bodin and crona 2008_social capital in natural resource mngmt

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Management of Natural Resources at the

Community Level: Exploring the Role

of Social Capital and Leadership in a Rural

Fishing Community

ORJAN BODIN and BEATRICE I. CRONA *

Stockholm University, Stockholm, Sweden

Summary. — Social capital and leadership characteristics are important in resource management.We present a case study of a fishing community showing high levels of social capital quantifiedthrough social network analysis, but low willingness to report rule breaking. Furthermore, identi-fied key individuals possess few links to financial institutions and important markets. These findingsmay, individually or in combination, explain the lack of common initiatives to deal with the over-exploitation of fisheries. Alternative hypotheses are also discussed and include homogeneity amongkey individuals leading to poor recognition of the problem of changing ecological conditions, andthe structural characteristics of their relational network, which reveal one person in a very influen-tial position.� 2008 Elsevier Ltd. All rights reserved.

Key words — social capital, social networks, natural resource management, coastal marine fisheries,agency, Africa

1. INTRODUCTION

Social capital is often suggested as having abeneficial effect on the capacity of individualsto organize themselves effectively (Coleman,1990; Fukuyama, 1995), and together withleadership, is often seen as crucial for the initi-ation and maintenance of environmental con-servation and management at the communitylevel (Olsson, Folke, & Berkes, 2004; Ostrom,2005; Pretty, 2003; Pretty & Smith, 2004). Thisstudy explores the aspects of social capital andleadership in a rural fishing community to seekexplanations for why collective action for sus-tainable management has not occurred, despitestrong indications of declining fisheries and in-shore habitat degradation, as well as increasingawareness of these problems among many fish-ermen and women (Crona, 2006; Crona & Bod-in, 2006; McClanahan, Glaesel, Rubens, &Kiambo, 1997; Ochiewo, 2004). The focus ofthis study is on natural resource management(NRM) at the community level. Thus, thisstudy touches upon concepts such as co-man-

agement (see overview in Carlsson & Berkes,2005) and adaptive co-management (Gadgil,Rao, Utkarsh, Pramod, & Chhatre, 2000;Olsson, 2003), often put forth as instrumentalin enabling sustainable NRM. Furthermore,in the context of fisheries social capital has beensuggested as an important factor affecting regu-lation and governance (Grafton, 2005; Sekhar,2007).

As shown above, the concept of social capitalhas been extensively cited as important for

* The authors would like to thank all respondents for

participating in this study, as well as Abdul Rashid for

his tireless work and organization in the field. The study

was conducted with financial support from Swedish In-

ternational Development and Aid (SIDA). Gratitude is

extended to the Kenya Marine and Fisheries Research

Institute, with which one of the authors (B. Crona) is

affiliated. Finally, we also thank Steve Lansing, Jon N-

orberg, Annica Sandstrom, and Maria Tengo for their

valuable comments during the development of this pa-

per. Final revision accepted: December 12, 2007.

World Development Vol. 36, No. 12, pp. 2763–2779, 2008� 2008 Elsevier Ltd. All rights reserved

0305-750X/$ - see front matter

doi:10.1016/j.worlddev.2007.12.002www.elsevier.com/locate/worlddev

2763


conservation and resource management. How-ever, its defining characteristics are multi-fac-eted (for review see, e.g., Krishna, 2002; Lin,1999; Woolcock & Narayan, 2000). First, theunit of analysis can vary from the individualto the group (Borgatti, Jones, & Everett,1998; Portes, 1998). For example, Burt (2004)argued that links to different groups may en-hance an individual’s social capital, whereasPutnam (1993) discussed social capital at thescale of whole countries. Second, there is a lackof agreement as to what actually constitutes so-cial capital. For example, Putnam (1993) de-fines social capital as ‘‘features of socialorganization such as networks, norms, and so-cial trust that facilitate coordination and coop-eration for mutual benefit.’’ Others suggest thatsocial capital can be defined as ‘‘resourcesembedded in a social structure which are ac-cessed and/or mobilized in purposive actions’’(Lin, 1999), thus leaving out collective assetssuch as trust and norms (although acknowledg-ing that factors such as trust may promote so-cial relations and vice versa).

Finally, social capital has been criticized forits lack of explanatory power, and several the-ses exist that differ primarily in their view of so-cial capital as either an exogenous or anendogenous variable. It is seen by critics as a re-sult of institutional performance rather than itscause, where independence cannot be verified.Adherents of this approach, represented by sev-eral disciplines (North, 1990; Schneider, Teske,Marschall, Mintrom, & Roch, 1997; Wade,1994), argue for reversed causality such thatthe existence of institutions explains social cap-ital. An intermediate position is taken byKrishna (2002), Berman (1997), and Dale andOnyx (2005), among others, who argue that so-cial capital has some explanatory potential butthat other factors also contribute to institu-tional performance and collective action. Onesuch factor is agency, which is realized throughthe existence of agents, that is, leaders or influ-ential actors, who activate a potentially latentstock of social capital and use it to produce aflow of benefits. In his extensive study in ruralIndia, Krishna (2002) found that the existenceof such leaders to mediate agency was necessaryto activate the stock of social capital and makeit productive in terms of economic develop-ment, community harmony, and democraticparticipation. Similarly, others have shownthe importance of leaders and sense-makersfor successful NRM, and the effect of goodleadership in this context is an expanding field

of research (e.g., Olsson, 2003; Westley & Vre-denburg, 1997).

Inspired by these findings, this study exam-ines the issue of social capital, agency, and col-lective action by applying an approach similarto the approach of Krishna (2002), althoughmodified to fit an East African NRM context.The study area is a rural fishing village alongthe southern coast of Kenya. The use of re-sources in the village is centered around a lowtechnology artisanal marine fishery, and tosome degree the use of mangroves for polesand firewood. A majority of households dependprimarily on fishing for their income, whilefarming and small scale businesses representalternative livelihoods for some. Fishermenare not a homogeneous group, however, butare grouped primarily based on gear type (Crona& Bodin, 2006) (Table 1). In spite of high levelsof resource dependence and the realization ofresource decline among many users, villagershave not been successful in regulating the in-shore local fishery. This lack of resource regula-tion is, unfortunately, not unique for thisparticular village but is rather common aroundthe world (Ostrom, 1990).

