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The Diffusion of Microfinance

TLDR
This article examined how participation in a micro-finance program diffuses through social networks and found that participants are significantly more likely to pass information on to friends and acquaintances than informed non-participants.
Abstract
We examine how participation in a microfinance program diffuses through social networks. We collected detailed demographic and social network data in 43 villages in South India before microfinance was introduced in those villages and then tracked eventual participation. We exploit exogenous variation in the importance (in a network sense) of the people who were first informed about the program, "the injection points". Microfinance participation is higher when the injection points have higher eigenvector centrality. We estimate structural models of diffusion that allow us to (i) determine the relative roles of basic information transmission versus other forms of peer influence, and (ii) distinguish information passing by participants and non-participants. We find that participants are significantly more likely to pass information on to friends and acquaintances than informed non-participants, but that information passing by non-participants is still substantial and significant, accounting for roughly a third of informedness and participation. We also find that, conditioned on being informed, an individual's decision is not significantly affected by the participation of her acquaintances.

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Network Structure and Education Outcomes: Evidence from a Field Experiment in Bangladesh

TL;DR: In this paper, the causal impact of network centrality on educational outcomes using field experiments in primary schools in Bangladesh was studied, and the authors found that groups that perform best are those with high Katz-Bonacich and key-player centralities.
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Role of Social Networks in Diversification o Income Sources in Rural India

TL;DR: In this article, the authors examined whether household's social networks influence income diversification in the Wayanad District of South India, and they developed a network econometric model based on a Spatial Autoregressive Econometric approach by replacing the spatial matrix with a network matrix.
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Optimal community structure for social contagions

TL;DR: A reversible social contagion model of community networks that includes the factor of social reinforcement is proposed that indicates that there is a first-order phase transition in the spreading dynamics, and that a hysteresis loop emerges in the system when there are a variety of initially-adopted seeds.
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Informative Social Interactions

TL;DR: The authors found that the extent to which the respondent's financial circle is informed about or participates in stockholding appears to influence perceptions of recent stock returns and, only through them, expectations of future returns.
References
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Journal ArticleDOI

Collective dynamics of small-world networks

TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.

A Contribution to the Mathematical Theory of Epidemics.

TL;DR: The present communication discussion will be limited to the case in which all members of the community are initially equally susceptible to the disease, and it will be further assumed that complete immunity is conferred by a single infection.
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A Simple Model of Herd Behavior

TL;DR: In this article, the authors analyze a sequential decision model in which each decision maker looks at the decisions made by previous decision makers in taking her own decision, and they show that the decision rules that are chosen by optimizing individuals will be characterized by herd behavior.
Proceedings ArticleDOI

Maximizing the spread of influence through a social network

TL;DR: An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.
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Epidemic Spreading in Scale-Free Networks

TL;DR: A dynamical model for the spreading of infections on scale-free networks is defined, finding the absence of an epidemic threshold and its associated critical behavior and this new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.