Institutional setting

Seven out of ten rural households in Senegal are members of RPOs, and RPOs have expanded rapidly in number and membership in the last few decades. Between 1982 and 2002, the percentage of villages with RPOs rose from 8 to 65 percent in Senegal (The World Bank 2008). Senegal is a country with a vast array of rural institutions in thousands of villages and with strong national-level organizations (Bernard and Wouterse Reference Bernard and Wouterse2015).Footnote ¹ It has been observed, though, that these RPOs face challenges in ensuring the commitment of members. This is particularly true for activities related to collective commercialization of agricultural output. In fact, while RPOs remain active in input and credit provision, their capacity to aggregate output has been weakened considerably. At the time of harvest, producers are now visited by private collectors and traders. This has led to important side selling and produce is not marketed solely through the RPOs. As a result, RPOs are seldom able to aggregate the necessary amount of produce to trigger economies of scale and bargaining power at the time of commercialization. This is despite isolated evidence that RPOs in Senegal can, in fact, provide their members with profitable and reliable output commercialization services. In sum, in the absence of a sufficient amount of aggregated output, RPOs are not in a position to effectively obtain higher output prices, which leads to further side selling and a lack of interest of members for commercialization services offered by these organizations (Bernard et al. Reference Bernard, Sene, Viceisza and Wouterse2014).

Collective action problems of Senegalese RPOs

Generally, problems of collective action arise “whenever individuals face alternative courses of actions between short-term self-regarding choices and one that, if followed by a large enough number of individuals in a group, benefits all” (Ostrom and Ahn Reference Ostrom and Ahn2003, p. xiv). In the context of commercialization of agricultural produce, farmers who are organized in RPOs can choose between selling their produce directly at the farmgate to a local trader with immediate payment and selling it collectively through their RPO. Farmers in our sample cultivate either groundnuts, rice, or onions as their main crop. While groundnut farmers primarily sell their groundnuts either individually or collectively for private gains, onion farmers have been observed to engage in joint collection and commercialization of firewood. Rice farmers use a part of their gains from selling their rice (privately or collectively) for financing irrigation systems.

For collective commercialization of any type of produce, the RPOs’ leaders will typically negotiate with a contract buyer who offers a higher price than the local traders if a minimum amount of produce is sold. This quantity is typically too large to be reached by just one individual farmer such that complying with the contract requires coordination among the members of the RPO. Moreover, these contracts tend to be rather discrete in the sense that they are void if a certain threshold of aggregated produce is not met and payments are usually made some time after the farmers delivered their produce to the RPO for collective commercialization (Bernard et al. Reference Bernard, Sene, Viceisza and Wouterse2014). In our sample, two thirds of the farmers who report to have sold to traders in the 2010/2011 agricultural season (March 2010 to March 2011) state that they received the payment on the day of the transaction, while only one quarter of the farmers who sold through the RPO report to have received the payment on the day the transaction took place.

Senegalese RPOs appear to be facing significant constraints when it comes to collective action in the form of collective commercialization of agricultural produce. Although RPOs in our sample were selected based on collective commercialization being one of their main objectives, only roughly one third of the RPOs in our sample report to have engaged in collective commercialization in the 2010/2011 agricultural season.

While a lack of financial means and technical capacities at the RPO level are likely to be significant constraints to collective commercialization, members’ perception of collective commercialization and issues of coordination between members also seem to represent important constraints, independent of the type of crop to be sold collectively. Leadership has been shown to be a key element of successful coordination within RPOs (Agrawal Reference Agrawal2001; Markelova et al. Reference Markelova, Meinzen-Dick, Hellin and Dohrn2009; Bernard and Wouterse Reference Bernard and Wouterse2015). In those RPOs that engaged in collective commercialization, only 44 percent of the farmers delivered a part of their produce to the RPO to be sold collectively. Respondents state three main reasons for why they chose to sell individually in spot market-like transactions to local traders rather than collectively through their RPO: most find collective commercialization to be too risky (without specifying the exact nature of this risk), some do not believe that it offers them a better price than selling individually, and for others, collective commercialization appears to be a complicated process that takes up too much time.

RPOs engaging in collective commercialization differ significantly from those who did not engage in collective sales in the 2010/2011 agricultural season (see Table A1). Their members are younger, more likely to own more motorcycles, are more likely to have savings accounts with banks and MFIs, and have larger fields. They cultivate groundnuts and rice more often than onions. Moreover, we find significantly higher average trust levels in RPOs that engaged in collective commercialization than in those that did not.

