Revenue Sharing and Social Capital in Community-Based Resource Management: Empirical Evidence from Japanese Surf-Clam Fisheries

1 Introduction

Globally, capture fisheries produce 90 million tons of food annually and provide employment for 40 million people (FAO 2018). At the same time, overfishing has damaged fisheries around the world since the late 1970s and overfished stocks have been steadily increasing (FAO 2018) despite some progress made in developed countries that have stepped up implementation of management measures. Significant challenges remain in nations in which the centralized institutions are weak. Fishery cooperatives, in which fishers collectively self-manage the fishery, have been garnering much attention from regulators and academics as a way to complement or supplement existing fishery management systems in nations that do not have adequate governmental institutions (Townsend, Shotton, and Uchida 2008; Pinkerton 2011; Deacon 2012; Emery et al. 2015; Emmett Environmental Law & Policy Clinic and Environmental Defense Fund 2016). Understanding how and why fishery cooperatives succeed could help resource users and policy makers design a mechanism for applications in other contexts, in which local governance is more important than central (Deacon, Parker, and Costello 2008; Deacon 2012; Holland et al. 2013).

In this article, we hypothesize that two factors interact to affect the success of fishery cooperatives: (1) management rules adopted by the cooperative regarding revenue sharing and (2) the cooperative’s stock of social capital. Revenue sharing is a type of management rule employed in fishery cooperatives in Japan, a nation with a long history of community-based fisheries management (Platteau and Seki 2001). As implemented in Japan, revenue sharing typically involves harvesters who share their catches and divide the resulting revenue regardless of how much each harvester contributes, which, in theory, reduces excessive fishing effort to a more efficient level (Gaspart and Seki 2003; Heintzelman, Salant, and Schott 2009). Social capital describes the degree of trust, cooperation, and reciprocity among people and the norms and networks in a community that can be leveraged to improve outcomes for community members (Fukuyama 1996; Putnam 2001).

Social capital has been associated empirically with economic productivity (e.g., Knack and Keefer 1997; Carter and Castillo 2002; Karlan 2005; Bouma, Bulte, and van Soest 2008; Barr and Serneels 2009).

We diverge from the theoretical literature on shirking (Gaspart and Seki 2003; Heintzelman, Salant, and Schott 2009) and focus on its interaction with social capital, which supports value-enhancing cooperation as a mechanism through which revenue sharing attains efficiency. While revenue-sharing arrangements can motivate some members of the group to free-ride on others’ fishing efforts (Gaspart and Seki 2003; Heintzelman, Salant, and Schott 2009), they also can provide an incentive for individuals to contribute to collective tasks. This synergistic effect aligns individual self-interests with the group’s interest (Sherstyuk 1998). Revenue sharing is particularly relevant for community-based management of a fishery, which often involves engaging in enhancing the fishery resource as a group (Platteau and Seki 2001; Uchida and Baba 2008). Resource enhancements are defined as any effort performed as a group to increase the harvest performance of a fishery and includes stock enhancement, rotation of fishing grounds, coordinated searches for concentrations of fish, and joint use of fishing boats and gear. Several prior studies have discussed the importance of resource enhancement in revenue-sharing groups, but no studies have empirically examined how social capital could mediate such enhancement (Platteau and Seki 2001; Uchida and Baba 2008). The role of social capital in a community that self-governs a shared resource has been highlighted in prior works (Bowles and Gintis 2002; Pretty 2003; Ahn and Ostrom 2008; Gutiérrez, Hil-born, and Defeo 2011), and, in fact, Carpenter and Seki (2011) showed a strong correlation between fishers’ propensity to cooperate and their fishing productivity.

In this article, we highlight the effects of social capital under several types of management rules in which revenue is and is not shared and hypothesize that the interaction between revenue sharing and social capital is bilateral: revenue sharing builds social capital in a community by fostering cooperative interactions among fishers, and social capital strengthens incentives to improve the group’s efficiency by outweighing the influence of individual benefits from free-riding.

