Long-distance relationships and BMU participation
Creators: name: Mulder, Monique Borgerhoff
name: Smith, Kristopher M
name: Pisor, Anne
Publisher: name: OSF Registries
Year: 2023
Subjects: subject: Anthropology
subject: FOS: Sociology
subject: Social and Behavioral Sciences
subject: Tanzania
subject: common pool resources
subject: fisheries
subject: long-distance relationships
subject: migration
subject: mobility
subject: natural resource management
subject: participatory co-management
subject: social relationships

description: Analysis Preregistration

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 &amp; Pinkerton, 2009; Varughese &amp; 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 – community-level characteristics like institutional effectiveness and individual-level characteristics, like wealth and gender – are anticipated to also influence the net benefits of between-community collective action. That said, between-community collective action is likely to be affected by other factors not shared with within-community collective action – namely, relationships between individuals in different communities. 

Long-distance social relationships, which often span community boundaries, can foster interdependence, which can increase individuals’ willingness to generate benefits and avoid costs affecting members of other communities (Pisor et al., 2023; Pisor &amp; Ross, 2022). However, long-distance relationships can also undercut successful collective action around natural resource management by undermining successful monitoring or encouraging outgoing resource flows, away from a forest or fishery (Pisor et al., 2023). In short, long-distance relationships present a unique challenge to the management of large common pool resources, as they can both promote and undercut successful collective action.

Here, we investigate the relative roles of (1) long-distance relationships and (2) local institutional effectiveness on participation in collective action in common pool resource management, while controlling for other variables that may modulate the potential net benefits of participation. Specifically, we draw on data from 28 villages in Tanga, Tanzania, each with a nearby Beach Management Unit (BMU) that manages local coastal fisheries, to explore whether an individual’s number of long-distance relationships and community-level perceptions of institutional effectiveness predict individual-level involvement in fishery-promoting activities. 


Predictions

We expect factors that commonly affect within-community collective action around natural resource management will likewise impact between-community collective action. Namely, we predict that:
(P1) Individual-level perceptions that BMU management is more effective will be associated with greater individual participation in BMU-related activities.

Further, we expect that long-distance relationships will impact between-community collective action, such that:
(P2) An individual’s number of long-distance relationships (family, friends, and business associates) will be associated with greater individual participation in BMU-related activities.

We estimate the effects of other variables likely to affect the benefits to and constraints on BMU participation, including age, gender, profession, wealth, immigration to the community, and trust of members of the same community and one randomly selected nearby community.

We previously preregistered the hypotheses and data collection methods prior to data collection on 2022-01-23 at https://osf.io/bvrty. In that preregistration, we also hypothesized that heterogeneity in communities would be associated with decreased participation in BMU-related activities; however, because of our sampling methods, we do not think we can validly estimate heterogeneity within the communities, and have dropped that hypothesis in these analyses.

We have outlined our a priori expectations of the relationships between these predictors, controls, and the outcome – BMU participation – a directed acyclic graph (DAG). The DAG was constructed in a feedback process of proposing a priori expected relationships between variables and then checking the conditional independencies against the data, and then repeating the process until we were satisfied the DAG represented the data and expectations based on ethnographic observations. Throughout this process, we did not examine the outcome variable, BMU participation, or any relationships with it, but only examined the relationships between independent variables. In the attached document, we include figures of the DAG. 

 
Relationships are distinguished between (1) family relationships and (2) voluntary friendships and associations. 
● An unobserved lineage variable - familial effects - causes family relationships, regardless of distance. That is, we assume that the size of one’s family is largely determined by family behavior rather than a person's characteristics. 
● Friendships and associations are then independently caused by unobserved preferences and constraints to form friends (friendliness) and associations (associativeness), but relationships of close- and long-distance share also share a common set of unobserved preferences and constraints causing more close- and long-distance voluntary relationships (close sociality, distant sociality). 
● Finally, more long-distance family is associated with more long-distance friends, possibly because of mutual connections in other villages or the residential mobility enabled by having distant family. The data suggests this is specific to long-distance friends and there is no relationship between long-distance family and associates – thus no connection between the two in our DAG. 