We substituted development (as defined byKrishna, 2002) with the ability of the commu-nity to initiate action for sustainable manage-ment of natural resources in light ofoverfishing and resource depletion. Agencywas approached from a social network perspec-tive by using structural network measures toidentify influential actors, based on the assump-tion that such measures offer a robust way ofidentifying these influential individuals in acommunity (for review see, e.g., Wasserman &Faust, 1994). Social capital was also ap-proached from a social network perspective.

Table 1. Fishermen and fishing techniquea

Type of fishery(at the household level)

Number of individuals

Gill net 10Speargun 3Handline 1Deep sea 43Kigumi 16

Total 73a The distribution of fishermen according to types offishing gear. In an earlier study (Crona & Bodin, 2006) itwas shown that the type of fishing gear strongly corre-lated with social ties, i.e., fishermen using the same typesof gear where also more socially connected to eachother.

2764 WORLD DEVELOPMENT


Thus we adhered to the large stream of scholarsusing network measures to assess social capital(see, e.g., Borgatti, 2003). Furthermore, mecha-nisms for conflict resolution and monitoringare often suggested as essential prerequisitesfor common property resource management(Ostrom, 1990), yet are rarely included inempirical studies of social capital. Here weincorporate these mechanisms into our assess-ment of community social capital, thereby com-bining the network approach (Borgatti et al.,1998; Lin, 1999) with the view of social capitalas also consisting of norms facilitatingcoordination and cooperation (Putnam, 2000).Figure 1 schematically presents our underlyingassumptions on the relationship of communitysocial capital, agency, and collective action tothe measured variables.

It is important to note that social capital isnot the only factor explaining the success orfailure of resource management in general andfor fisheries in particular. Contextual differ-ences among cases, such as culture, institutions,and type of fishery, will also play an importantrole. While not neglecting contextual influ-ences, this study has three primary aims: (1)to assess selected aspects of community socialcapital, as outlined above; (2) to identify poten-

tially influential actors; and (3) to assess if lackof community social capital and importantleadership characteristics, individually or incombination, may explain the lack of collectiveaction. The results are analyzed in relation tothe community’s lack of collective action withrespect to the regulation of natural resourceextraction. This paper is an attempt to furtheroperationalize the link between social capital,leadership, and agency in NRM research, andis one of the few studies to empirically quantifyaspects of social capital in an NRM context,utilizing social network analysis.

2. METHODS

(a) Study area

The area of focus in this study was a ruralcoastal fishing village located approximately50 km south of Mombasa in Kenya (Figure2), and is further described in Crona and Bodin(2006). The village has approximately 200households and an estimated 1000 inhabitants.Approximately 44% of households are directlyinvolved in fishery extraction, while manyothers benefit indirectly since fishery-generated

Collective action

SocialCapital

Agency

Social network characteristics

• Density• Fragmentation• Bonding/Bridging

Influential actors

• Socio-demographics• Linkages to external

resources• Relations among

themselves• Etc.

• Networkpositions

• Trusted parties & common procedures

Conflict resolution & monitoring mechanisms

Perceptions,knowledge and

capabilities

Figure 1. The figure illustrates schematically how different concepts (circles) and variables (boxes) are assumed to

relate to each other. The bulleted text describes how variables are assessed in this study. Only variables and concepts

discussed in this paper are outlined, although the authors acknowledge that numerous other variables may be of

importance in defining social capital.

MANAGEMENT OF NATURAL RESOURCES AT THE COMMUNITY LEVEL 2765


income is spent, to a large extent, in localstores. Thus we consider the majority of thecommunity members as dependent on marinefishery.

It is important to note that this area lies out-side the Diani/Chale National Marine Park andReserve. Consequently, the fishing groundsused by fishermen associated with this villagestretch further south and are not constrainedby this restricted access. Furthermore, thelong-term conflicts experienced among fishingcommunities and other industries in the Dianiarea have not prevailed in the studied commu-nity, as only a limited tourism industry hasdeveloped there to date.

The population of coastal Kenya comprisesof two main ethnic groups; the Mijikenda ofBantu origin and the Swahili who are of mixedBantu, Asian, and Arabic descent (King, 2000).The Mijikenda comprise nine tribes, of whichDigo is the predominant ethnicity of inhabit-ants in the study area. However, other coastaltribes such as Bajuni, historically associatedwith the Lamu region of the north coast, havemigrated south and are present in the studiedcommunity. Many Bajuni families have tradi-tionally been involved in mangrove cuttingand trading, as well as fishing, accumulatingsubstantial wealth, while the Mijikenda wereprimarily farmers but in the last century haddiversified their livelihoods to include fisheries,

which now constitute a substantial portion ofincomes (King, 2000). It is worth noting thatalthough a minority, Bajuni families constitutea disproportionate number of the households inthe upper income bracket in this community,while households of Digo and other Mijikendadescent constitute the large majority of thepoorest families. More recently, a third ethnicgroup has emerged in the community, account-ing for approximately 26% of households andconsisting of semi-migrant fishermen from theTanzanian island of Pemba, where they returnon a regular basis. This migration is linked toboth economic factors and kinship ties. Duringthe high season, migrating fishermen return tothe study area to fish and are often assisted withtravel expenses and permits by local middlemen(fishmongers) operating out of the village. Atthe same time, kinship ties play a significantrole in who is recruited to come along as crewfor the duration of the season.

The most dramatic influx of Tanzanian fish-ermen occurred after the 1964 overthrow ofthe Zanzibar-Pemba government, resulting inlarge seine crews establishing more or lesssemi-permanent operations along the Kenyancoast (Glaesel, 1997). The majority of Pembascurrently residing in the village primarily usering nets (a variant of purse seines), operatingfrom larger vessels and employing crews of upto 30 men.

Figure 2. Map of the study area with the target community indicated in the right hand corner of the inset. The area is

located on the southern Kenyan coast at 4�25 0S and 39�50 0E, approximately 50 km south of Mombasa.



