. Environmental Protection Agency(EPA), Natural Resource Defense Council (NRDC), New Jersey Department of Environmen-tal Protection (NJDEP), New York City Department of Parks and Recreation (NYCParks)NY=NJ Baykeeper (BayKeeper), New York State Department of Environmental Conserva-tion (NYSDEC), New York State Department of State (NYSDOS) and Port Authority ofNew York and New Jersey (Port Authority). Source: Author generated graphic usingUCINET 6 (Borgatti, Everett, and Freeman 1999).

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The diminished capacity to form a denser network of new relationships isattributable not only to physical distance but also to the availability of resourcesembedded within the community. Following the argument that relationships are aproduct of investment into the relationship for perceived benefits—need of andavailability of resources (Bourdieu 1986; Coleman 1988)—it is fair to conclude thatthe access to and availability of resources influenced the formation of new relation-ships within the Habitat Workgroup. In comparison to the information exchanges inwhich it is easy to access (via telephone, e-mail, or face-to-face encounters) informa-tion embedded in the community, it is more difficult to exchange physical resources(i.e., office space and staff) and to partner on projects. In spite of these challenges,participating organizations did form new relationships and, as one New York Stateenvironmental agency representative stated, the participants valued the workgroupmeetings for providing opportunities for ‘‘communication and partnership.’’

The focus of the Habitat Workgroup’s initiatives was another factor impactinghow the organizations built new social capital. One outcome of the Habitat Work-group’s collaborative planning process was a priority list of habitat sites for acqui-sition and restoration, which guided habitat activities throughout the region.During the process to identify and nominate sites to the priority list, organizationscollaborated to share data on open-space plans and wildlife populations as well asto share staffing resources to conducted field investigations in order to collectscientific data required by the nomination form. In addition, when funds becomingavailable to purchase sites on the priority list sites new partnerships formed tofacilitate acquisitions. For example, BayKeeper played a very important role inNew Jersey by partnering with NJDEP and Port Authority as their local advocatereaching out to communities and citizens to match priority sites with NJDEP’sGreen Acres program and the Port Authority’s natural lands acquisition programfunds. Because the priority list was site specific it encouraged the sharing offresources and forming new partnerships on a geographic basis. These factorsaccount for the proximity of the Port Authority, BayKeeper, and NJDEP in thenetwork structure.

Funds Exchange NetworkWhile the structure of the funds exchange network is not as comprehensive as thepreceding two examples it does have a strong density, 46%, and a strong reportedincrease (36%) in relationships (11% remained the same, 2% decreased, and 52%did not exist). Similar to the analysis of the resource exchange network, these net-work data were analyzed using MDS of the similarities of funds exchanges, hierarch-ical clustering, and centrality. It is clear from Figure 4 that the distribution of ties isdensest around the U.S. EPA, the core figure in this network, and thinner around theperimeter of the graph. The centrality analysis revealed that the U.S. EPA is the onlyorganization with a complete network, with the vast majority of the exchanges beingmutual. Figure 4 also reveals and the hierarchical clustering analysis confirmed thatthe Port Authority and NRDC and to a lesser extent BayKeeper, all nongovernmen-tal organizations, are distanced from the denser grouping of governmental organiza-tions in the lower portion of the graph. In addition, the clustering analysisdemonstrated that the geography also had a strong influence; the samegeographic-based subsets appear in the funds exchange network.

The principle factor inhibiting the formation of a denser network of relation-ships was the scarcity of funding resources embedded in the community of actors.

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Interview responses showed consensus that the activities of the Habitat Work-group were constrained by lack of funding. In fact, several NGO representativesindicated that they lobbied up and down political channels to secure more fundsfor Habitat Workgroup initiatives. In spite of the lack of funds, more$100,000,000 has been allocated to the restoration and acquisition of sites onthe priority list (Mandarano 2004).

