Friends near and afar, through thick and thin?: Comparing contingent help between close-distance and long-distance friends in Tanzanian fishing villages
Creators: name: Pisor, Anne
name: Smith, Kristopher M
Publisher: name: OSF Registries
Year: 2024
Subjects: subject: Biological and Physical Anthropology
subject: Social and Cultural Anthropology
subject: Anthropology
subject: FOS: Sociology
subject: Social Psychology
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology
subject: Tanzania
subject: cooperation
subject: friendship
subject: long-distance relationships
subject: reciprocity
subject: risk-buffering

description: Note: This preregistration was created during data collection but before examining the data.

Hypothesis
 Across cultures, pairs of friends usually help each other without strict bookkeeping of who did what and without making their help explicitly contingent on being repaid. However, research on friendships has mostly focused on close-distance friends, and people often form friendships with people across vast distances. Long-distance friendships can be more costly to maintain; for example, infrequent interactions and not belonging to the same institutions, which are important for enforcing rules, can make defections between long-distance friends more likely. Because of these challenges, long-distance friends might only be willing to help each other when help is assured to be repaid. We hypothesize that help between long-distance friends is more explicitly contingent than help between close-distance friends. 
However, distance isn’t binary: friendships exist on a spectrum of close-distance to long-distance. For example, while on average long-distance friends will be less likely to interact frequently and less likely to belong to the same religious institution, close-distance friends who interact infrequently or belong to different religious institutions will be more likely to make their help more explicitly contingent. Likewise, if some long-distance friends interact more frequently or belong to the same institution, their help may be less explicitly contingent. We hypothesize that when friends, regardless of distance, interact more frequently or belong to the same religious institution, help between friends will be less explicitly contingent. 

Method
Sample
 KMS, BA, KB, PF, and RK collected data from January 2022 to April 2022 in 28 fishing villages across five Collaborative Fisheries Management Areas (CFMA) in the Tanga Region in northeastern Tanzania. We anticipate recruiting approximately 45 participants per village, with an expected final sample of 1,260 participants. Participants were recruited by local leaders, who brought fishery users to us to be interviewed. 

Procedures
Interviews were conducted in Kiswahili using Open Data Kit (ODK) on a cellphone to record responses. Participants were asked the number of friends living in each village in the CFMA. ODK randomly selected one friend from the focal village and one friend from another village in the CFMA for follow-up questions about characteristics of the friendship and the help they have received from the friend. Participants were first asked about the friend in the focal village followed by the friend in the other village. Due to software constraints rather than deliberate design, questions were always asked in the same order. This study was declared exempt by the Washington State University IRB and was approved by the Tanzanian Commission for Science and Technology (COSTECH).

Measures
Help received from friend
 We asked participants whether their friend has ever given or loaned them more than 10,000 TSH. We specified an amount greater than 10,000 TSH because people are expected to regularly help their neighbors in need of help by providing a few thousand shillings; per pilot work in the Lindi and Pwani Regions of Tanzania, 10,000 TSH is usually gifted and loaned only between family and friends. 
 When participants say a friend helped by giving or loaning money, we asked follow-up questions about the largest amount of money given or loaned and what that gift or loan was for. For participants who answered no to receiving a gift or a loan, we impute 0 as the max amount received. We also asked how frequently participants receive gifts or loans from the friend, with response options of monthly, seasonally, yearly, or less than once a year. For participants who answered no to receiving a gift or a loan, we impute a response category of never. 
Interaction frequency
 We asked participants how often they see each other face-to-face, with response options of daily, every other day, weekly, monthly, seasonally, yearly, and less than once a year. 
Belong to same religious institution
 We asked participants their religion, their friend’s religion, and whether they and their friend attend the same mosque or church, to which they could respond yes or no. Participants will be categorized into “not belonging to the same religious institution,” “belonging to the same religion but not in the same congregation,” and “belonging to the same congregation.” 
Profession
 We asked participants their profession in the fishery. Different professions differ in how much they might travel and the importance of business connections in other villages. Options for profession are fisher, captain, boat owner, processor, trader, agent (someone who buys and transports fish, usually sardines, on behalf of an investor or company), seaweed farmer, and other. Participants could choose all options that apply to them. 
Wealth
 To measure wealth, we asked participants whether they own a house with amenities and own personal items associated with wealth in the local context. For house amenities, we asked whether their house has: a cement or tile floor, block or stone walls, a metal roof, electricity, piped water, an indoor toilet, and a satellite dish. For personal possession, we asked if they or someone in their household own: a cellphone, a smartphone, a radio, a television, an electric fan, a refrigerator, a generator, a bicycle, and a motorcycle. Responses could be yes or no. We will use multiple correspondence analysis (MCA)of these items to create a standardized-wealth index, extracting the first 1-2 dimensions such that at least 40% of the variance is summarized. 

Pre-analysis
Exclusion criteria
 We have no exclusion criteria for participants. Participants who completed the friendship section of the survey and had at least one close- or long-distance friend will be included in analyses.
Missing data
 Gift size and frequency. Due to a coding error in the survey, in the first seven villages we did not ask follow-up questions about the gifts of help people received from close- and long-distance friends. We will exclude these participants from analyses. 
 Wealth. Our measure of wealth is constructed from a number of questions using MCA, which generally requires complete data. Because of the number of questions, it is possible that some questions were occasionally skipped due to researcher error, and thus would require excluding these participants from analysis. If more than 1% of participants would be excluded because of missing wealth data, we will use multiple imputation for multivariate analysis via the missMDA R package. We will impute 10 data sets and run the models on each dataset, pooling the results together into one posterior distribution to estimate effects.