Next, we add demographic associations relating to relationships:

 

● The population is weakly patrilocal, with (1) women more likely to move near their husband’s family, and (2) inter-village marriage not being uncommon. This results in women having fewer close-distance family members than men, but this does not affect long-distance family because that is still largely a function of extended family members’ reproductive decisions (see lineage, above). This of course also affects how long women have lived in a village, given men are more likely to have lived in a village their whole lives.
● Gender also structures daily social relationships, and women are likely to have fewer friends and fewer long-distance relationships. 
● Age and residence length both work together to predict close-distance family, with people who have lived in the village longer than expected (given their age) having more close-distance family and people who are younger than expected (given how long they have lived in the village) having more close-distance family; this might represent processes of a growing extended family within the village with more individuals dispersing to other villages as a person grows older. 
● Similarly, longer residence length is associated with more long-distance family, even when adjusting for age; this is less straightforward but might also be a process of close-distance family dispersing. 
● Finally, one’s profession (which is determined in part by gender) is associated with their tendency to make friends and associates. Different professions lead to different opportunities and incentives to form different relationships; for example, fishermen might have more long-distance relationships because they visit different landing sites and meet more people, and traders might have more associates because they provide access to more market opportunities. After exploring the data, we find that profession is associated with close- and long-distance friends and close-associates, but not long-distance associates. Thus, our data suggests profession is a causal factor in friendliness and close-sociality, but not distant-sociality.

Next, we add variables expected to be related to collective actions around the fishery:

 

We examine four variables related to collective action: how effective one perceives the BMU to be, within- and between-community trust, and whether one is an elected BMU leader. 
● Within- and between-community trust share a common set of unobserved causes as generalized trust, which is likely caused by gender - women are less trusting than men in this sample - and age - older people are more trusting. Within-community trust then also is caused by close-friends and associates, as well as residence length - knowing more people within the village and living longer in the village leads to more trust. Likewise, between-community trust is greater for people with more long-distance friends and associates. However, while we expected this to also be the case for family, we did not find any relationship in this sample between trust and family for either distance.
● BMU leader is a position elected by members of the village, and people with more family members are more likely to be a BMU leader, regardless of whether the family lives within or outside the village, possibly representing inherited prestige markers that they can leverage during elections – in short, a causal relationship between the unobserved variable “lineage” and BMU leader. BMU leader is also associated with more long-distance friends and greater between-community trust. Because leaders are elected by local village members, we expect that these do not cause BMU leaders to be elected but rather BMU leaders have more long-distance friends and between-community trust because they are more likely to visit other villages while performing their duties. 
● BMU efficacy is caused by greater perceived trust within- and between-community, as well as by having more close-friends and being a BMU leader. 

Finally, we add in the outcome variable: 

We anticipate nearly every measured variable to have a direct effect on our outcome (in blue) BMU participation, except for age, as age did not come up during ethnographic interviews as a factor in whether someone participated in the BMU, but the other variables did. Controlling for the full set of variables allows us to estimate the direct effect of each long-distance relationship in a single model; we can also estimate the direct and total effect of BMU efficacy in the same model. Our key assumptions are that there is no direct connection between the unobserved relationship variables (distant sociality, friendliness, and associativeness) and BMU participation, and that there are no intervening variables between the relationship variables and BMU participation. 

Data collection

 We collected data from 2022-01-25 to 2022-04-03 in 28 villages with a BMU in the Tanga region of Tanzania. A total of 1,399 participants were interviewed. 

Exclusion criteria
 Participants who do not live in the focal village will be excluded. In our earlier preregistration, we stated we would sample and include non-resident participants; however, this was based on observations during the pilot phase in a different region, where most non-residents where dago fishers who camp at the landing site and are expected to cooperate with the BMU. Most of our non-resident participants were not dago fishers, but transient fishers who would fish for the day before returning home, and are not expected to cooperate with the BMU. There are no other exclusion criteria.

Variables

BMU Participation
 Our outcome variable will be self-reported participation in BMU activities in the past twelve months. Participants were asked 9 questions about their participation and answered either yes or no, with a “yes” indicating they had participated in the activity. The questions were about participation in meetings, beach cleanings, educating others, projects, and monitoring and enforcement of regulations toward neighbors and people from outside the village. Responses will be analyzed in a multilevel Bernoulli model, estimating the probability of responding yes to each of the participation questions, with varying intercepts for participants and varying intercepts and slopes for questions.