There are several funding programs that appear to have had the greatest influ-ence on the relationships that did result from this collaboration. The U.S. EPA’sNEP funds were dispersed annually to the administrator of the NYNJ HEP, theEPA regional office. NEP funding policy requires a 50% local match and organiza-tions participating in the NYNJ HEP provide matching funds in the form of in kindservices or financial contributions. Although the amount of U.S. EPA NEP fundingwas minimal, the funding policy is accountable for the EPA’s central location in thisnetwork. The other funds included New York State’s Environmental ProtectionFund and Clean Water=Clean Air Bond Act, which were administered by theNYSDOS to finance land acquisition, and waterfront and water quality improve-ment projects. The policy required that the funds be distributed to local New Yorkgovernments and thus contributed to partitioning the organizations into nongovern-mental and governmental. In addition, the policy required a 50% local match. How-ever, matching funds were not directed backed to the NYSDOS but were invested by

Figure 4. Funds exchange network. Acronyms: U.S. Environmental Protection Agency(EPA), Natural Resource Defense Council (NRDC), New Jersey Department of Environmen-tal Protection (NJDEP), New York City Department of Parks and Recreation (NYCParks)NY=NJ Baykeeper (BayKeeper), New York State Department of Environmental Conserva-tion (NYSDEC), New York State Department of State (NYSDOS) and Port Authority ofNew York and New Jersey (Port Authority). Source: Author generated graphic using UCI-NET 6 (Borgatti, Everett, and Freeman 1999).

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the local entity in site acquisition or restoration. As a result, the NYSDOS does nothave as many mutual ties as does the U.S. EPA.

With respects to the nongovernmental organizations in the network, theirproximity is related to their roles. For example, BayKeeper is shown within closeproximity to the governmental cluster because it not only acted as a broker tomatch sites with available funds but also brought funds into the network fromsuch external sources as the U.S. Fish and Wildlife Service (USFWS) coastal zonemanagement grants. The latter extended the buying power of NJDEP Green Acregrants. On the other hand, the Port Authority is positioned further away from thecore. Although it established a natural lands acquisition program to contributetoward buying priority list sites, the Port Authority does not distribute these fundswithin this network of organizations because it purchases land directly fromproperty owners.

Discussion

The goal of this study was to demonstrate the value of employing social networkanalysis in the evaluation of the effectiveness of collaborative planning at developingsocial capital in the form of new and improved inter-organizational relationships.Through an application of a series of social network methodologies the study demon-strated how participants in a NEP partnership were able to build upon common inter-ests to form new relationships and to improve existing relationships as well as toovercome differences to build relationships amongst organizations that had notworked together historically. The various methods employed in this study generatedquantitative measures that could stand alone as measures of social capital a criterionof collaborative planning success (Innes and Booher 1999) or could be used, as in thisstudy, in combination with qualitative analyses to uncover relationships betweensocial capital and collaborative planning, such as the specific aspects of the collabora-tive process that facilitated or constrained the formation of new relationships. Thisstudy also highlighted important benefits of using social network analysis in the eva-luation of social capital resulting from collaborative planning.

First, with respect to the evaluation of the range of interests represented by theHabitat Workgroup’s participants, the social network analysis provided richerinsight than a tabulation of the range of interests represented. While the latter isan indicator of partnership diversity, a key criterion of a stakeholder partnership(Leach and Sabatier 2005; Innes and Booher 1999; Wondolleck and Yaffee 2000),the evaluation of similarities of such interests delivers more information from a simi-lar data set. The graphic display of organizations by similar interests (see Figure 1)highlights the community’s structure and sheds light on which organizations sharesimilar interests that could be used as building blocks to form new and improvedrelationships and which organizations are removed from the network because of dif-ferences in interests. Such an analysis if undertaken in the early stages of a collabora-tive process could provide valuable information enlightening practitioners on theneed to facilitate constructive dialogue, research, or activities that attempt to findcommon ground between the most distanced stakeholders and other stakeholdergroups in order to bridge the differences in interests.