Using data from a survey of fishers in community-managed shellfish fisheries in Japan, we find that revenue sharing and social capital affect the outcomes of a fishery by influencing the degree of cooperation in harvesting and resource enhancement, which connects the path from our variables of interest to the fishery outcomes. We observe that revenue sharing alters the incentives in the fishing group. All of the revenue-sharing fishers in our sample coordinated their fishing operations—whether and potentially where to fish on a particular day—while those in groups that did not share revenue did not coordinate their efforts. Revenue sharing in the presence of social capital further improves the effect of resource enhancements by changing the incentive to cooperate in those efforts in addition to fishing operations, leading to efficient use of resources in the long run. Contributions to resource-enhancement efforts align with individual self-interests when a group of harvesters shares revenue, and harvesters are devoted to collective value when social capital binds the members in a community and fosters cooperation.

Using data collected in Japan, this article formally tests the separate effects of revenue sharing and social capital on four fishery outcome measures: ex-vessel price per kilogram (group-level economic outcome), resource stock density per square meter (group-level biological outcome), fishers’ self-reported perceptions of economic success (individual-level economic outcome), and resource conditions perceived by fishers (individual-level biological outcome). In addition, this article tests the pathways through which revenue sharing and social capital interact to affect the outcomes indirectly. Combined, these analyses provide a novel measurement of the effects of revenue sharing and different levels of social capital in a community-managed fishery and identify how revenue sharing and a community’s social capital affect fishery outcomes. We further examine the effect of revenue sharing on social capital in terms of whether revenue-sharing arrangements affect the size of a group’s information network and degree of cooperation.

2 Conceptual Framework

Revenue sharing results in conflicting incentives for fishers: free-riding on the efforts of others versus maximizing the collective value of the fishery (Kandel and Lazear 1992). Theoretically, free-riding on others’ fishing efforts is the dominant motive in a sufficiently large organization because a 1/N share of the marginal return from fishing is usually less than the cost of effort. However, given that the total effort is often overexerted in the use of common resources, shirking behavior can be beneficial in curtailing the effort level. If overshirking is counteracted by maintaining a certain degree of competition via status-seeking motives by some members of the group (Gaspart and Seki 2003) or intragroup competition in which more than one revenue-sharing group pursues the same resource (Heintzelman, Salant, and Schott 2009), the “right” level of shirking results in the effort level that is close to the social optimum (Gaspart and Seki 2003; Heintzelman, Salant, and Schott 2009).

In contrast to these previous studies, this article explores the possibility that revenue sharing increases collective value because it presents incentives that affect both how fishers operate and, separately, the effectiveness of efforts to enhance the value of a fishery.

In other words, revenue sharing leads to intentional activities by fishers to improve the collective value of the fishery, and increased efficiency comes not from shirking but from greater cooperation between resource users. This mechanism was shown to be key in a partnership structure that resembles revenue sharing (Sherstyuk 1998).

Furthermore, many efforts for cooperative fishing and fishery enhancement exhibit a synergistic effect; the more effort devoted as a group, the better, and greater production can be achieved than by individuals working solely in their own interests. This is especially the case when resource-enhancement efforts need to be coordinated with harvesting efforts, which are private actions in the absence of revenue sharing. Resource-enhancement efforts are public or club goods for all fishers. By aligning individual self-interests with a group interest, revenue sharing supports an incentive to cooperate in fishing and contribute to resource enhancements, leading to production efficiency.

We analyze three hypotheses linking revenue-sharing and fishery outcomes. The first hypothesis is that revenue-sharing arrangements directly improve management outcomes by coordinating a group’s fishing efforts and resource-enhancement efforts more efficiently than in groups without revenue sharing (see Figure 1a). This study compares revenue-sharing groups with groups with no revenue sharing that impose individual fishing quotas, so a difference in performance is not likely to be the result of shirking of effort. We thus can focus on the incentive of revenue sharing to maximize collective value.

