Long-distance relationships and resource-management in Tanzanian fisheries

Long-distance relationships and resource management in Tanzanian fisheries: Baseline data collection Collective action is often studied in the context of common-pool resource systems--systems in which resources can be overharvested but excluding users is costly (Ostrom, 2003). This cost creates a dilemma: preventing overharvesting requires successful cooperation among many common pool resource users (Ostrom, 2008), or one or more individuals willing to foster cooperation among users and/or punish overuse (Gutiérrez et al., 2011; Olson, 1965). The difficulty of successful common pool resource management is compounded by the fact that many common pool resources, like forests, watersheds, grasslands, and fisheries, can be accessed by multiple communities (Ramirez-Sanchez & Pinkerton, 2009; Varughese & Ostrom, 2001), such that monitoring and enforcing rules governing the common pool resource requires multiple communities working together. That is, these systems present a problem of between-community collective action, which may be more difficult to achieve than within-community collective action. People are more likely to participate in collective action when the perceived benefit-cost ratio (that is, net benefits) of participation are greater than the net benefits of not participating. The same principle applies to between-community collective action, and many of the same factors that influence the perceived net benefits of within-community collective action, such as institutional effectiveness and community heterogeneity, are anticipated to also influence the net benefits of between-community collective action. However, between-community collective action is likely to be affected by other factors not shared with within-community collective action, namely the relationships between communities. People vary in their preference for and ability to form and sustain connections between-communities, and the quality of these relationships vary as well. These between-community relationships may impact the perceived net benefits of between-community collective action. Institutional effectiveness Institutions can promote effective common-pool resource management between communities by enhancing the benefits and lowering the costs of between-community interactions. Broadly defined, institutions are rules-in-use for managing individuals’ behavior (North, 1991; Ostrom, 2005); these rules-in-use can be laws or even norms of behavior enforced by others (Ostrom, 2007). Within-community and between-community resource management institutions can lower the costs of between-community management by punishing those who defect against strangers (Henrich et al., 2010) or members of other groups (Ensminger, 1992; Fearon & Laitin, 1996), arbitrating between-community disputes (Paciotti & Borgerhoff Mulder, 2005), minimizing opportunities for misunderstanding (Habyarimana et al., 2007), or meeting basic needs such that individuals have surplus time or resources to invest into managing shared resources (Hruschka et al., 2014). Institutions can also increase the benefits of management by rewarding cooperation (Andrews & Borgerhoff Mulder, 2018), managing divisions of labor via specialization (Chagnon, 1992; Jochim, 1981), or mandating turn-taking so that individuals paying opportunity costs need not wait so long to obtain benefits (Bardhan, 2000; Lansing, 2007; Rustagi et al., 2010). People who perceive their communities to have more effective between-community institutions are predicted to participate more in between-community collective action. Heterogeneity Heterogeneity in the perceived benefits and costs of collective action can reduce participation in collective action within a community. Individuals who stand to benefit less are less likely to contribute to collective action (Ostrom, 2008). When benefits or costs are unequally distributed across the community, people who are less likely to benefit may not participate in collective action; even those who do stand to benefit may then choose not to participate because (1) the costs of participation are distributed among fewer people or (2) they feel cheated if others do not participate (Fischbacher & Gächter, 2010). Heterogeneity of benefits and costs can arise from a number of factors, including gender (Pandolfelli et al., 2008), ethnicity (Waring & Bell, 2013), wealth (Ruttan & Borgerhoff Mulder, 1999), and geography (Lam, 1996), among others, all of which may affect preferences (Tucker, 2007) and participation in collective action in turn. Long-distance orientation and relationships Individuals who have a preference for or existing between-community relationships may perceive greater benefit from between-community collective action and thus participate more. Ethnographic (Hruschka, 2010; Malinowski, 1922; Pelling, 2002; Pisor & Gurven, 2016, 2018; Pisor & Surbeck, 2019; Smith, 1988; Wiessner, 1977) and archaeological data (Braun & Plog, 1982; Demps & Winterhalder, 2019; Lathrap, 1973; Spielmann, 1986; Whallon, 2006) suggests that people rely on between-community relationships to buffer community-wide shortfalls and provide access to non-local resources. When the benefits of this risk-buffering strategy exceed the expected cost of long-distance relationships (Pisor & Gurven, 2016, 2018; Pisor & Jones, 2021), people will be more likely to have a preference for forming long-distance relationships --that is, have greater long-distance orientation (LDO). People with greater LDO will be motivated to seek relationships in other communities; we expect that indicators of LDO will include generosity or contribution to the collective good, which can bolster one’s reputation as a desirable social partner, interest in what is happening in other communities, and wanderlust—a desire to visit elsewhere. Further, individuals with LDO will be interested in forging relationships with other communities. Thus, we predict that people with higher LDO will be more likely to participate in between-community