(b) Methodological approach

This study assesses social capital at the levelof the whole village, which we hereafter denoteas the community social capital. Communitysocial capital is assessed by (1) quantifyingimportant characteristics of the community’ssocial networks (Borgatti et al., 1998), (2) eval-uating mechanisms in use for conflict resolu-tion, and (3) evaluating villagers’ attitudestoward self-monitoring and reporting. Thuswe adhere to Putnam’s (2000) definitions of so-cial capital by including both networks and as-pects of norms in our assessment. We base asignificant portion of the measures of socialcapital on the community’s ability to solve con-flicts and their willingness to impose self-moni-toring and sanctions. This was done since thefocus is on aspects of social capital enhancing

the ability for self-regulation of natural re-source extraction, and both monitoring andthe ability for conflict resolution have beenshown to be crucially important for successfulmanagement of common-pool resources (Os-trom, 1990). The ability to solve conflicts maybe viewed as an outcome of, rather than socialcapital itself (cf. Fukuyama, 1995; Putnam,2000). However, as our focus was to assessthe level of social capital, we did not differenti-ate between causes and consequences.

Structural network measures were used toidentify the most influential individuals of thecommunity based on network position (hereaf-ter referred to as key individuals). Assessmentof the key individuals’ characteristics and theirrelation to agency is based primarily on inter-view data generated using a modified version ofKrishna’s questionnaires (2002) to fit the cultural

Table 2. Network measurements and social capital a

Network measure Description Relation to social capital

Density oraverage linksper node

Network density is formally defined as thenumber of actual ties divided by thenumber of potential ties in a network.However, this study used the relatedmetric of average number of ties perindividual for ease of comparison withother studies

The number of links among the villagersindicates the overall level of cohesiveness inthe community and their capability of actingin common (Coleman, 1990; Granovetter,1973). In general, the more the relations thebetter in regards to the social capital,although there may be an upper limit abovewhich an increasing number of relations leadto excessive homogenization (cf. Bodin andNorberg, 2005; Oh et al., 2004)

Number ofcomponents

A measure of the extent to which thenetwork is divided into separate sub-networks (i.e., degree of fragmentation)

Indicates to which degree the community isdivided into separate (non-overlapping)sub-groups. Distances between members indifferent components are thus infinite. Wewere particularly interested in the number ofisolates (single-node components, i.e.,unconnected villagers) as well as the size ofthe largest component

Ratio betweenwithin-groupties andoutgoing tiesamong groups

The ratio between the number of tiesamong members of the same sub-groupand the number of ties between membersof different sub-groups in a given component

Captures the idea of bonding versus bridgingsocial capital (Pretty & Smith, 2004;Woolcock, 2001). Bonding ties are ties withinSub-groups of villagers which may maintain ahigh level of intimacy and trust (cf.McPherson, Smith-Lovin, & Cook, 2001),while bridging ties are relations betweenmembers of different such sub-groups.Both kinds of ties are important in enhancinga community’s social capital(Ancona, 1990; Granovetter, 1973;Volker & Flap, 2001)

a Network measures with descriptions and comments on their relevance in assessing the social capital of groups ofindividuals.



context of an East African fishing community.All interactions with respondents were con-ducted in Kiswahili.

(c) Assessment of community social capital

(i) Social networksThe network measures used are related to the

cohesiveness of the community, the degree offragmentation, and the ratio of bonding andbridging relations of the community’s socialnetworks. Cohesiveness is measured based onthe density of relations. The level of fragmenta-tion is measured by the number of isolated sub-nets (i.e., network components), indicating towhich degree the community is divided into iso-lated sub-groups. The ratio of relations thatfalls within versus between different sub-groups(Crona & Bodin, 2006) is also assessed. Rela-tions within and between sub-groups will fromhereon be referred to as bonding and bridgingties, respectively, to capture the idea of bondingand bridging social capital. These measures andtheir relevance in assessing social capital arefurther described in Table 2. The network un-der study was a combination of a social supportnetwork and an environmental knowledge net-work (Table 3). The social support networkwas chosen since it encompasses the strongest,most intimate social relations. The environmen-tal knowledge network was chosen due to itsimportance in developing a common under-standing of natural resources, which is essentialin enabling collective action in NRM (e.g., Os-trom, 2005). In the combined support andknowledge network, relations were based on

the existence of ties in either or both the socialsupport network and the knowledge network.Network raw data were gathered during datacollection for a previous study (Crona & Bodin,2006), but have not been previously analyzedfor this purpose.

(ii) Conflict resolution and monitoringThe institutions in place for conflict resolu-

tion were examined by measuring the extentto which villagers identify and utilize commonconflict-resolution mechanisms and procedures.Each villager was presented with a hypotheticalscenario in which they encountered a conflictthat they could not resolve themselves. We thenasked if, and to whom, they would turn for helpin resolving the issue. Furthermore, we mea-sured villagers’ willingness to monitor and, incase of rule breaking, report others. More spe-cifically, we presented each villager with ahypothetical scenario in which they observedsomeone breaking a recognized rule such asfishing regulations. We then asked if, and towhom, they would report this. Care was takento pose the scenario-based questions in thesame way to all respondents.

The reason these questions were asked wasthat these behavioral characteristics could beinterpreted as an indication of their respectfor common rules and practices as well as thesense of community expressed by villagers.We acknowledge that cultural differences be-tween researchers and respondents may affectthe interpretation of the questions asked, aswell as the respondents’ replies. However, theauthors have years of experience working in

Table 3. Network typesa

Network name Type of network Metric applied

Social support network Discussion of important mattersKnowledge network Exchange of information and knowledge

regarding natural resourcesCombined support andknowledge network

Network generated from a combinationof ties in either/both Social supportnetwork and Knowledge network

Degree-, betweenness-, andeigenvector centrality

Gear dependency network Dependency network, that is, who arerespondents dependent upon to carry outtheir occupation (e.g., lease of fishingequipment)

Degree centrality

Trade network Business network, that is, with whom dorespondents trade (buy and sell) theirproducts/catches

Degree centrality

a Types of social networks used in this study and their assigned names. In addition, the metrics used to construct thecriteria used to identify key individuals in each type of network are presented.



the case community and in Kenya; therefore,this issue should not have had any significantbearing on the results.

