This paper examines how participation in a microfinance program diffuses through social networks in 43 villages in South India. The authors use detailed demographic, social network, and participation data to explore the role of initial "injection points" and network characteristics in the diffusion process. They find that microfinance participation is significantly higher when the injection points have higher eigenvector centrality, indicating their importance in spreading information. The study also estimates structural models to distinguish between basic information transmission and peer influence. Key findings include: 1. **Information Transmission**: Participants are more likely to pass information to friends and acquaintances than non-participants, but non-participants also transmit significant amounts of information, accounting for about one-third of informedness and participation. 2. **Peer Influence**: Once an individual is informed, her decision to participate is not significantly influenced by the participation of her acquaintances. 3. **Network Characteristics**: While other village-level measures of network connectivity (e.g., average degree, clustering) do not significantly affect participation, the eigenvector centrality of injection points plays a crucial role in the diffusion process. The paper contributes to the literature on diffusion by distinguishing between information transmission and peer effects, providing insights into how social networks influence the spread of microfinance programs.This paper examines how participation in a microfinance program diffuses through social networks in 43 villages in South India. The authors use detailed demographic, social network, and participation data to explore the role of initial "injection points" and network characteristics in the diffusion process. They find that microfinance participation is significantly higher when the injection points have higher eigenvector centrality, indicating their importance in spreading information. The study also estimates structural models to distinguish between basic information transmission and peer influence. Key findings include: 1. **Information Transmission**: Participants are more likely to pass information to friends and acquaintances than non-participants, but non-participants also transmit significant amounts of information, accounting for about one-third of informedness and participation. 2. **Peer Influence**: Once an individual is informed, her decision to participate is not significantly influenced by the participation of her acquaintances. 3. **Network Characteristics**: While other village-level measures of network connectivity (e.g., average degree, clustering) do not significantly affect participation, the eigenvector centrality of injection points plays a crucial role in the diffusion process. The paper contributes to the literature on diffusion by distinguishing between information transmission and peer effects, providing insights into how social networks influence the spread of microfinance programs.