Second, this analysis revealed not only the community’s social network structureof direct relationships but also important insights on the relationship between struc-ture, collaborative processes, and resources embedded in the community. Although

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the general social structure showed the Port Authority as isolated because it did notshare interests with the greater community, the collaborative process included exten-sive dialogues concerning these differences in interests enabling the participants toforge meaningful relationships with the Port Authority. In addition, this studyshowed that accessibility and availability of resources within the communityimpacted the degree new relationships formed and network structure. For example,Habitat Workgroup participants may laud their ability to work collaboratively andto form new partnerships as a result of participation in the Habitat Workgroup, butthey may not be aware that they have experienced diminished capacity to form newrelations to facilitate implementation of their initiatives. This study generated power-ful information that, with a bit of insight, the Habitat Workgroup could use to revi-sit activities in order to encourage bridging the existing gaps in the resource exchangenetwork and in the case of the fund exchange network to make a case for additionalfunding or recruiting participation of other stakeholders with ties to funding sources.

Third, with respect to policy, the findings provide further evidence of thestrengths of the collaborative model encouraged by NEP legislation. While otherresearchers have shown that the NEP collaborative model encouraged the formationof trust and cooperative attitudes (Lubell 2004, 2005; Schneider et al. 2003), thisresearch demonstrates that the Habitat Workgroup was successful at establishinga collaborative process through which its participants formed new relationships—the infrastructure of social capital. These new relationships enabled the participantsto share information, resources, and funds, as well as enabled the Habitat Work-group to have an impact on regional habitat protection and restoration.

Finally, future research using social network analysis methods in combinationwith evaluations of the other dimensions of Putnam’s definition of social capitalcould reveal important information on the interrelationships between social capitaland successful collaborative planning outcomes. It is important to note that dueto the complexity of mapping interorganizational relationships, application of thisapproach is constrained to evaluations at the micro level. However, studies thatattempt to correlate network formation, structure, trust, and norms with successfuloutcomes would provide case study richness complementary to the broad scope ofmacro-level analyses.

Notes

1. The Policy Committee was comprised of the following representatives: U.S. EPA, RegionII Regional Administrator (Chair); NYSDEC Commissioner; NJDEP Commissioner;ACOE North Atlantic Division, Brigadier General; New Jersey Local Government Repre-sentative, City of Elizabeth; New York Local Government Representative, NYCDEP; anda CAC and STAC Representative. In addition, the Management Committee was comprisedof representatives from the following organizations: U.S. EPA, Region II (Chair), NYS-DEC, NJDEP, Interstate Environmental Commission, ACOE, NOAA, EPA Office ofResearch and Development, NYCDEP, New Jersey Harbor Dischargers Group Represen-tation, NY local government representative, NYSDOS, NJ local government representa-tive, U.S. Department of the Interior, Port Authority of New York and New Jersey,STAC Representatives (STAC Co-chairs), and CAC Representatives (CAC Co-chairsand two CAC members).

2. The survey prompted the respondent to indicate its organizations level of involvement (0—no involvement, 1—monitor activity, and 2—directly involved) in the following harbor-related interests: contaminated sediments; environmental education, public outreach, pub-lic stewardship; environmental policy, regulation, permits, and compliance; economic

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development; discharges to waterways; dredging, dredged material management; fishing,shell fishing; funding environmental projects; habitat conservation and restoration; hous-ing; land conservation; land use law; navigation, port activities; populations and healthof species; population growth; public access to waterfront=waterways; recreation; research;sea level rise, climate change; shoreline modification; stormwater management, floatabledebris; transportation; water quality; water usage, conservation; waterfront development;and watershed planning, protection, and management.

3. The network analyses of communication patterns interpret responses from 8 of the 10 com-pleted surveys because two data sets were incomplete.

4. For descriptions of social network analysis methods and applications see Social NetworkAnalysis: A Handbook by John Scott (1991) and Social Network Analysis: Methods andApplications by Wasserman and Faust (1994).

5. Although the Port Authority’s jurisdictional interests span New York and New Jerseylands and waters, the Port Authority’s largest marine facilities are in New Jersey.

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