(d) Leadership

The first step in assessing agency and leader-ship was to identify the most influential individ-uals in the community. We choose to call themkey individuals, instead of leaders, since theconcept of leadership includes aspects otherthan merely the potential for being influential.

(i) Identification of key individualsKey individuals were identified based on their

structural positions in the community socialnetworks. The following points motivate thisapproach:

(1) The possibility for social influence andleadership is closely tied to a person’s struc-tural position in a network (e.g., Wasserman& Faust, 1994). Hence, by identifying keyindividuals based on their structural posi-tion, we were able to select the potentiallymost influential individuals.(2) By using these structural criteria, wewere not dependent on a few experts’ per-ceptions of who the most influential individ-uals are (Davis & Wagner, 2003), nor werewe limited to relying only on formal (autho-rized) leaders, but instead could focus on thekey individuals who were, indirectly, pointedout by the community through the networkstructure.

We identified key individuals based on theircentrality in the different social networks (Table3). Numerous studies agree that influence is clo-sely related to centrality, although the connec-tion is not unambiguous (Degenne & Forse,1999). However, a range of centrality measuresare available, all with their specific relation toinfluence and possibility for leadership. Sincewe did not want to determine key individualsbased on a single, narrow criterion, we choseto apply several network metrics. In all, weused five different criteria (Table 3). We useddegree centrality (see, e.g., Wasserman &Faust, 1994), betweenness centrality (Freeman,1979), and Eigenvector centrality (Bonacich,1972) for the combined social support andknowledge network (Table 3). Thus we wereable to account for three important, but sepa-rate, types of centrality for that specific net-work. The degree centrality in the Geardependency network and the Trade networkmade up the two remaining criteria. The reason

for only measuring degree centrality in theselast two networks is that a greater number ofties can be beneficial for an actor in these twotypes of networks, while the benefit of high de-grees of betweenness and eigenvector centralityis less obvious in networks of dependency andcommodity trading.

Key individuals were identified by assigningeach villager numerical scores according totheir rank for each of the five positional criteria(top position was given a score of 10, whereasranking below the top 10 list resulted in a scoreequal to 0). The total number of key individualswas limited to 10, representing 5% of the popu-lation. These individuals were the top 10 villag-ers ranked (in descending order of importance)according to (1) the number of times they oc-curred on the top 10 list in any of the criteria,(2) the number of times they scored highest inany of the criteria, and finally (3) their totalnumber of scores for all five criteria. Thus,for example, an individual appearing amongthe top 10 individuals for all five criteria wouldbe ranked higher than an individual who gothigher scores when accounting for all centralitycriteria, but only appeared on the top 10 listsfor four of the five different criteria.

In addition, our analysis also included twoformally appointed leaders who were not partof the 10 ranked key individuals—the villagesub-chief and the chairman of the beach com-mittee. The village sub-chief is assigned by thegovernment and represents the highest level offormal authority in the village, and the lower-most, grass-roots level of government. Thebeach chairman is the head of an elected com-mittee of fishermen which is a ‘‘semi-formal’’body to which fishermen are expected to turnregarding fisheries-related issues.

Finally, by extracting the identified 10 keyindividuals and the two formal leaders fromthe combined support and knowledge network(Table 3), we created a separate network (onlyconsisting of these individuals) in order to esti-mate their level of internal communication(Figure 3).

(ii) Key individual characteristicsThe methods presented above are primarily

concerned with extracting and analyzing quan-titative data gathered from the whole commu-nity. In order to collect more detailedqualitative data from the identified keyindividuals, semi-structured interviews wereconducted. All 10 key individuals as well asthe two formal leaders, the village sub-chief



and the chairman for the beach committee,were individually interviewed. Questions weredesigned to elicit responses on the perceivedpresent and future possibility of deriving a live-lihood from fisheries in the area. This was alsovalidated through questions on their perceptionof the state of the local fishery, fish popula-tions, and nearby mangroves. They were alsoasked about their opinion on the community’sability to manage their resources provided theywere given the appropriate authority. Theywere asked about their recognition of formaland informal village leaders and institutions,as well as conflict-resolution mechanisms. Fi-nally, they were asked about their personal con-nections with higher-level authorities, markets/suppliers, non-governmental organizations(NGOs), etc., and their willingness to utilizethese on behalf of other members of the com-munity. Basic attribute data (gathered previ-ously, see Crona & Bodin, 2006) such as age,level of education, tribe, and occupation ofkey individuals were also compared to the rest

of the community in order to assess to what ex-tent they represent the whole diversity of thecommunity, but also to identify common attri-butes among them.

3. RESULTS

(a) Assessing community social capital

(i) Social networksIn analyzing the previously gathered network

data (Crona & Bodin, 2006), we found that the172 respondents, constituting 83% of thevillage’s entire population of household heads,reported 634 ties concerning the discussion ofimportant matters and/or the exchange ofinformation/knowledge regarding naturalresources (social support network and knowl-edge network, respectively). This correspondsto an average of 3.7 ties per person. Thirteenvillagers, of which 11 were Digo and none werefishermen, reported that they did not have any

Figure 3. Network of social ties among key individuals. CM = Village chairman, DSF = deep sea fisherman,

MM = middleman (i.e., fishmonger), BC = beach chairman, RF = retired fisherman, BM = local businessman, and

SC = sub-chief. The formally appointed leaders are underlined. Note the central position of the village chairman, as well

as the fact that he is the only one connected to the sub-chief. Only one key individual is completely disconnected from the

others.



such ties with anyone else among the set ofrespondents (i.e., they were either connectedonly to people outside the village (10), and/orconnected to fellow villagers not being inter-viewed (2), or were isolated (1)). Two villagerswere confined to a separate subnet (i.e., a com-ponent), and the remaining 157 villagers wereconfined to a single large component. For thesocial support network alone, the average num-ber of ties per person is 1.3.