Farmers who contributed a part of their produce to collective commercialization in the season prior to the baseline data collection appear to be significantly more trusting than those who did not contribute (see Table A2). Moreover, they are more likely to be literate than those who did not contribute, less likely to be a member of a RPO, less likely to be related to a leader, more likely to have a savings account, have larger fields, are more likely to own a motorcycle, and are younger. They are also more likely to come from ethnically heterogeneous RPOs, have been members of their RPO for a shorter time period, are more likely to be cultivating groundnuts, and less likely to be cultivating onions. Looking at different measures for trust, we find a significantly positive pairwise correlation between several trust measures and an indicator for whether a RPO engaged in collective commercialization. Similary, we find a significantly positive pairwise correlation between several trust measures and individual participation in collective commercialization (see Table A3). Overall, these results indicate that trust in leaders and in members may play a significant role in fostering collective commercialization. Aiming at enhancing trust within RPOs to encourage collective commercialization, we organized a three-day training and discussion around the potential benefits and difficulties of coordinating toward collective commercialization.

Trust and collective action: theory and hypotheses

Ostrom and Ahn (Reference Ostrom and Ahn2003) provide a theoretical framework for analyzing issues of collective action that can be applied to analyzing collective commercialization as an example of collective action. Success and failure of collective action is determined by a complex configuration of various factors that they categorize as forms of social capital. They argue that the different forms of social capital—trustworthiness, networks, and institutions—enhance trust which they see as the core link between social capital and collective action. Figure 1 shows the relationships between the forms of social capital, trust, and collective action.

Fig. 1. Social capital, trust, and collective action (Ostrom and Ahn Reference Ostrom and Ahn2003, p. xvii).

Trust reduces opportunistic behavior and, thus, transaction costs (Putnam Reference Putnam1993; Collier Reference Collier2002). Applied to the context of collective commercialization in RPOs, farmers who are organized in RPOs need to be trustworthy and sufficiently connected with each other, and the RPO needs to provide adequate institutions for trust to be prevalent within the RPO and for collective commercialization to be successful. Trust in leaders allows the trusting farmer to commit to commercialization despite the risk of loss if the leaders do not behave as the trusting farmer expected. Trust in members allows the trusting farmer to commit to commercialization despite the risk of loss if the fellow farmers do not participate in the joint endeavors as expected by the trusting farmer.

If the training intervention increases the different forms of social capital, it will enhance trust and ultimately lead to more successful collective commercialization and potentially other collective endeavors in treated RPOs. Putnam (Reference Putnam1993) distinguishes between horizontal networks bringing together individuals of equivalent status, for example, fellow members or fellow leaders, and vertical networks bringing together individuals from different hierarchical levels, for example, members and leaders. Dense horizontal networks with the capability of efficiently transmitting information across individuals create incentives to behave in a trustworthy manner. Repeated interactions among individuals are seen as a sign of robust networks (Ostrom and Ahn Reference Ostrom and Ahn2003). We expect that the training intervention strengthens existing horizontal and vertical networks and may even create new ones through repeated interactions of farmers. Put differently, we expect the intervention to have a positive effect on participating leaders’ trust in other leaders and on participating members’ trust in other members through tightening existing bonds and creating new ones during and after the joint field trip to the training. Similarly, in RPOs where both members and leaders participate, the training may increase trust between leaders and members through strengthening and/or creating vertical networks. We hypothesize that such increases in social capital will materialize in a positive treatment effect on trust in members and on trust in leaders.

Both networks and institutions change the incentive structure of individuals with institutions creating incentives for both the trusting and the trusted party to behave in a trustworthy manner (Ostrom and Ahn Reference Ostrom and Ahn2003). We expect that the intervention clarifies the process of collective commercialization as one form of collective action and the rules involved and, thus, creates a common understanding of the relevant institutions. In other words, trained members (leaders) may obtain a better understanding of leaders’ (members’) role and position, which may increase members’ (leaders’) trust in leaders (members) even if no leaders (members) are present during the training. Further, the intervention may also create positive spillover effects, that is, increase nonparticipants’ trust in leaders and members. Participants may convey their newly acquired understanding of leaders’ and members’ role and position to nonparticipants during the general assembly held shortly after the training. We hypothesize that such increases in social capital will again materialize in a positive treatment effect on trust in members and on trust in leaders.

