The Diffusion of Microfinance

By Abhijit Banerjee, Arun G. Chandrasekhar, Esther Duflo, Matthew O. Jackson. Science, 2013

1,353 citations recorded by the source corpus.

Introduction 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. 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. Methods 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. Results 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. Discussion 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. The first person in a village to learn about microfinance influences how widely the information spreads. 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.

Subfields: Business and International Management, Economics and Econometrics, Information Systems

View Paper: https://www.semanticscholar.org/paper/b16073ff14c88ccab398f9112d87dae610ab5041

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Citations in the corpus

The Operational Value of Social Media Information (2018, background): “The social media literature has identiﬁed the attention and endorsement effects as providing different types of informational signals that capture user behaviors (see Banerjee et al. 2012, Chen et al. 2011, Li and Wu 2014).”
Shared analyst coverage: Unifying momentum spillover effects (2018, neutral): “24 Another closely related centrality measure is diffusion centrality, as introduced by Banerjee et al. (2013) .”
The Miracle of Microfinance? Evidence from a Randomized Evaluation (2013, neutral): “This does not appear to be an anomaly: two other randomized interventions that have a similar design (in Morocco and in Mexico) also find relatively low take-up, while another study in rural South India that focuses specifically on take-up of microfinance also finds it to be low (Banerjee et al. 201”
Social Networks and the Decision to Insure (2015, background): “21As in Banerjee et al. (2012), we consider an undirected graph for most estimations in the paper.”
Information Heterogeneity and the Speed of Learning in Social Networks (2013, background): “Even though based on somewhat different premises, the theoretical implications of our model match Banerjee et al. (2012)’s empirical observations that (i) the network properties and locations of the exogenously informed individuals can substantially impact the diffusion of information; (ii) the exte”
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference (2020, methodology): “We use data collected by Banerjee et al. (2013) on social networks for 75 villages in Karnataka, India.”
Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia (2016, neutral): “Second, they could pursue an avenue along the lines of work by Banerjee et al. (2014)— making use of the fact that individuals in the network may have knowledge about the features of the network structure.”
Networks in the Understanding of Economic Behaviors (2014, neutral): “For example, consider the network pictured in Figure 1, which is a network of connections between households in one of the villages from the Banerjee et al. (2013) study.”
The Economic Impact of Social Ties: Evidence from German Reunification* (2011, neutral): “Beaman (2012), Kuhnen (2009), Shue (2011), and Banerjee et al. (2012). QUARTERLY JOURNAL OF ECONOMICS 1224”