Estimates of the ratio of bonding (ties withinsub-groups) and bridging ties (ties betweenmembers of different sub-groups) are presentedin Table 4. In agreement with results from theprevious analysis of the knowledge network(Crona & Bodin, 2006), we defined the sub-groups based on the respondents’ occupation(Table 4). Furthermore, in this analysis we onlyconsidered relations related to the exchange ofinformation/knowledge regarding natural re-sources. Only occupations with more than threemembers are included in this analysis. Occupa-tions that were very broadly defined and conse-quently too heterogeneous to be perceived ascoherent categories were also excluded. Afterexclusions, 116 individuals remained (see fur-ther details in Crona & Bodin, 2006). Exceptfor the small group of farmers, bonding ties ex-ceed 50% for all groups. The highest fraction is,however, limited to 75%, which indicates that asignificant share of villagers’ relations is indeedwith members of a different sub-group.

(ii) Conflict resolution and monitoringOf the 172 interviewed villagers, only 11 did

not name any trusted third party they would

contact in case of being in conflict and notbeing able to solve it. Of all the reported thirdparties, we present the five most cited in Table5. The most cited person, the elected villagechairman, is followed by the government ap-pointed village sub-chief.

Seventy villagers stated they would reportothers to a named person, if encounteredbreaking a law. The five most cited personsare listed in Table 6. Of the remaining 102 vil-lagers who would not report others to anynamed persons 48 said they would either reportto the police (20), to their fishing captain (11),to those affected by the rule breaking (9), orthey would confront the person themselves(8). The remaining 54 villagers would not re-port others breaking the law to any kind ofauthority. It is important to note that reportingto the police is a daunting task in this rather re-mote village, so one can assume that only avery serious crime would result in a report. Fol-

Table 4. Distributions of social ties in the knowledge networka

Occupation Sizeb Rel./ind.c Rel./ind. in group (bonding ties)d Ratio in./out. (%) (bonding/bridging)e

Seine net 16 3.1 2.0 65Businessman 27 0.7 0.5 71Farmer 8 0.9 0.3 33Deep sea 45 3.6 2.7 75Gill net 10 3.3 1.8 55Middleman 10 2.0 1.0 50

a Distribution of self-reported, within-group relations among occupational groups (adopted from Crona & Bodin,2006). Note that the set of villagers (and the relations) presented in this table is limited to those belonging to the listedoccupations.b Size refers to the number of individuals within respective occupation.c Rel./Ind. refers to the total number of reported relations to individuals irrespective of their occupation, divided bythe number of members within the group.d Rel./Ind. in group refers to the number of reported within-group relations divided by the number of groupmembers (i.e., bonding ties per group member).e Ratio In./Out. (%) refers to the ratio of within-group relations versus all reported relations (i.e., the percentage ofbonding ties of that group).

Table 5. Citations for conflict solvinga

Person Number of timescited by villagers

Chairman 143Sub-chief 85Deep sea fisherman A 17Former sub-chief 5Member of elders’ council 5

a The top five persons cited as alters to whom membersof the community would turn for help in solvingconflicts. Note that each respondent could cite morethan one person.



lowing this assumption, 74 1 of the interviewedvillagers, that is, 43%, would not report rulebreaking unless very serious crimes were com-mitted.

(b) Leadership

(i) Identification of key individualsThe identified key individuals are presented

in Table 7, in order of centrality. As expected,the identified key individuals clearly stand outas more centrally positioned compared to theother villagers—a general pattern that reoc-curred for all five criteria. For example, thekey individuals have direct social ties to 80(49%) of the other villagers in the combinedsupport and knowledge network. If the re-ported contacts’ ties are also accounted for,key individuals are no more than two relationalsteps from reaching 132 persons, that is, 82% ofthe remaining villagers. Figure 3 shows thecombined support and knowledge networkamong identified key individuals only.

Table 6. Citations for reporting rule breakinga

Person Number of timescited by villagers

Chairman 40Sub-chief 14Former beach chairman 8Fisheries officer 6Deep sea fisherman B 4

a The top five persons cited as alters to whom membersof the community would report rule-breaking. Note thateach respondent could cite more than one person.

Table 7. Key individualsa

Rankorder

Leader attributes External contacts

Occupation Age Tribe Governmental agencies NGOs Finance Market/suppliersb

FSc FOd AGe

1 Businessman 48 Bajuni X2 Middleman 37 Bajuni X X3 Retired fisherman 76 Bajuni X X X X4 Deep sea fisherman and

captain32 Pemba X X

5 Chairman 59 Digo X X X X6 Deep sea fisherman and

captain36 Bondoi X X X X

7 Deep sea fisherman andmiddleman

51 Bajuni X X X X

8 Deep sea fisherman andcaptain

39 Pemba X X X

9 Deep sea fisherman 40 Pemba10 Deep sea fisherman 38 Bajuni X>10 Beach chairman and

Kigumi fisherman37 Digo X X X X

>10 Sub-chieff 41 Rabai X X X X

Sum ofcontacts

9 (75%) 6 (50%) 7 (58%) 4 (33%) 1 (8%) 6 (50%)

a List of identified key individuals and some of their attributes and links to external agencies which they may use forthe benefit of other villagers.b Most of the reported contacts with markets/suppliers were related to the provision of different types of fishing-related equipments.c Fisheries officials.d Forestry officials.e Administrative governmental.f The last two individuals on the list did not qualify for the top-ten list, as ranked based on centrality criteria, butwere included due to their formally appointed positions in the community.



(ii) Key individual characteristicsThe 10 identified key individuals have similar

levels of education, marital status, and religion(all are Muslim, as is the absolute majority ofall villagers) as the remaining villagers, andmost of them have resided in the village formore than 20 years. The only two significantdifferences between key individuals and othercommunity members are related to tribal mem-bership and occupation (see Table 8A and B).The Bajuni tribe is highly over-represented(50% of the key individuals, but only 12% ofthe villager population), while the Digo tribeis highly under-represented (10% vs. 49% ofthe village population). Furthermore, deep seafishermen are highly over-represented com-pared to other villagers (60% vs. 27%). Thiscould, although only partially, be ascribed tothe fact that the network of (environmental)knowledge exchange was used in identifyingkey persons; thus non-fishing occupations suchas local businessmen may have been discrimi-nated against. This does not, however, explainwhy there are no other types of fishermenamong these individuals.