Measures of trust

We consider six survey measures of trust capturing different aspects of trust within a RPO that we consider to be important for successful collective commercialization. We distinguish between trust in leaders and trust in members. Originally, all trust measures were recorded on a four-point Likert-type scale. The items to choose from are (1) “Do not agree at all”, (2) “Do not really agree”, (3) “Agree”, and (4) “Fully agree”. However, given that the average trust levels at baseline are well above 3 for all measures of trust, we use binary variables as regressands in the empirical analysis that are equal to one if respondents chose “fully agree”. Hence, for trust in leaders, we created four binary measures: negotiation equal to one if the respondent fully agrees with “The RPO's board is capable of negotiating better prices for our produce than I am capable of myself.”, and a binary variable integrity equal to one if the respondent fully agrees with “Board members defend the RPO's interests and their own interests in equal measure.”, a binary variable information equal to one if the respondent fully agrees with “If the RPO's board says that I can achieve better prices if I wait a little, they do so because they have adequate information.”, and finally, a binary variable efficiency equal to one if the respondent fully agrees with “If I invest my money or my produce in the RPO, it will be used efficiently.” For trust in members, we created two binary measures: a binary variable general equal to one if the respondent fully agrees with “The majority of people in my RPO are trustworthy.”, and a binary variable farming equal to one if the respondent fully agrees with “I can trust the people from the RPO to look after my field during an absence of two months.” Moreover, we create two comprehensive indices of trust. For trust_sum, we take the sum over the six binary trust measures such that values of trust_sum range from 0 to 6. For trust_factor, we run a factor analysis with the six binary trust measures. Values of the resulting index of trust range from −1.74 to 0.81.

Treatment effects at individual level

In the following, we now present treatment effects at the individual level, that is, the effects of the (invitation to the) training on trust based on individual-level regressions permitting us to disentangle the effect of having personally been invited from a potential spillover effect on noninvited farmers within treated RPOs. To further study spillover effects and direct treatment effects, we estimate:

(1) $$\Delta trust_{ig} = \alpha_0 + \alpha_1 spillover\_invitation_{ig} + \alpha_2 personal\_invitation_i + \tau_{ig}$$

where Δtrust _ig  = trust _ig,t=1 − trust _ig,t=0 is the difference between trust of person i in RPO g at endline t = 1 and baseline t = 0, $spillover\_invitation_{ig}$ is a binary variable equal to one if some member or leader from RPO g other than person i has received an invitation, $personal\_invitation_i$ is a binary variable equal to one if person i has received an invitation to participate in the training intervention, and τ _ig is a random disturbance term for individual i in RPO g. Hence, the estimated coefficient $\widehat {\alpha _1}$ captures any potential spillover effects, while $\widehat {\alpha _2}$ captures the effect of having personally been invited.

Again, we estimate the causal effect of the training using an instrumental variable strategy. We estimate:

(2) $$\Delta trust_{ig} = \theta_0 + \theta_1 spillover\_training_{ig} + \theta_2 personal\_training_i + \kappa_{ig}$$

where $spillover\_training_{ig}$ is a binary variable equal to one if some member or leader from RPO g other than person i has participated in the training, and $personal\_training_i$ is a binary variable equal to one if person i has participated in the training intervention. We instrument for $spillover\_training_{ig}$ and $personal\_training_i$ using $RPO\_invitation_g$ and $personal\_invitation_i$ . Hence, the estimated coefficient $\widehat {\theta _1}$ captures any potential spillover effects, while $\widehat {\theta _2}$ captures the effect of having personally been trained.

To gain further insights into spillover effects, we also estimate regressions as specified in equation 2 separately for subsamples containing only members or only leaders. For the subsample of members, we further split the spillover effect by treatment type, that is, by whether only members were treated, only leaders, or both. In these regressions, we instrument for $spillover\_training_{ig}$ and $personal\_training_i$ by $RPO\_invitation_g$ and $personal\_invitation_i$ . We instrument for $spillover\_training\_OL_{ig}$ , $spillover\_training\_OM_{ig}$ , $spillover\_training\_LM_{ig}$ , and $personal\_training_i$ using $RPO\_invitation\_OL_g$ , $RPO\_invitation\_LM_g$ , and $personal\_invitation_i$ . The highly significant correlation coefficients range between −0.2863 and 0.9021.

Regression results

In Table 1, we report the estimated treatment effects on individual trust. We present intention-to-treat effects based on reduced-form regressions as specified in equation 1, and the results from instrumental variable estimations as specified in equation 2. The results hint at a significantly positive treatment effect of having personally received an invitation. In detail, we find a significant personal intention-to-treat effect of 13 to 20 percentage points for all individual trust measures but trust in leaders’ negotiation skills.Footnote ³ These effects increase to 15 to 24 percentage points when considering the treatment effect of the training.