Causal models
Base model
Our primary outcome variables are whether the participant has been given (give) or loaned (loan) money from their friend. Our primary exposure variable is whether the friend is close- or long-distance (D). However, many participants will not have long-distance friends, and participants who do not have long-distance friends may interact with close-distance friends differently than people with long-distance friends. As such, we include in our analyses variables that might affect both whether one has a long-distance friend and whether one receives help in a gift or loan. These variables are wealth (w), profession (p), gender (g), and age (a). Economic and social activities are highly gendered in this context, and gender determines what professions are available to a person, their opportunity to generate and accumulate wealth, and the kind of help they receive. Profession likely affects wealth, the opportunity and importance of making long-distance friends, and the kind of help one asks for and receives. Wealth likely affects the ability to travel to meet and maintain long-distance friends and the kind of help one receives. Age influences what profession one can do, what wealth a person has accumulated, their opportunity of making long-distance friends, and their likelihood of receiving help. These relationships result in the directed acyclic graph (DAG) in Figure 1. To estimate the direct effect of distance on help, we will adjust for gender, profession, wealth, and age (see Figure 2). 

Full model
For this model, our primary outcome variables are whether the participant has been given (give) or loaned (loan) money from their friend. Our exposure variables are whether the friends belong to the same religious institution (R) and how frequently they interact (F). Other relevant variables are whether the friend is close- or long distance (d), wealth (w), profession (p), gender (g), and age (a). The relationships between gender, profession, wealth, age, distance, loan, and give are the same as the base model. Only distance determines whether friends belong to the same religious institution; because almost all participants will be Muslim, gender, profession, wealth, and age are not expected to affect whether they attend the same mosque. Frequency of interaction is determined by gender, profession, wealth, age, distance, and whether friends belong to the same religious institution. This results in the DAG in Figure 3. To test the direct effects of the exposure variables of interest, we will adjust for gender, profession, wealth, age, and distance (see Figure 4). 

Predictions
1. Comparing gifts and loans, people are more likely to receive help, receive more money, and receive help more frequently in the form of a gift than a loan from close-distance friends than long-distance friends.
2. People who interact more frequently with their friends are more likely to receive help, receive more money, and receive help more frequently in the form of a gift than a loan.
3. People who belong to the same religious institution as their friends are more likely to receive help, receive more money, and receive help more frequently in the form of a gift than a loan.
For Prediction 1, we use a difference-in-difference approach, testing the interaction between distance and the kind of help, comparing the difference between receiving help in the form of a gift and a loan between close- and long-distance friends. Our prediction is that the difference between receiving help as a gift or a loan is greater for close-distance friends than long-distance friends. For Predictions 2 - 3, we predict frequency of interaction and belonging to the same religious institution will interact only with the form of help, not with distance, so we will directly compare the effect of each variable on receiving help in the form of a gift and a loan. Our prediction is that the effects of frequency of interaction and belonging to the same religious institution are larger for gifts than loans. 

Models
 We will analyze the data using multilevel Bayesian regression models. We will use a hurdle lognormal likelihood to estimate the probability one receives a gift or a loan, and if they do receive a gift or a loan, the mean max amount received. For frequency of receiving a gift or loan, we will use an ordered categorical likelihood to estimate the probability of receiving gifts or loans monthly, seasonally, yearly, less than yearly, and never. We will run two versions of each model, one a baseline model estimating the total effect of distance on help received, and one a full model estimating the direct effects of frequency of interactions and belonging to the same religious institution on help received. In all models, we include gender, age, profession, and wealth. All parameters interact with the form of help: gifts vs loans. We include in this preregistration R code for implementing the model (see file model.r); here we describe key decisions.
 Parameters. We estimate categorical predictors (gender, profession, distance) and their interaction with the form of help (gifts vs loans) as varying intercepts. This is to avoid estimating differences relative to an intercept that would include the effects of other categorical variables. By estimating these effects as varying intercepts, we can compute the difference between the estimated intercepts of receiving help from a close- and long-distance friend as a gift and as a loan while holding the other effects constant. We estimate the effect of continuous predictors - standardized age, standardized wealth, frequency of interaction, belonging to the same religious institution - as fixed-effects. For continuous (age, wealth) and ordered categorical (frequency of interaction, belonging to the same religious institutions) variables, there are two predictors, one for gifts and one for loans. Because religious institutions and frequency of interactions are ordered categories, we model these as monotonic predictors. Finally, we include varying intercepts for participant, CFMA, participant village of residence, and alter village of residence.
 Priors. In the included script, we specify regularizing priors that make reasonable predictions but still allow for a range of possibilities. These priors predict thick-tailed Gaussian distributions over the range of possible values, but extreme values are somewhat less likely than more moderate values (see Figures 5 and 6 for prior predictive checks for the model of max amount given and frequency of contribution, respectively). With the anticipated data, these priors should have minimal effects on the posterior. The priors also do not specify any of the effects in the model to be in a particular direction, and what effect the priors do have would be to make somewhat more conservative estimates of the effects. However, these priors may change - particularly their sigma may be decreased - to help with model estimation by reducing the state space of potential values.