Research Article

The Diffusion of Microfinance

Science  26 Jul 2013:
Vol. 341, Issue 6144, pp.
DOI: 10.1126/science.1236498

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Structured Abstract


How do the network positions of the first individuals in a society to receive information about a new product affect its eventual diffusion? To answer this question, we develop a model of information diffusion through a social network that discriminates between information passing (individuals must be aware of the product before they can adopt it, and they can learn from their friends) and endorsement (the decisions of informed individuals to adopt the product might be influenced by their friends’ decisions). We apply it to the diffusion of microfinance loans, in a setting where the set of potentially first-informed individuals is known. We then propose two new measures of how “central” individuals are in their social network with regard to spreading information; the centrality of the first-informed individuals in a village helps significantly in predicting eventual adoption.

Embedded Image

Diffusion of information and participation. (Left) First-informed households have decided whether to participate and stochastically pass on information to their neighbors. (Right) Participation may affect the probability of passing information. Newly informed nodes make their decisions, possibly being influenced by the decisions of their neighbors. After newly informed nodes make their participation decisions, all informed nodes engage in another round of stochastic communication.


Six months before a microfinance institution entered 43 villages in India and began offering microfinance loans to villagers, we collected detailed network data by surveying households about a wide range of interactions. The microfinance institution began by inviting “leaders” (e.g., teachers, shopkeepers, savings group leaders) to an informational meeting and then asked them to spread information about the loans. Using the network data, the locations in the network of these first-informed villagers (or injection points), and data regarding the villagers’ subsequent participation, we estimate the parameters of our diffusion model using the method of simulated moments. The parameters of the model are validated by showing that the model correctly predicts the evolution of participation in each village over time. The model yields a new measure of the effectiveness of any given node as an injection point, which we call communication centrality. Finally, we develop an easily computed proxy for communication centrality, which we call diffusion centrality.


We find that a microfinance participant is seven times as likely to inform another household as a nonparticipant; nonetheless, information transmitted by nonparticipants is important and accounts for about one-third of the eventual informedness and participation in the village because nonparticipants are much more numerous. Once information passing is accounted for, an informed household’s decision to participate is not significantly dependent on how many of its neighbors have participated. Communication centrality, when applied to the set of first-informed individuals in a village, substantially outperforms other standard network measures of centrality in predicting microfinance participation in this context. Finally, the simpler proxy measure—diffusion centrality—is strongly correlated with communication centrality and inherits its predictive properties.


Our results suggest that a model of diffusion can distinguish information passing from endorsement effects, and that understanding the nature of transmission may be important in identifying the ideal places to inject information.

Infectious Information?

Much of the recent work on how individuals in social networks behave has relied upon the established Susceptible, Infectious, Recovered model developed in epidemiology. Information, however, differs from disease in one respect, namely that an individual might acquire information and yet not use it (or become “infected” by it). Banerjee et al. (1236498) examined the spread of information about microfinance and its adoption in 43 villages in Karnataka, a state in southern India. Adopters of microfinance were more likely to pass information about it on, and a new measure—diffusion centrality—of the first person to learn new information predicted how widely and quickly others would be likely to make use of it.


To study the impact of the choice of injection points in the diffusion of a new product in a society, we developed a model of word-of-mouth diffusion and then applied it to data on social networks and participation in a newly available microfinance loan program in 43 Indian villages. Our model allows us to distinguish information passing among neighbors from direct influence of neighbors’ participation decisions, as well as information passing by participants versus nonparticipants. The model estimates suggest that participants are seven times as likely to pass information compared to informed nonparticipants, but information passed by nonparticipants still accounts for roughly one-third of eventual participation. An informed household is not more likely to participate if its informed friends participate. We then propose two new measures of how effective a given household would be as an injection point. We show that the centrality of the injection points according to these measures constitutes a strong and significant predictor of eventual village-level participation.

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