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figur 1

Structure of the Hypotheses: (a) Hypothesis 1, (b) Hypothesis 2, (c) Hypothesis 3

Our second hypothesis is that social capital, which includes cooperation, trust, and the extent of the information network, directly improves outcomes by uniting fishers at all levels of incentives (Figure 1b). Social capital also can sustain individual fishers’ contributions to enhancing the fishery as well as improve the efficiency of the enhancements. Though many community-managed fishery groups engage in some form of enhancement, the amount of time and effort devoted in a group and by each individual within a group affects their effectiveness. For example, one possibility is that each harvester contributes to enhancements rather than free-riding because of a moral imperative or to enhance their reputation among fishers, which counteracts the incentive to free-ride in a public good situation (Brekke, Kverndokk, and Nyborg 2003; Gaspart and Seki 2003; Bénabou and Tirole 2006). Social capital, trust, reciprocity among members, information sharing, and social norms might also affect support for individuals’ motivation to make sincere commitments and contributions to the group interest. Information sharing also can further increase the efficiency of resource enhancements.

Our third hypothesis posits that the effects of revenue sharing and social capital interact, reinforcing each other (Figure 1c). Identifying the pathways that connect revenue sharing to social capital (and social capital to revenue sharing) and to economic and biological outcomes of a fishery through the enhancements is a novel and critical approach to understanding the contexts in which cooperatives can be effective. Social capital can affect the likelihood of a group adopting revenue sharing and can induce collective efforts by the group’s members to create value, allowing the group to enhance a fishery more effectively than groups that have less social capital. As previously noted, the interactions go both ways. However, we specifically test unidirectional relationships because of the nature of our variables and anecdotal evidence, as noted in the section on our empirical framework.

3 Japanese Surf-Clam Fishing in Hokkaido Prefecture

To test our hypotheses about the role of social capital in mediating the effectiveness of revenue-sharing arrangements, we collected data from Japanese fishery cooperative associations (FCAs), one of the oldest cooperative management cultures in the world. The FCAs govern territorial use rights for Japan’s coastal resources, and any entity that conducts commercial fishing in coastal waters must belong to the local FCA. The FCAs enforce both national and prefectural regulations and self-regulate local resources, which allows them to tailor restrictions and requirements to their local conditions and social and economic objectives. Within an association, many groups of fishers are formed, based mainly on the species they target or the fishing gear they use. Each group has its own rules to regulate and manage their type of fish or gear and can choose to share revenue with group members.

No fisher in an FCA can fish independently; every member must operate as part of an existing group. A primary function of FCAs is to provide a formal social safety net as insurance against severe reductions in the members’ incomes. Thus, there is no reason for any fishers in Japan to pool their revenue solely to mitigate risk (Platteau and Seki 2001).

We chose the Japanese/Sakhalin surf-clam (Pseudocardium sachalinensis) fishery, known as Hokkigai in Hokkaido Prefecture, for this study. Hokkigai is harvested by a large number of local groups, and an approximately equal number of groups do and do not share revenue. Otherwise, they are relatively homogeneous in terms of their operational rules, including use of enhancements. Focusing on a particular region and carefully selecting groups based on preliminary data rather than on outcomes allows us to control many observed and potentially unobserved characteristics at the time of sampling (Ho et al. 2007).

The harvesting technology used is an important factor to control at the time of sampling. The Hokkaido government requires fishers to use jet dredges, imposes a minimum catch size of 7.5 cm, and closes fisheries during the spawning season. In addition, many FCAs in Hokkaido impose total allowable catch (TAC) restrictions, control landing volumes, and require efforts to enhance fish stocks, a key element in our study. Individual skippers are required to cooperate with the FCA’s regulations, including stock enhancement, but their degree of effective cooperation varies.¹

The FCAs in Hokkaido use the same structure to organize their shellfish fisheries. Stock assessments are conducted by prefectural government scientists at Fisheries Extension Offices located across coastal Hokkaido who collaborate with local skippers and the FCAs prior to or after every fishing season. All skippers are called to a preseason meeting at which the local Fisheries Extension Office reports on the results of the most recent stock assessment, which is used to determine the season’s TAC, and reviews the operational rules and policies for the upcoming season. Then, during the season, the leader and subleaders of the groups closely watch market prices (mostly by talking directly to the middlemen in the market) and decide daily whether the groups will fish and, if so, how much they can land that day based on the seasonal TAC. Upon a leader’s decision to allow fishing, individuals who do not share revenue can still choose whether to actually go fishing and what time. Revenue-sharing skippers depart from and return to the port together. The daily TAC eliminates the incentive for fishers to compete for the volume of fish caught regardless of whether they share revenue. Finally, during or after the season, all of the skippers in all of the surf-clam FCAs (regardless of use of revenue sharing) are required to contribute to enhancement activities to make the fishery favorable for the clams. How much each FCA must contribute varies. The enhancements include cultivating ocean beds, removing predators such as starfish, and transplanting clams into the fishery. Many FCAs also buy seed clams from other fishery groups and release them in their waters.

