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This thesis contributes to a growing literature that explores relationships between social networks and innovation diffusion within a developing country context. Given this context, the networks of interest within this thesis are the offline interpersonal relationships between community members. Diffusion channels for new innovation are therefore limited to word-of-mouth communication, observation, and personal experience. Chapter 2 of this thesis analyses two policy tools in targeting these information gaps. The first is through social learning as part of a farmer extension program. The second combines social learning with experiential learning, reducing the cost to personal experimentation with subsidized improved input packages. Our results indicate that farmers who are exposed to both social learning and learning-by-doing more significantly impacts farmer productivity relative to those receiving no intervention and those exposed only to social learning. I interpret this result as an indication of learning-by-doing combined with social learning being a more effective strategy for facilitating adoption of technologies that have more heterogeneous returns to adoption. Chapter 3 of this thesis tests the difference in diffusion patterns that result by varying the network contact- point. Specifically, network contact-points are selected as being either the most central or least central individuals within the network. I find evidence that centrality affects the speed of distribution but does not affect the width of diffusion nor which individuals are participating within the diffusion process. Furthermore, large attenuation is observed throughout the diffusion process, which suggests the importance of selecting a sufficiently large set of lead community members for the spread of new technology. Chapter 4 combines a community-wide polling of network entry-points combined with detailed community network and socio-economic data. First we explore what attributes are prioritized by community members in nominating a resident farmer as an extension contact-point. Second, we use simulations to compare the diffusion spread of top-nominated individuals as network entry-points compared to entry-points that achieve maximal spread within diffusion simulations. We find that community members prioritize network connectedness, pro-social preferences, and socioeconomic indicators of gender, age, formal leadership, and education levels within their nomination decisions. Furthermore, receiving the top three most amount of nominations is found to be significantly correlated with selection as an optimal entry-point within the diffusion simulation. These results suggest that community-wide polling offers a less data-intensive opportunity to realize gains in diffusion warranted through network-based seeding. Chapter 5 explore whether an individual’s observed social preferences is correlated with an individual’s centrality within the network structure. Our results indicate that individuals with high centrality are more trusting and more trustworthy than individuals with lower centrality. Moreover, individuals with low centrality are treated worse in these interactions—people trust them less initially, and return less money to them. Within a group context, little evidence is found of more central individuals displaying more cooperative behavior. Instead, for group cooperation, when a single monitor can observe contribution decisions, the presence of a direct link and more mutual network connections with a monitor correlates to more cooperative behavior by that individual. Our results suggest that network centrality and pro-social preferences are related but more localized network ties are more strongly correlated with pro-sociality than overall network connectedness.

Leveraging social networks for agricultural development in Africa