collective action with other communities to augment their reputation and to meet candidate long-distance connections. In theory, if people have the ability to form long-distance relationships (e.g., adequate time, passable roads, cellphone service), then people with greater LDO will be more likely to have long-distance relationships (LDRs) in other communities. LDRs have two functions that may increase likelihood of participating in between-community collective action. First, because long-distance relationships help buffer against shocks, people have a vested interest in their long-distance relationships, and the communities in which they reside, to ensure they are able to provide assistance when needed (Aktipis et al., 2018). Second, by having an LDR in a particular community, an individual’s trust in members of that community to behave in predictable ways will increase, as other communities become more familiar through LDRs and LDRs can help individuals monitor others’ behavior (e.g., through gossip)—at least, relative to what trust would be without LDRs. Thus, we also predict that people with more relationships in other communities will be more likely to participate in between-community collective action with the communities their long-distance relationships reside in. Note that in practice, the effect of LDO and LDRs on between-community collective action may be difficult to disentangle; for example, individuals with higher LDO are likely to have more LDRs (although the strength and direction of the relationship between the two is an empirical question). Contingent help within long-distance relationships LDRs may vary in the extent to which they resemble close-proximity friendships. While friendships are sometimes described as examples of reciprocal altruism (Trivers, 1971), across cultures friendships actually deviate in many ways from the logic of reciprocity. Friends usually do not track debts or reciprocally match contributions within the relationship (Xue & Silk, 2012); instead, friends help each other when needed as long as the contributions in the long-run roughly balance out (Hruschka, 2010; Tooby & Cosmides, 1996). However, close-proximity friendships require many repeated-interactions for accounts to eventually balance, and close-proximity friendships are easier to monitor and happen within a shared institutional context, providing common mechanisms to potentially mediate disputes and inequality between friends. This means that close-proximity friends can help each other without explicitly making the help contingent on being reciprocated. On the other hand, LDRs are more difficult to monitor and involve fewer interactions, so debts can accumulate and may not be repaid; because of this, LDRs are riskier than close-proximity friends. This means that LDRs will make their help more explicitly contingent on being repaid in the future. However, when there is greater trust within an LDR, either through greater opportunities to interact with and monitor each other or shared institutions, or when individuals within the LDR have access to other risk-buffering strategies and are better able to afford a failure to reciprocate, help within LDR will be less explicitly contingent. Ethnographic context The study will focus on the implementation of fisheries management initiatives on the mainland coast of Tanzania. Approximately 20% of Tanzanians live within the coastal zone (National Bureau of Statistics Tanzania, 2012), relying primarily on maritime production as their primary source of subsistence and income. About 95% of all fishing in Tanzania is artisanal, concentrated along the inshore shallow water; the most productive fishing grounds are coral reefs, mangrove creeks, seagrass beds, and sand banks (Jiddawi & Öhman, 2002). Even though artisanal fishing is “small-scale,” concentrated fishing in areas such as coral reefs can deplete local resources over time (Mangi & Roberts, 2006). Indeed, catch data over three decades confirms that fishery productivity has decreased as the number of fishers increased (Silas et al., 2020). When asked why fishers do not abandon fishing, most affirm that they are worried about dwindling fish stock, but cite a lack of other income sources for their continued reliance on fishing (Naar & Mahenge, 2014; Silas et al., 2020). Governance Structure Beach Management Units (BMU) are semi-autonomous organizations, operating under the oversight of District Authorities and legally recognized by the national government (Fisheries Act, 2003), that represent local stakeholders - including boat crews, boat owners, traders, processors, boat builders and repairers, and net repairers - in the management of fishery resources (Leeney et al., 2019). Each village with a large enough landing site has at least one BMU with an elected executive committee, and this committee is responsible for consulting with the community, developing management plans, monitoring and enforcing regulations, and reporting results back to District Authorities. The BMU are a co-management participatory approach to management (Ogwang et al., 2005), with the village and district governments working with local stakeholders to manage the fisheries. Recently, recognizing that multiple villages access the same fisheries, Collaborative Fisheries Management Areas (CFMA) have been established under the Director of Fisheries (Leeney et al., 2019). These involve usually three to five collaborative BMU. The CFMA executive committee, made up of representatives from the executive committees of the BMU, will develop management policies for the entire area, but ultimately each BMU is responsible for the implementation of the CFMA policies. Thus, the BMU is a within-community institution for managing fishery activities within the village, and the CFMA is a between-community institution for managing fisheries shared between villages. However, the success of the CFMA depends on the BMU successfully managing the local fisheries, so that individual contributions at the BMU level are contributions to within- and