4 Data

The data for empirical testing of our hypotheses were collected using (1) controlled economic experiments conducted with members of fishery cooperatives for measures of individual and group cooperation, (2) a survey of fishers to measure individual and group social capital and outcome variables, (3) a survey of FCA staffs to measure the variables for group operation, and (4) secondary data from the FCAs to measure group outcomes and control variables.

The controlled economic experiments with fishers as subjects were conducted from fall 2013 through spring 2014. The fishers were recruited from 10 FCAs, 5 of which shared revenue and 5 of which did not and never had. They all operated in small-scale trawl fisheries targeting Japanese surf clams and were located in the same or adjacent regions in Hokkaido Prefecture (see Appendix Figure A1). Thus, the 10 FCAs shared the same market, biological conditions, and historical backgrounds. The FCAs differed only in whether they employed revenue sharing along with collective fishing operations as major characteristics of their management (see Table 1); however, the degree of self-regulation can also differ across the FCAs.

The participants in the experiments completed a survey collecting information on their demographic characteristics, perceptions of the management outcomes, their trusting attitudes, and the size of their information networks with other fishers, such as the number of fishers with whom they exchanged information (see Table 2). Comparing the FCAs that shared revenue with those that did not, we find significant differences in average age and household size (p -values < 0.05 in a Mann-Whitney-Wilcoxon test) but not in experience in shellfish fishing, which is likely to be one of the most important factors affecting social capital.

Using the economic experiment and survey, we created a unique dataset containing group-level information for the 10 FCAs in panel format for 1990 through 2012 and individual-level information on 79 skippers (independent fishers not hired for a fixed remuneration by a captain) in the FCAs in cross-sectional format. Two of the five FCAs labeled as revenue sharing in our study were not sharing revenue at the beginning of that period but switched between 1990 and 2012. Their shifts to revenue sharing provided information on the groups’ performance pre-and post-revenue sharing, which facilitated identification of the effect of revenue sharing on outcomes but not necessarily of the effect on social capital, since we collected data only on their current levels of social capital. The other groups had been under the same management system throughout that period. And all of the FCAs were performing other relevant fishing practices such as stock assessments, establishing TACs, and transplanting and stocking surf clams throughout the period.

Information Network and Trust Indices

We further measure social capital by capturing the extent of individuals’ information networks and degree of trust using responses to the survey. Based on Holland et al. (2013) and Holland, da Silva, and Kitts (2015), we constructed the measures of the information network a skipper has in the shellfish fishery (see Table 2). A survey question asked the participants to provide the number of shellfish fishers with whom they shared important information that could potentially affect their own profits during the 2012 fishing season. Among skippers, the average network consisted of 10 other skippers. We also calculated the coverage of the information networks by normalizing the individual networks’ sizes by the possible number of relationships (the size of a fishery group from the FCA data). On average, the information networks represented about 30% of total membership but ranging from 0% to 100%. In the case of the fishery group composed of 10 fishers, for example, 30% coverage indicates that an average fisher would have shared important information with 3 other fishers.

The social capital represented by an information network can also be measured by the value of the information shared. Based on information provided by FCA staff, six kinds of information were identified as important in the surf-clam fishery: market conditions, buyer information, specific fishing hot spots, the market for bycatch and its hot spots,⁶ areas having a high density of boats and gear, and type of boats and gear. Respondents were asked to list which of these kinds of information they had shared during the preceding year. Taking the average of the number of kinds of information shared by skippers and the size of their information networks, we generated an index for varieties of information shared. The average skipper shared two types of information. The most commonly shared information was specific fishing hot spots (61% of the information networks), followed by market information (56%; see Figure 2). This suggests that decisions regarding where to fish are one of the most important drivers of successful catches in these fisheries. Also, as previously discussed, the survey responses reemphasized the importance of knowing about changes in markets, the second most commonly shared information.