The same skewed representation for tribeand occupation is also seen among the con-tacts of key individuals, although less pro-nounced.

In spite of several indicators showing a de-cline of the fish stocks in the area (McClanahanet al., 1997; Ochiewo, 2004), and the distinctawareness of this fact among many fishermen(and women) such as gillnet, spear gun, shrimp,and seine net fishermen (Crona, 2006), only twoof the 12 interviewed key individuals recog-nized that the current situation may jeopardizethe continuation of fishery-based livelihoods.One of the two was not even a fisherman. Thesetwo individuals with a more pessimistic view ofthe future fisheries referred to destructive fish-ing methods and the harvesting of under-sizedfish as the primary reasons for the current stateof affairs. Among the 10 more optimistic keyindividuals, two indicated that even though adecline in fish catches seemed apparent, newfishing technologies such as better boats andgears would improve the situation in the future.None of the key individuals had any plans tomove away from the village.

Only two were satisfied with the current man-agement of the fisheries. Major complaintswere related to the lack of regulation enforce-ment, but three persons also complained aboutthe ban of seine nets that was recently re-imple-mented by fishery authorities. These regula-tions are enforced without the provision ofany alternative resources such as loans to fish-ermen whose fishing gear became illegal orwas confiscated.

Ten of the 12 key individuals thought itwould be a good idea to designate more respon-sibility for fishery management to the village,and eight of them would also consider takinga leading part in such management efforts.However, only seven could recall any organiza-tional attempts internally initiated in the vil-lage. The two who did not believe intransferring more authority to the village citedthe selfishness of villagers and their inabilityto report each other.

Almost all key individuals (>90%) agreed onthe formal leadership in the village, and theyalso tended to identify the same set of informalleaders, although no single person mentionedall of them. Only one respondent expressedgreat discontent with the current leadershipand did not recognize any leaders at all. Allkey individuals reported the same proceduralsteps to take in case of fishing-related conflicts,and they also identified the same set of trustedpersons and authorities as presented in Tables 5and 6.

Three categories of links to external agencieswere investigated. These were links to govern-

Table 8. Tribe and occupationa

Tribe Key individuals(%)

Village population(%)

Panel A

Bajuni 50 12Bondoi 10 <1Digo 10 49Pemba 30 26

Occupation

Panel B

Deep seafisherman

60 27

Localbusinessman

10 16

Middleman(fishmonger)

10 5

Chairman 10 <1Retiredchairman

10 <1

a Tribal membership (A), and Occupations (B) of iden-tified key individuals (Key Ind.) and of the whole pop-ulation of respondents (Village Pop.).



ment agencies, NGOs, and financial institutionsand markets (Table 7). Between 50% and 75%of key individuals reported contacts with gov-ernment agencies, with the highest proportionof links directed at fisheries officials at the locallevel and fewer to representatives of the forestrydepartment and other administrative bodies(Table 7). Only 33% reported any contact withNGOs, and only one had any contact with afinancial institution. While a number of keyindividuals in the fishing occupation had tiesto markets, these consisted primarily of con-tacts for the acquisition of gear, as opposedto contacts for marketing/selling fish. The latterare normally handled by local middlemen.

4. DISCUSSION

(a) Community social capital

Social capital was assessed from a networkperspective as proposed by Borgatti et al.(1998). Average ties per person was 3.7 forthe combined social support and knowledgenetwork, versus 1.3 ties per person consideringonly the social support network. The last figurecan be compared with the outcomes of the 2004US General Social Survey (GSS), which gener-ated an average of approximately two ties perperson (SDA Archives, see http://sda.berke-ley.edu/archive.htm), and a similar study in ur-ban China (conducted in 1993) yielding 3.4 tiesper person (Ruan, Freeman, Dai, Pan, &Zhang, 1997). Despite slight differences in thephrasing of questions, as well as in cultural con-texts, these results are interesting to compare.At first glance, these figures indicate lower lev-els of communication compared to other stud-ies. However, we asked the heads ofhouseholds to report their ties to others outsidetheir households, while the other studies madeno such distinction. For example, approxi-mately 50% of the reported ties in an earlierUS GSS were to kin (Marsden, 1987). Thus itis fair to assume that a significant number of re-ported ties in both the US GSS and China wereto kin and, if accounted for, the difference be-tween our results and the other studies de-creases significantly. The comparison thenindicates that average numbers of reportedrelations in the social support network arewithin the same order of magnitude for the dif-ferent cases, although the average number ofties in our study area is likely in the lowerrange.

The analysis of network components showsthat only 10 (6%) of the villagers are uncon-nected relative to fellow villagers. The majorityof the community belongs to a large networkseemingly conducive to the formation of socialcapital according to the line of argument sup-ported by Putnam (2000) and Coleman(1990), where low levels of network fragmenta-tion are argued to enhance social capital byknitting together societies and by generatingtrust. Furthermore, 82% of the villagers(excluding key individuals) are within one ortwo relational steps from the key individuals,thus indicating that fragmentation is not a ma-jor issue for this village.

Bonding social capital describes the links be-tween people with similar objectives, whilebridging social capital describes the capacityof such groups to communicate with othershaving different views (Woolcock, 2001). Theratio between bonding and bridging ties in thecommunity knowledge network appears fairlybalanced, with bonding ties accounting for over50% of reported relations in all but one sub-group, yet never more than 75% (Table 4).The bonding/bridging ratio shows that ties be-tween members of different sub-groups, in thiscase groups based on occupation, provide forcommunication that spans the whole commu-nity, even though most ties exist within sub-groups.

The large majority of villagers specifying acontact to ask for help in conflict resolution re-ported the same set of trusted and/or autho-rized persons (Table 5). Thus it seems thatmechanisms are in place for solving conflicts,and that these are recognized by the majorityof villagers. It is interesting to note, however,that the local fisheries officer (not shown) re-ceived less than 1% of citations despite the factthat he is the formal representative of the gov-ernment in charge of all fisheries-related issuesand regulation enforcement.

