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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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Citations
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Journal ArticleDOI

Shocks to food market systems: A network approach

TL;DR: In this paper, the authors augment an error correction model with a network approach and apply it to Tanzania to better understand how cross-border price shocks are transmitted to domestic food markets, and they show that a network-based approach in conjunction with time series analysis can enhance their understanding of the origins and channels through which shocks were transmitted to food markets.

Sooner Rather than Later: Social Networks and Technology Adoption

TL;DR: In this paper , the authors estimate the causal effect of social networks on technology adoption using data from a randomised experiment in Kenya and find that an additional social connection with a treated farmer causes an untreated farmer to be 2.25 pp more likely to adopt TCB 6-18 months post-intervention, but not in the longer term.
Book ChapterDOI

Evaluating the Natural Variability in Generative Models for Complex Networks

TL;DR: The empirical analysis quantifying the ability of network models to replicate characteristics of a population of networks highlights the need for rethinking the way the goodness of fit of new and existing network models is evaluated.
Journal ArticleDOI

The role of social networks in the adoption of competing new technologies in Ghana

TL;DR: In this paper , the authors examined the impact of social networks on farmers' adoption of competing improved soybean varieties in Ghana, using a spatial autoregressive multinomial probit model to examine how neighbours' varietal and cross-varietal adoption of improved varieties affect a farmer's adoption decision in the social network.
Posted Content

Universal Scaling in Complex Substitutive Systems

TL;DR: It is found that, ranging from mobile handsets to automobiles to smart phone apps, early growth patterns in substitutive systems follow a power law with non-integer exponents, in sharp contrast to the exponential growth customary in spreading phenomena.
References
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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.
Journal ArticleDOI

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