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figur 2

Content of the Shared Information

Note: Observations = 229 and number of individuals = 61.

Information on boats and gear is also important in these fisheries. Many of the FCAs require skippers to share boats and gear. The fishers tend to regard other fishers with whom they share information as being close friends (49%) and having in common boat and gear (33%).

The survey further asked participants to report how frequently they shared each type of information with members of their networks using a Likert scale of 1-7 in which 1 indicated “as frequently as every day” and 7 indicated “once during the season.” The average skipper shared important information with other skippers at least once a week during the season.

The trusting attitudes of skippers were measured using two survey questions from the General Social Survey (GSS) rated using a five-point Likert scale: “These days you can’t count on strangers” and “In dealing with strangers one is better off to be cautious until they have provided evidence that they are trustworthy” (see Table 2).

The resulting data on participants’ social capital allow our hypotheses regarding the interaction between revenue sharing and social capital to be translated into measurable terms (Figure 1c). Members’ sense of cooperation is an outgrowth of their everyday interactions with each other at sea and thus is affected by revenue-sharing arrangements. We hypothesize that revenue sharing fosters cooperation among fishers and that greater cooperation further reinforces coordination in the fishery, resulting in improved economic and biological outcomes. We further hypothesize that exchanges of information and the existence of an information network are closely related to how fishing efforts are managed. Once revenue sharing was introduced in a fishery, the individual fishers’ efforts were coordinated for the benefit of the group, suggesting that revenue sharing influences the formation and size of information networks. We hypothesize that revenue sharing provides fishers with an incentive to share information in such networks, which affects the efficiency of coordination and improves outcomes. Trusting attitudes, on the other hand, are constructed in more general contexts that are not bounded by the fishing community. Therefore, we hypothesize that a general trusting attitude in a community leads to revenue sharing, which further assures coordination of fishing efforts and better outcomes.

5 Empirical Model

To test our main hypotheses, we use a random-effects model with the following specification: where i indexes the groups and t is years. Each group i contains T_i observations, which sum to N. Outcomes are denoted y_it and are the price per kilogram or the resource stock density in grams per square meter. The variable of interest is X_it, either the revenue-sharing indicator or one of the time-invariant social capital parameters. There are L control variables that are different for each outcome and denoted Z_itl ; u_i is the random, time-invariant parameter specific to the ith group; and α is the mean of u_i. When the outcome variable is one of the individual-level measures derived from survey responses, the data are cross-sectional and all of the it subscripts are replaced with ij subscripts identifying individual i in group j. We use the ordinary least squares estimator with clustered standard errors.

We controlled for several important observables for the fishery groups in our study at the time of sampling such as target species, technology, and region. Other relevant characteristics of group operations are controlled in the regression in addition to time-invariant unobserved characteristics by exploiting a panel structure. It is important to note that, since the only way to fish in an individual’s community is through the FCA and the FCA determines whether revenue will be shared, the choice of the key revenue-sharing institution is exogenous to the individual (Carpenter and Seki 2011)

We apply the wild cluster bootstrap to estimate standard errors because cluster-robust standard errors are less reliable when applied to data containing a few clusters and time-invariant variables of interest within a cluster (Bertrand, Duflo, and Mullainathan 2004; Donald and Lang 2007; Angrist and Lavy 2009). Asymptotic justification of cluster-robust standard errors relies on the assumption that the number of clusters goes to infinity, and data with only 10 clusters (FCAs in our case) do not meet that assumption.

Several solutions for data with a small number of clusters have been proposed, and the wild cluster bootstrap analyzed in Cameron, Gelbach, and Miller (2008) is the most appropriate in this study. Unlike standard bootstrap methods with cluster options commonly implemented by statistical software, the wild cluster bootstrap forms pseudosamples based on the residuals and uses a statistic in which the asymptotic distribution does not rely on unknown parameters. While the standard bootstrap directly evaluates the distribution of the ordinary least squares estimates, the wild cluster bootstrap forms Wald statistics for every pseudosample and evaluates the distribution of those statistics. In cases involving just a few clusters, this feature is crucial. A cluster bootstrap based on pairs of a dependent variable and an explanatory variable has a good chance of replicating the same pseudosamples when the explanatory variables do not vary within a cluster. Thus, the wild cluster bootstrap avoids this problem by basing its sampling on residuals. We extend this bootstrap to the generalized least squares estimator,⁷ and we present bootstrapped p -values using the wild cluster bootstrap t -method to provide for better inferences.