between-community collective action. The first CFMA outside of a Marine Protected Area on the mainland coast were established in 2009 with the help of WWF Tanzania. These were concentrated around Dar es Salaam, the Pwani Region, and the Lindi Region. Recently, since 2016, the World Bank through the SWIOFish Project has helped establish more CFMA to the north in the Tanga Region, with partner organizations Mwambao Coastal Community Network and WCS helping to implement the rollout of these new CFMA. At the time of this preregistration, CFMA were finalizing management plans which are expected to be implemented within the next year. Hypotheses H1. Individuals who perceive institutions to be more effective at managing the fishery and enforcing policies fairly will participate to a greater degree in collective action. H2. Individuals who live in communities with less heterogeneity in participation in decision-making, norms of cooperation and expected benefits from the common-pool resource will participate to a greater degree in collective action. H3a. Individuals who have greater long-distance orientation will participate to a greater degree in collective action. H3b. Individuals with more long-distance relationships in the communities sharing the common-pool resource will engage in more collective action. H4a. Help between LDR will be more explicitly contingent on being reciprocated than help between close-proximity friends. H4b. Help between LDR will be less explicitly contingent when friends interact with each other more or are members of the same cooperative institutions. H4c. Help between LDR will be less explicitly contingent when the friends have access to other risk-buffering strategies. Method Sampling plan We will sample participants from 28 villages with BMU across 5 CFMA in the Tanga Region of Tanzania. Within each village, we will sample between 40 to 60 fishers, processors, traders, and patrons working at the village. Exact number of participants to be sampled in each village will depend on logistics of recruiting participants and the size of the village. The minimum total sample will be approximately 1,200 participants. In addition, we will conduct ethnographic interviews with approximately 5 to 15 participants in each village. These interviews will be used to provide context to responses within each village. The final sample will be at least 150 participants for these interviews. By design, both the survey and ethnographic samples will include both residents and non-residents of the village sampled. Seasonal mobility is part of making a living, and long stays at other villages, often repeated across years, can both draw on LDR and shift the meaning of LDR (that is, if a fisher returns repeatedly to the same village seasonally, are their relationships there still LDR?). Further, our pilot data from the Lindi and Pwani Regions suggest that BMU vary in the extent to which they permit non-residents to engage in collective action, which may ultimately impact the success of the fishery. By asking about the sampling village and other villages in the CFMA, we hope to capture different degrees of LDR for these non-resident participants. Measures Here we describe and justify the key variables discussed in the background. Actual questions for these and additional measures are included in the attached XLS survey. Collective action. Our primary dependent variable is collective action within the BMU. Based on pilot surveys, we determined that in most BMU, participation in collective action includes being an elected leader, attending meetings, helping with beach cleanings, educating others, stopping or reporting fishing activities that violate regulations, and contributing to development projects. We ask participants to self-report whether and how much they engage in these activities. Institutional efficacy. Pilot data indicated that people thought effective BMU stop illegal fishing activity, clean the beach environment, develop the landing site and users’ capacity, and fairly represent the community and stakeholders. We use survey items asking if BMU do enough or need to do more along these dimensions. Long-distance orientation. We are measuring long-distance orientation using three different methods. First, in a series of questions, we will ask whether participants like to learn and interact with people from other villages. Second, we will ask about their tendencies to travel and migrate. Finally, we will ask them to complete a task dividing money between villages, which will be shared with one random participant from those villages. For resident participants, this will include their home village (that is, the sampled village), and we will be able to measure their willingness to share with distant villages compared to their own village; for non-resident participants, this will include a village where they are likely to have existing connections (the sampled village) and villages where they are less likely to have connections. Long-distance relationships. We will ask each participant the number of family, friends, and business associates within each village within the CFMA. We will ask follow-up questions about one of their friends in 1) the sampled village and 2) a friend from another village in the CFMA. These follow-up questions will include how they met, how often they interact, what qualities they like in the friend, and how that friend has helped them. To compare contingent helping, we focus on two specific forms of helping: giving money vs loaning money. Gifts entail movement of money where reciprocation either wasn't expected or never happened, without negative consequences for the friendship, while loans mean movement of money that was either reciprocated or, to date, return is still expected. We will ask about whether they have received both kinds of help, the most help they have received in that form, and how often they receive help in that form. Analysis We do not preregister any specific models or analyses before data collection. 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