Close-distance Relationships
 Participants were asked the number of adult family, friends, and business associates they had in their village. The total number of each relationship will be log-transformed for analysis because we expect the effects of relationships to be non-linear - going from zero to one relationship is likely to have a larger effect than going from two to three relationships. Log-transforming will also help to handle outliers in the data.


Long-distance relationships
 Participants were asked the number of adult family, friends, and business associates they had in each other village in the CFMA. For each relationship type, we will sum the number of that relationship in each village. The total number of each relationship will be log-transformed for analysis because we expect the effects of relationships to be non-linear - going from zero to one relationship is likely to have a larger effect than going from two to three relationships. Log-transforming will also help to handle outliers in the data.

BMU efficacy
 Participants were asked 6 questions about how effective the local BMU at working toward different goals, such as cleaning the beach or developing the landing site. Participants were asked whether the BMU does anything to advance that goal, and participants could respond yes or no. A principal components analysis (PCA) of the questions finds that reducing the responses to a single dimension accounts for 43% of the variance in responses, and that the second dimension had an eigenvalue of less than 1, suggesting the second dimension captures less variability than a single question. We will thus use computed scores from the first dimension of the PCA as our measure of BMU efficacy. 

Within- and Between-Community Trust
 To measure within-community trust, we asked participants how many people in the village they trust to keep their promises and how many they trust to cooperate, with responses of none, a few, half, most, and all. We will code these responses as 1 (none) to 5 (all), sum them, and subtract 1 to produce a scale ranging from 1 to 9. Participants were also asked the same questions for a randomly selected village within the CFMA; the same coding procedure will be used to calculate a participant’s between-community trust in this randomly-selected village. These variables will be modeled as monotonic ordinal effects in the analysis.

Profession
 Participants were asked how they were involved in the fishery as a source of income. Participants could give multiple responses. We will collapse profession categories into boat owner, fisher, trader, processor, seaweed farmer, and other. People who gave multiple responses will be assigned a primary profession based on observations during ethnographic interviews that some professions often involved ancillary professions (e.g., traders also often processed their own goods). This procedure was used in a previously published paper with this data set (see: https://doi.org/10.1016/j.evolhumbehav.2022.09.004). Profession will be included in the model as five indicator variables comparing each other profession to fisher.

Residency length
 Participants were asked the year they moved to their current primary residence. We will compute the years they have lived in the current village by computing the difference between 2022 and the year they moved into the village. Before analysis, residency length will be standardized across participants. We include age in the model, so residency length will be adjusted by age.

Age
 We asked participants what year they were born. Age is the difference between 2022 and their birth year. Before analysis, age will be standardized across participants. 

Gender
 Participants could respond either “man” or “woman.” We will use indicator coding, with man coded as 0 and woman coded as 1. 

Leader
 Some participants were elected leaders of the BMU. We will include in the model an indicator variable of whether people were a leader, with non-leader coded as the baseline. Note that the last BMU elections in every village were two years prior to the survey, so participation in the BMU in the past twelve months could not have caused a person to be elected as a BMU leader. 



Analysis
Missing Data
 Missing data in responses likely results from errors during the interview as researchers accidentally missed or passed a question. As such, we expect the data to be missing completely at random. Rather than throwing out responses, we will use the mice package in R using predictive mean matching to impute missing data prior to any transformations above. We will use the predictive mean matching algorithm in mice to impute 5 data sets, which will be pooled together and analyzed using brms.

Model Specification
 We will analyze the data in a Bayesian multilevel regression model, regressing BMU participation on the variables described above, using a Bernoulli likelihood. The Bernoulli distribution is a special case of the binomial distribution with only one trial, which describes the probability of observing a “success,” or in our model, the probability of saying yes to a given participation question; we use this distribution because the outcome variable is a single yes/no question and we are estimating the probability of answering yes to that question. The model will include varying intercepts for participants, and varying intercepts and slopes for relationship for BMU participation question, BMU, CFMA, and researcher; the random slopes for predictors of interest are included to examine generalizability across actions, sites, and researcher characteristics. See attached script for model specification.