6 Results

We are interested in estimating the effect of revenue sharing on four measures of fishery management outcomes (hypothesis 1), the effect of social capital on fishery management outcomes (hypothesis 2), and assessing whether an interaction between revenue sharing and social capital improves fishery management outcomes (hypothesis 3) through several specific pathways. Table 3 provides our estimates for Real prices and Stock densities outcomes, and Table 4 presents our estimates for Perceptions toward economic outcomes and Perceptions toward biological outcomes. Both tables contain a selected set of the estimation results, and complete results are provided in the Appendix.

The first striking result is that the effect of Revenue sharing is consistently insignificant across the four outcome measures (columns 1 and 8 in Table 3 and columns 1 and 13 in Table 4). Thus, hypothesis 1, that revenue-sharing arrangements improve the individual and group outcomes analyzed in this study, is rejected. Second, we find that some of the social capital parameters positively affect the outcomes and, thus, that hypothesis 2 holds for some aspects of social capital but not all. To test hypothesis 3 and determine whether revenue sharing and social capital have a synergistic effect that improves economic and biological outcomes, we use the two variables of interest (revenue sharing and social capital) together in the model. Since a causal relationship between revenue sharing and social capital is expected, estimates from this model do not represent the unbiased effects of the two, but we can still infer any alternative effects from the significance of the estimates, as done in Maccini and Yang (2009).

The estimates of the effect of Trust suggest that a trusting attitude has a greater impact on economic outcomes than on biological outcomes, and Trust was the only social capital variable that had a significant effect on Real price. The FCAs in which fishers were more trusting obtained higher prices for their catches (columns 5 and 6 in Table 3), and a marginal increase in the Trust of fishers raised surf-clam prices by 40 yen (wild cluster p -value = 0.07). To further examine the effect of Trust on Real prices, we compared the results of the model measuring the effects of Trust with the model measuring the combined effects of Trust and Revenue sharing and found no synergistic effect of Trust and Revenue sharing on Real prices. The coefficient of Trust was only slightly increased by adding Revenue sharing to the model. Thus, it appears that a trusting attitude is the sole driver of improved prices, and there is no additional effect of Trust affecting Revenue sharing.

Our analysis of four characteristics of fishers’ information networks (columns 7 and 12 in Table 3) shows that the mean of Network size and the standard deviation of Network coverage have some influence on Real prices and Stock densities, respectively, with some ambiguity. A greater standard deviation for Network coverage decreases Stock densities. This suggests that relative uniformity in the information networks within an FCA improves the resource’s stock density. We find that a smaller deviation in Network coverage increases the Stock densities by 317 grams per square meter at the margin (p -value = 0.03), which is observed when the absolute level of Network coverage is controlled (column 13 in Table 3). Although the absolute level of Network coverage becomes important when explaining the outcome together with its standard deviation, the overall level of the Network coverage alone is insignificant when the mean is used in the regression without the standard deviation (Appendix Table A7). These results cast doubt on the effect of the overall level of Network coverage. Extensive coverage appears to be important to improving biological outcomes regardless of whether revenue is shared.

The small number of groups in our sample naturally raises concern that the significant effects are driven by a peculiar observation (group or person). However, this is not necessarily the case because the distributions of the social capital parameters for the groups that shared revenue and the groups that did not are not significantly different according to Mann-Whitney-Wilcoxon testing except for Frequency of sharing information.

We further examine the effect of Revenue sharing and social capital on individual fishers’ perceptions regarding management outcomes to validate the group-level conclusions. Since Real prices and Perceptions toward economic outcomes reflect different aspects of economic performance, the results lead to different inferences. Real prices are a precise measure of revenue-maximization, while Perceptions toward economic outcomes can be affected by trends in profits that relate to satisfaction derived from profits, harvest volumes, and costs. Perceptions toward biological outcomes are more closely related to Stock densities unless some other aspect is more important to fishers. However, Stock densities allow us to capture precise changes in the resource condition in grams per square meter, which may not be noticeable to fishers.

