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

Essays on social networks in development economics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 203-210).This thesis examines the role that social networks play in developing economies. The first two chapters analyze econometric issues that arise when researchers work with sampled network data. The final two chapters study how the embedding of agents in a network affects a group's ability to overcome weak contracting institutions and what models of social learning are important in describing the diffusion of information. These chapters make use of experiments that I conducted in rural Karnataka, India. The first chapter (co-authored with Randall Lewis) examines the econometric difficulties that applied researchers face when using partially observed network data. In applied work, researchers generally construct networks from data collected from a partial sample of nodes. Treating this sampled network as the true network of interest, the researcher constructs statistics to describe the network or specific nodes and employs these statistics in regression or GMM analysis. This chapter shows that even if nodes are selected randomly, partial sampling leads to non-classical measurement error and therefore bias in estimates of the regression coefficients or GMM parameters. We provide analytical and numerical examples to illustrate the severity of the biases in common applications and discuss possible solutions. Our analysis of the sampling problem as well as the proposed solutions are applied to rich network data of Banerjee et al. (2012) from 43 villages in Karnataka, India. In the second chapter, 1 develop an econometric method to cope with sampled network data. I develop a method, graphical reconstruction, by which a researcher can consistently estimate the economic parameters of interest. Graphical reconstruction uses the available (partial) network data to predict the missing links and uses these predictions to mitigate the biases. As each network may be generated by a different network formation model, the asymptotic theory allows for heterogeneity in the network formation process across graphs. The third chapter (co-authored with Cynthia Kinnan and Horacio Larreguy) analyzes how social networks affect the provision of informal insurance. Social networks are understood to play an important role in smoothing consumption risk, particularly in developing countries where formal contracts are limited and financial development is low. Yet understanding why social networks matter is confounded by endogeneity of risk-sharing partners. This chapter, first, examines the causal effect of close social ties between individuals on their ability to informally insure one another. Second, we examine how the interaction of social proximity and access to savings affects consumption smoothing. Theoretically, they could be complements or substitutes. Savings access may crowd out insurance unless social proximity is high, in which case it benefits the highly connected. Or savings may crowd out risk sharing among the highly connected while helping the less connected smooth risk intertemporally. By conducting a framed field experiment in Karnataka, India, we study the relationships between inability to commit to insurance, ability to save, and social proximity. We find that limited commitment reduces risk sharing, but social proximity(cont.) substitutes for commitment. On net, savings allows individuals to smooth risk that cannot be shared interpersonally, with the largest benefits for those who are weakly connected in the network. The final chapter (co-authored with my classmates Horacio Larreguy and Juan Pablo Xandri) attempts to determine which models of social learning on networks best describe empirical behavior. Theory has focused on two leading models of social learning on networks: Bayesian and DeGroot rules of thumb learning. These models can yield greatly divergent behavior; individuals employing rules of thumb often double-count information and may not exhibit convergent behavior in the long run. By conducting a unique lab experiment in rural Karnataka, India, set up to exactly differentiate between these two models, we test which model best describes social learning processes on networks. We study experiments in which seven individuals are placed into a network, each with full knowledge of its structure. The participants attempt to learn the underlying (binary) state of the world. Individuals receive independent, identically distributed signals about the state in the first period only; thereafter, individuals make guesses about the underlying state of the world and these guesses are transmitted to their neighbors at the beginning of the following round. We consider various environments including incomplete information Bayesian models and provide evidence that individuals are best described by DeGroot models wherein they either take simple majority of opinions in their neighborhood.by Arun Gautham Chandrasekhar.Ph.D

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

Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia

We use unique data from over 600 Indonesian communities on what individuals know about the poverty status of others to study how network structure influences information aggregation. We develop a model of semi-Bayesian learning on networks, which we structurally estimate using within-village data. The model generates qualitative predictions about how cross-village patterns of learning relate to network structure, which we show are borne out in the data. We apply our findings to a community-based targeting program, where citizens chose households to receive aid, and show that the networks that the model predicts to be more diffusive differentially benefit from community targeting