Looking at social capital from the perspectiveof attitudes toward sanctioning and self-moni-toring, 59% of respondents state no specific per-son to whom they would report violations ofrules or laws, and 43% would not report a vio-lation at all or only if it was extremely seriousand required police involvement. It thus ap-pears that in regard to self-monitoring, andsubsequently sanctioning, a great part of thecommunity has adopted a rather ‘‘laissez-faire’’attitude. We acknowledge that the unwilling-ness to report others may instead be attributedto the presence of strong social capital, that is,



the villagers have developed a strong normwhereby they do not report rule breaking.However, such a norm would, within the con-text of NRM, restrain common pool resourcemanagement (cf. Ostrom, 1990), and would,therefore, work against Putnam’s (1993) defini-tion of social capital as being mutually benefi-cial for community members. We refrain frominterpreting non-reporting behavior as eitherstrong or weak social capital. However, shouldit be strong social capital, this could serve as avehicle for resource management provided thatthe norms were changed in favor of reportingrule breaking. If social capital, on the otherhand, was so strong that it made such changesalmost impossible, then it would serve as agood illustration of how social capital may infact counter resource regulation issues by pre-serving unsuitable norms and behaviors.

Summarizing the situation, it appears thatthe level of social capital depends upon whichaspect is under consideration. Network mea-sures indicate a potential for relatively high lev-els of social capital in the village. In addition, aconsensus exists among respondents regardingmechanisms for conflict resolution, yet there islow willingness to report rule breaking. Reluc-tance to report rule breaking could actuallybe reinforced by the coherent social networksspanning almost the entire village. In fact,many respondents stated that they would notreport rule breaking since it would embarrassthe offender, and they themselves would risk so-cial rejection. We turn now to an examinationof the identified key individuals as agents withthe potential to activate latent social capital.

(b) Agency and social capital

Krishna (2002) demonstrated that leadersplay a crucial role in activating social capitalfor the benefit of the community by providingvillagers with the know-how to maneuvrebureaucracy in order to benefit from govern-ment programs, and by facilitating collectiveaction through coordination and conflict reso-lution. In addition, from a resource manage-ment perspective, leaders can provide links toagencies assisting with information and educa-tion (government or NGOs) and act as thecoordinators of such efforts in order to maxi-mize their benefit and ensure their implementa-tion. The crucial importance of such boundary-spanning leadership in NRM has been identi-fied by several scholars in numerous cases(e.g., Olsson, 2003; Frances Westley, personal

communication). Cross-boundary links are as-sessed here by the ties to external agencies pos-sessed by identified key individuals (Table 7).The results show that they are fairly well con-nected to external agencies, with the exceptionof financial institutions and markets other thanthe trade of fishing gear.

Comparing these results with Krishna’s(2002) findings, two aspects of social capitaland leadership emerge as potential explana-tions for the observed lack of communal initia-tives in fishery regulation despite declining fishstocks: low willingness to report rule breaking,and the lack of external contacts related tofinancial institutions and markets beyond thetrade of fishing gear among key individuals.Our focus on NRM differs from that of Krish-na (who looked at community development),and it can be argued that the connection be-tween the lack of financial links and successfulresource management is not intuitively clear.However, the lack of financial links arguablylimits a leader’s ability to support the integra-tion of economic and/or market-related com-ponents into any initiative relating tocommon-pool resource management. Suchintegration may be crucial for the success ofthese initiatives (cf. Ostrom, 1990), for exampleby helping in providing investment capital foralternative sources of income and access tonew markets. At the same time, it must benoted that such links to financial agenciesmay be held by persons other than the inter-viewed key individuals.

Some comments on the limitations of usingKrishna’s (2002) findings to explain the lackof common-pool resource regulations in thestudied village are presented here. First, westudied a small rural East African village,whereas Krishna focused on rural villages in In-dia. Second, we used different methods andmeasurements to assess community social capi-tal, and we also used the data to explain aslightly different outcome variable (NRM vs.development). These differences in context andmethods obviously reduce the explanatorypower of Krishna’s theories in our analysis.Although we acknowledge these limitations,we argue that there are still enough similaritiesto make a comparison meaningful. Rural Indiaand rural East Africa are both developing re-gions, and despite using different methods toassess social capital, we argue that these mea-sures correlate. For example, a high density ofsocial relations is likely to correlate with indi-viduals’ degree of participation in village-re-



lated activities (measured by Krishna, 2002) (cf.Putnam, 2000). Similarly, we argue that the dif-ferent outcome variables are comparable sincedevelopment in rural and natural resource-dependent villages is closely linked with theircollective ability to manage these resources(WRI, 2005). However, it is worth noting thatthe lack of comparative studies of similar com-munities with different management outcomesalso decreases the ability to draw any generalconclusions.

(c) How well do key individuals reflect villageheterogeneity?

It is likely that the shortcomings regardingcommunity social capital and agency describedabove cannot, by themselves, fully explain thelack of resource regulation. Other factorspotentially contribute to the current situation.We propose and discuss some of these below.

One factor likely to raise the barrier for initi-ation of collective action is the lack of probleminternalization (cf. Adams, Brockington, Dyson,& Vira, 2003; Haro, Doyo, & McPeak, 2005).Although many villagers are distinctly awareof declining fish stocks (Crona, 2006), inter-views show that the vast majority of key indi-viduals either do not perceive the problem ofoverfishing, or else they do not take the intellec-tual leap and recognize this as a threat to futurelivelihood. This gap is likely an effect of theiroccupational homogeneity. All key individualswho are fishermen or are directly involved infisheries (i.e., all except the chairman, sub-chiefand a local businessman) are (or have been)deep sea fishermen. As such, they mainly fishoutside the reefs, generally use bigger boatsmoving over larger areas, and can relativelyeasily relocate their fishing efforts to areas fur-ther away. Thus a decline in fish stocks inshoreor in the near vicinity of the bay is not per-ceived as a major problem by them, contraryto fishermen targeting inshore species only (cf.Agrawal, 2002). Virtually no other type of fish-ermen is represented among the identified keyindividuals. Furthermore, deep sea fishermen(as a group) are centrally positioned in thecommunity network (Crona & Bodin, 2006).Possessing a central position in a network islinked to a greater ability to exert influenceand power, as well as coordinating action(Burt, 2003). Therefore, the current situationwill most likely present a barrier for other fisher

groups to initiate collective action. Thus, ourfindings suggest that to amass support for amanagement initiative, the number of peopleperceiving a resource problem is not the onlydeterminant of success but also relies on thesupport from influential actors.