Though the varieties of information shared and frequency of sharing such information are irrelevant to Real prices and Stock densities, they seem to influence the fishers’ Perceptions toward economic outcomes. A greater Variety of information shared among fishers is associated with improvement in their Perceptions toward economic outcomes, and this effect is synergistic with Revenue sharing (columns 2 and 3 in Table 4). When Revenue sharing is included in the regression, the magnitude of the effect of shared information types decreases, but the effect of revenue sharing becomes significant and the explanatory power of the model increases. This suggests that fishers are more likely to view their profits as maintaining a certain level or increasing, not only when they share more types of information— the simple effect of information sharing—but also when they share revenue and more types of information, reflecting a synergistic effect of revenue sharing interacting with information sharing. These effects remain significant even with small-sample inferences (all p -values < 0.1). More frequent information sharing also increases fishers’ profits, but the effect is not enhanced by revenue sharing (columns 4 and 5 in Table 4).

We analyzed the effect of each type of information shared and report the results in columns 7-12 in Table 4. They show that information on specific hot spots and areas with a high density of gear had the greatest effect on perceived economic outcomes. These two types of information are not highly correlated (0.23). Thus, we find that revenue sharing encourages fishers to share both types of information in such a way that it improves their perceptions of the economic outcomes.

We use a supplementary regression to examine the relationship between revenue sharing and social capital and report those results in Table 5. The results indicate that revenue sharing encourages information sharing. Fishers in the groups that shared revenue shared 0.6 more varieties of information on average than the ones that did not share revenue, and they shared such information much more frequently. The average fisher in the overall sample shared 2.00 kinds of information about the fishery, with economically valuable information on specific hot spots shared most often, followed by market information. The average for fishers in the groups that shared revenue is estimated at 2.02 on a scale of frequency that can be interpreted as an increase in frequency from every two weeks (-4) to every two days (-2). This result could reflect the fact that revenue-sharing fishers organize their efforts as a group, which likely requires sharing more detailed information about what each group member is doing. Their collective daily fishing operations, whose success is aligned with self-interest under revenue sharing, can motivate fishers to communicate with each other more frequently and share more kinds of information. Interestingly, as shown in Table 5, revenue sharing does not seem to affect the absolute size or the normalized size of the information networks. The results also show that revenue sharing does not influence unconditional cooperation among fishers but has a negative effect on conditional cooperation.

7 Discussion and Conclusion

We find that the direct effect of revenue sharing has no significant effect on the overall economic and biological outcomes but also find that the incentives presented by revenue sharing affect individual-level perceived economic outcomes by promoting information networks. Revenue sharing leads to larger and more frequently used networks, which leads to better perceived economic outcomes. Specifically, revenue sharing alters the nature of fishers who share a resource from competitors to cooperative group members, thus eliminating the disincentive to share information with them. In addition, we find that several of our measures of social capital have a significant influence on an FCA’s observed as well as perceived outcomes, thus highlighting the importance of the characteristics of a community in the success of fisheries management.

Our results provide insight into how management outcomes in a fishery can be improved. The first lesson is that simply implementing revenue sharing in a comanaged fishery does not necessarily lead to improved outcomes through incentives to cooperate in harvesting and enhancing stocks. Theoretically, revenue sharing can improve both economic and biological outcomes and, in the case of Japanese surf-clam fisheries in Hokkaido, can also lead to production efficiency because revenue sharing and its constraint on total effort supports group incentives. However, the data collected and the results of the analysis do not support this prediction generally; no direct effect of revenue sharing on the economic and biological outcomes of the FCAs was found. Which is not to say that revenue sharing does not produce benefits. The benefits might be derived through other mechanisms such as reductions in costs and conflicts over gear, the effects of which are not measured by prices.

