Experimental Design Details
Local Leaders: social capital and institutions arm
For this treatment arm we will rely on existing institutions on these communities, which are a natural benchmark as they are the typical entry points in traditional extension programs. For such local institutions we will consider existing local organizations, such as political committees or water joints. The injection points will be the presidents of such organizations. These leaders are easier and less costly to approach, given that the institutional structure is already in place. Local leaders are appointed by political reasons. They may or may not have a central positionality in the local network. Thus, the scope of their message diffusion could be outperformed by more central individuals.
Diffusion central: network theory/statistics arm
For this treatment arm we calculate a centrality measure for all network members and identify the members that appear to be the most central. Different centrality measures have been studied and tested in the literature and for this study we use the centrality measure proposed by Banerjee et al (2013). Banerjee et. al (2013) develop a model to measure the effectiveness of every node as an injection point to increase diffusion of a newly available microfinance program. The authors find that the centrality measure they call “communication centrality” is the one that most strongly predicts adoption. This communication centrality is costly to estimate, so the authors develop an easily computable proxy that they call “diffusion centrality”. Banerjee et al (2013) show that diffusion centrality can be calculated by: DC(g;q,T)=[∑_(t=1)^T▒(qg)^t ].1 , where T are the iterations of information passing, q the probability of information passing, and g the adjacency matrix. If T=1, diffusion centrality is proportional to degree centrality. As T→∞, it becomes proportional to Katz-Bonacich centrality or eigen-vector centrality, depending on whether q is smaller or not than the inverse of the first eigen-value of the adjacency matrix. In the intermediate region of T, the indicator differs from existing measures. Any method that does not rely on the estimation of their model requires an appropriate choice of q. The authors suggest an intermediate value of q, given by the inverse of the first eigenvalue of the adjacency matrix, λ_1 (g). This is, the critical value of q for which the entries of (qg)^Ttend to 0 as T grows if q < 1/λ_1 and some entries diverge if q>1/λ_1.
We calculate this centrality measure for all network members and sort them by this measure. We have information on a sample of the network from 20 randomly selected farmers in each community (where each community has between 20 and 140 farmers). Given that we do not have the full network information, the measure of centrality might be biased. However, the rank order of the farmers in our observed network is preserved and, for identifying the injection points, this is a sufficient statistic.
Community nominated: the people’s voice arm
For this treatment arm we will check how does asking people to tell us who will be the best person to disseminate information compares to the other treatments. To obtain such information, we will ask members of the community who they think is the most suited farmer to spread out information about a new technological innovation. Banerjee et. al (2016) find that asking a random sample of individuals to nominate best suited people to spread information highly correlates with diffusion centrality; and that these nominees are more central than traditional leaders and geographical central individuals.
Given that we deal with small communities, individuals may even know about other important characteristics of their fellow farmers. For example, they might know of farmers who are more prone to innovate, have more experience, know better about agriculture, are more charismatic; all important characteristics that might influence diffusion and adoption. The idea is that this treatment arm can incorporates both centrality and such other (unobservable to the researcher) characteristics.
Random farmers: natural diffusion arm
To have a benchmark for this comparison, we will use random farmers as injection points. Given that we have network information for each community, in a group of them we will randomly choose a farmer to deliver the information to and compare its message reach with the other groups. How effective this group performs is a central part of this study.