From a broader perspective, homogeneityamong key persons is likely to reduce their col-lective ability to perceive and synthesize newinformation and knowledge of different kinds(see, e.g., Oh, Chung, & Labianca, 2004; Rea-gans & McEvily, 2003). As such it reduces theirability to adapt to new circumstances (e.g., thedecline of fish stocks), potentially contributingto lowering the community’s adaptive capacity(e.g., Berkes, Folke, & Colding, 2003) and theability to respond to change and disturbances.

(d) Coordination versus influence?

Another factor worth consideration inexplaining the lack of collective action is thenetwork structure among key individuals. First,it is interesting to note that neither the beachchairman nor the formally appointed villagesub-chief qualified for the top-10 list of identi-fied key individuals (through centrality crite-ria), whereas the village chairman rankedfifth. In fact, neither the village sub-chief northe beach chairman appeared among the 10most central individuals for any of the five cen-trality criteria. Thus the sub-chief, as the onlyleader in the village formally appointed by thegovernment, appears quite loosely attached toinformal networks of communication ofimportant matters and/or the exchange ofinformation/knowledge about the natural envi-ronment. In contrast, the village chairman,who is elected but unauthorized in terms of offi-cial authorities, is firmly embedded in the vil-lage social networks. In terms of informationtransfer, the chairman thus has a central andpowerful position and is the only link to thesub-chief (Figure 3). This may not be unusual,but it creates a situation whereby the chairmanobtains a lot of power in the sense that he can(1) decide which issues to bring forward to thesub-chief (i.e., setting the agenda), and (2) be-come a block for information flow and agencyif he does not perceive the issue at hand asimportant, or if it conflicts with his personalinterests.

From a different perspective, possible benefitsof the current structure include the fact that ini-tiation and coordination of action can be



greatly enhanced because the chairman is firmlyembedded and centrally positioned both in thecommunity social networks and in the networkof key individuals. He can thus act on behalf ofthe villagers, vis-a-vis the sub-chief, by a directlink; that is, he has the ability to link the wholecommunity to external authorities. However,the chairman may instead be constrained inhis capacity to act by these numerous social tiesif a consensus for a course of action is notreached among his reported contacts (cf. Frank& Yasumoto, 1998). For good or bad, the com-munity is seemingly highly dependent on thechairman for initiating collective action of anykind. Vulnerability, or reduction of resilience,lies in this dependency and in the impact thatthe personal characteristics and interests of asingle person have on prioritization and deci-sion-making. This is perhaps an inevitableside-effect associated with boundary-spanningleadership, an issue that should be accountedfor when arguing for the benefits of this kindof leadership.

5. CONCLUSION

This study shows that levels of communitysocial capital can differ, depending on whichaspect is under consideration. While socialnetwork measures indicate relatively high lev-els of social capital, reluctance to report rulebreaking was high. This reluctance couldactually be reinforced by cohesive social net-works and potentially counter-balance theformer. The study also identified key individ-uals and showed them to possess links to anumber of external agencies, although witha marked shortage of links to financial insti-tutions and markets beyond the trade of fish-ing gear. Comparing these results with similarstudies (Krishna, 2002), identified shortcom-ings may help to explain the lack of commoninitiatives in the village to deal with the over-exploitation of fisheries. However, if we lookinstead at the identified strengths, it is inter-esting to note that relatively high levels of so-cial capital and the existence of variouslinkages among key individuals to differentgovernmental authorities have not been suffi-cient for initiatives regarding resource man-agement to occur.

Other factors that are likely to influence col-lective action for resource management werealso identified. First, a marked homogeneity

of key individuals in terms of occupationand ethnicity appears to be related to poorproblem internalization and recognition ofchanging ecological conditions within thisgroup. Second, structural characteristics ofthe social network among the key individualsshowed that one person occupies a very cen-tral position and possesses the only link tothe formally appointed sub-chief. It is arguedthat these factors, respectively, affect resourcemanagement (1) by imposing a social barrierfor management initiatives and reducing keyindividuals’ ability to perceive and synthesizenew information and knowledge, potentiallyreducing the community’s ability to adapt tonew circumstances; and (2) by the inherentvulnerability in depending on a single personfor connections to formal government officials,as well as the impact that the personal charac-teristics and interests of a single person haveon prioritization and decision-making. How-ever, it is recognized that benefits may alsobe derived from the latter ‘‘bottleneck’’ byfacilitating coordination of collective actioninitiatives.

These findings support the idea that effortsdirected at enhancing NRM at the communitylevel should take several aspects of social cap-ital into consideration. Furthermore, leader-ship characteristics such as links to externalagencies, homogeneity of the most influentialindividuals, and structural characteristics ofthe social network among these individualsmay also contribute to a community’s poten-tial for management of natural resources. Weargue that the methods applied in this studyextend the framework developed by Krishna(2002) in a way that enables further investiga-tion of the role of social capital and agency inNRM by local communities. In particular, byusing methods based on structural characteris-tics of the community’s social networks, wewere able to (1) identify the most influentialindividuals without having to rely on the so-called informed experts, and (2) provide forless context-dependency, thus making it easierto compare different cases worldwide. The lat-ter is crucial in researching possible general as-pects of social capital and agency, and theirroles in successful community-based NRM.By using a structural network approach, it isalso easier to communicate findings to abroader audience of researchers who are inter-ested in developing the concept of socialcapital.



NOTES

1. This figure was arrived at by taking into account thenumber of respondents who did not report any specificperson (102) and then subtracting those who reported

either to (i) the fishing captain (11), (ii) to those affectedby the rule breaking (9), or (iii) to those who wouldconfront the rule breaker themselves (8).

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