Table 1. Treatment effects at individual level

Notes: The number of invited/trained persons in treated RPOs ranges from 2 to 16. Hence, it is impossible to be the only invited/treated person in a treated RPO. We report coefficients from regressions using both baseline and endline data regressing changes in trust on a constant, a binary indicator for personal treatment and a binary indicator for treatment of others. For instrumental variable regressions we report coefficients from two-stage estimations using $RPO\_invitation$ and $personal\_invitation$ as instruments for $spillover\_training$ and $personal\_training$ . Standard errors are reported in parentheses (clustered at RPO level).

*p < 0.10, **p < 0.05, ***p < 0.01.

To gain further insights into spillover effects and direct treatment effects, we rerun estimations as specified in equations 1 and 2 on subsamples containing only members or only leaders. The results for the subsample of leaders are omitted since all estimates were insignificant, which presumably is linked to the small sample size of leaders (N = 168). The results for the subsample of leaders were inconclusive.

In Table 2, the results are shown in the subsample of members. In Table 2, we present intention-to-treat effects based on reduced form regressions on a subsample of members as specified in equation 1 and the results from instrumental variable estimations on a subsample of members as specified in equation 2. For members we find both a significantly positive personal treatment effect and a significantly positive spillover effect. In particular, we find significantly positive direct treatment effects of 19 to 20 percentage points for trust in leaders’ integrity and their informational advantage as well as a significant personal intention-to-treat effect of 18 percentage points on general trust and farming-specific trust in members. For trust in leaders’ negotiation skills, their integrity, and their informational advantages, the results appear to be indicating a significantly positive spillover effect of 15 to 16 percentage points. This may be attributed to untrained members believing that trained leaders have gained relevant skills (trust in competence) and that trained leaders are less likely to privately capture rents (trust in motives). We further split these spillover effects into spillovers in RPOs where only leaders were invited (participated), RPOs where only members were invited (participated), and RPOs where both leaders and members were invited (participated). The results presented in Table 2 hint at the spillover effects in trust in leaders being most pronounced in RPOs where either only leaders or only members were invited (participated).

Table 2. Treatment effects at individual level—subsample of members

Notes: We report coefficients from regressions on the subsample of members using both baseline and endline data regressing changes in trust on a constant, a binary indicator for personal treatment and a binary indicator for treatment of others (binary indicators for treatment of others by group type). For instrumental variable regressions, we report coefficients from two-stage estimations using $RPO\_invitation$ and $personal\_invitation$ as instruments for $spillover\_training$ and $personal\_training$ and from two-stage estimations using $RPO\_invitation\_OL$ , $RPO\_invitation\_OM$ , $RPO\_invitation\_LM$ and $personal\_invitation$ as instruments for $spillover\_training\_OL$ , $spillover\_training\_OM$ , $spillover\_training\_LM$ and $personal\_training$ . Standard errors are reported in parentheses (clustered at RPO level).

*p < 0.10, **p < 0.05, ***p < 0.01.

In Table 3, we examine effects beyond trust. Besides having an impact on intra-RPO trust in both members and leaders, the training intervention may have also affected farmers’ perception of their RPO. Data about the true and perceived level of information are scarce in our dataset. We have information on how well farmers know their own RPO, namely whether they believe to know how many members their RPO has. This question was included only in the endline survey. In Table 3, we report the percentage of farmers stating that they know the size of their RPO by intended treatment status. On average, farmers from invited RPOs feel significantly better informed about the size of their RPO than farmers in the control group. This difference is dominated by farmers who were personally invited, but also noninvited farmers in the treatment group fare better than farmers in the control group. Apparently, the training was able to provide farmers with information they were previously lacking. In line with this, we find that farmers in invited RPOs appear better informed about the potential benefits of an efficiently working RPO and also a significant difference when looking at the evaluation of the actual RPOs (see Table 3). Farmers from invited RPOs are significantly more confident than farmers from the control group that their RPO can help overcome hindrances that they encounter when selling individually.

Table 3. Perception of RPO at endline by intended treatment status

Notes: We report the percentage of persons answering the questions affirmatively at endline as well as the p-value of two-sample t-tests with the null of equal means. Samples in panels B and C are smaller than the sample in panel A as people who stated to not be facing any constraints when selling individually did not answer these questions.