Second, we find evidence of a synergistic effect of revenue sharing and social capital on the types of information shared. Revenue sharing partly incentivizes greater information sharing and sharing of more valuable information, which jointly contribute to fishers’ perceptions of better economic outcomes. This is particularly true for exchanging information on specific fishing hot spots and areas where there is an excess of gear. This finding has important policy implications for fisheries that suffer from inefficiency because of a lack of information sharing between fishers. One mediating factor is that revenue sharing has been implemented along with coordinated fishing operations in all fishery groups in the sample, which may be a driver of information sharing. While simply sharing revenue can only mitigate the disincentive to share information, coordinated fishing operations under revenue sharing make such sharing a practical necessity. Thus, revenue sharing can be a particularly effective policy tool for improving efficiency and fisher management outcomes when coupled with coordinated fishing operations.

The results of our analysis also provide insight into revenue sharing as a tool for supplementing other prevailing management systems such as a rights-based management that does not fully internalize a fishery’s production externalities (Boyce 1992; Huang and Smith 2014). For example, fishers governed solely by an individually transferable quota can still race to fish if the timing of their harvesting during a season or their choice of fishing grounds is important. The inefficiency results from a failure to coordinate their efforts as a group.

Third, in terms of the various aspects of social capital examined in this study, two points are noteworthy: (1) fostering general trust within a community, the trust measure used in our survey, and not just toward fellow fishers is key to observed economic success; and (2) information sharing among fishers accessing a common resource is critical to the health of the fishery economically and biologically. Fishers who had greater trust in people around them obtained higher prices for their clams than fishers who were less trusting. While the mechanism is not tested in this study, one possibility is that broad-based trust in a community can lead to highly effective teamwork in a fishery group within that community and support for efforts to enhance the fishery, which eventually allows for harvesting of higher-quality clams that bring better prices. Trust in the community at large also could allow fishers to trust other fishers not to undermine their enhancement efforts.

The greater influence of the absolute size of the information network over the percentage of FCA fishers included in it implies that additional sources of information are beneficial regardless of how comprehensive the network is. Perhaps an individual’s network does not need to be comprehensive to spread information effectively throughout the group. In fact, the most efficient network should be structured so that individuals need to have a minimal size of network to spread information across the group. However, caution is required when inferring the estimated effect of network size because of moderate linear correlation between the network size reported by the fishers and the size of the FCA (correlation = 0.64).

An interesting aspect of the effect of an information network’s coverage on fish stocks is that resource conditions deteriorate when one or some subnetworks of fishers in an FCA exchange information more intensively than others. Thus, FCAs with relatively uniform distributions of network coverage could be able to create better resource conditions over time by spreading relevant information evenly to all members and, consequently, more successfully coordinating their harvesting and stock-enhancement efforts. We find no such effect from network coverage on prices. However, relatively small standard deviations in information coverage do not always imply that the networks are more efficient. Such networks can be used for disseminating other kinds of information, such as sanctioning members, and the most beneficial structure for those uses would likely be different. An examination of detailed hypotheses about network formation is beyond the scope of this study because of the nature of the data collected.

In addition to the bounded scope of the network analysis, our conclusions are subject to several important limitations. First, while we argue that the revenue-sharing treatment is exogenous and that the presence of revenue sharing and the social capital parameters (cooperation, trust, and information networks) lead to changes in prices and the density of the resource stock, higher prices and a greater abundance of stocks could influence the FCA’s interest in maintaining a revenue-sharing arrangement and the associated levels of cooperation, trust, and information sharing. Second, our results for 10 FCAs could suffer from a small-sample problem. Unless the outcomes of two treatments are consistently significantly different, the model might not be able to capture a subtle or heterogeneously manifested group-level difference. We provided asymptotic refinement with the wild cluster bootstrap to increase confidence in our estimates, but the procedure would not necessarily minimize the variance. This limitation provides an opportunity for future study to refine the estimates with a larger sample and more stringent identification.

To our knowledge, this is the first empirical study of a variety of hypotheses regarding pathways by which revenue sharing can improve efficiency. We also quantify the effects of revenue sharing and social capital on fisheries using data that include more than a single case, which has not previously been studied. Our results provide insight into how fishery interventions can and cannot strengthen social capital and can be expected to improve an FCA’s performance. This study empirically highlights the multifaceted effects that revenue sharing can have on fishers’ economic incentives—not only shirking but also strengthening cooperation.