56 research outputs found
The Diffusion of Microfinance
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.
When Celebrities Speak: A Nationwide Twitter Experiment Promoting Vaccination in Indonesia
Celebrity endorsements are often sought to influence public opinion. We ask
whether celebrity endorsement per se has an effect beyond the fact that their
statements are seen by many, and whether on net their statements actually lead
people to change their beliefs. To do so, we conducted a nationwide Twitter
experiment in Indonesia with 46 high-profile celebrities and organizations,
with a total of 7.8 million followers, who agreed to let us randomly tweet or
retweet content promoting immunization from their accounts. Our design exploits
the structure of what information is passed on along a retweet chain on Twitter
to parse reach versus endorsement effects. Endorsements matter: tweets that
users can identify as being originated by a celebrity are far more likely to be
liked or retweeted by users than similar tweets seen by the same users but
without the celebrities' imprimatur. By contrast, explicitly citing sources in
the tweets actually reduces diffusion. By randomizing which celebrities tweeted
when, we find suggestive evidence that overall exposure to the campaign may
influence beliefs about vaccination and knowledge of immunization-seeking
behavior by one's network. Taken together, the findings suggest an important
role for celebrity endorsement.Comment: 55 pages, 13 tables, 6 figure
Consistently estimating graph statistics using Aggregated Relational Data
Aggregated Relational Data, known as ARD, capture information about a social
network by asking about the number of connections between a person and a group
with a particular characteristic, rather than asking about connections between
each pair of individuals directly. Breza et al. (Forthcoming) and McCormick and
Zheng (2015) relate ARD questions, consisting of survey items of the form "How
many people with characteristic X do you know?" to parametric statistical
models for complete graphs. In this paper, we propose criteria for consistent
estimation of individual and graph level statistics from ARD data
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Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia
We use a unique data-set from Indonesia on what individuals know about the income distribution in their village to test theories such as Jackson and Rogers (2007) that link information aggregation in networks to the structure of the network. The observed patterns are consistent with a basic diffusion model: more central individuals are better informed, and individuals are able to better evaluate the poverty status of those to whom they are more socially proximate. To understand what the theory predicts for cross-village patterns, we estimate a simple diffusion model using within-village variation, simulate network-level diffusion under this model for the over 600 different networks in our data, and use this simulated data to gauge what the simple diffusion model predicts for the cross-village relationship between information diffusion and network characteristics (e.g. clustering, density). The coefficients in these simulated regressions are generally consistent with relationships suggested in previous theoretical work, even though in our setting formal analytical predictions have not been derived. We then show that the qualitative predictions from the simulated model largely match the actual data in the sense that we obtain similar results both when the dependent variable is an empirical measure of the accuracy of a village’s aggregate information and when it is the simulation outcome. Finally, we consider a real-world application to community based targeting, where villagers chose which households should receive an anti-poverty program, and show that networks with better diffusive properties (as predicted by our model) differentially benefit from community based targeting policies
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