Abstract. We examine how participation in a microfinance program diffuses through social networks, using detailed demographic, social network, and participation data from 43 villages in South India. 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 significantly higher when the injection points have higher eigenvector centrality. We also 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 nonparticipants. We find that participants are significantly more likely to pass information on to friends and acquaintances than informed non-participants. However, information passing by non-participants is still substantial and significant, accounting for roughly one-third of informedness and participation. We also find that, once we have properly conditioned on an individual being informed, her decision to participate is not significantly affected by the participation of her acquaintances.JEL Classification Codes: D85, D13, G21, L14, O12, O16, Z13

Abstract. We examine how participation in a microfinance program diffuses through social networks, using detailed demographic, social network, and participation data from 43 villages in South India. 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 significantly higher when the injection points have higher eigenvector centrality. We also 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 nonparticipants. We find that participants are significantly more likely to pass information on to friends and acquaintances than informed non-participants. However, information passing by non-participants is still substantial and significant, accounting for roughly one-third of informedness and participation. We also find that, once we have properly conditioned on an individual being informed, her decision to participate is not significantly affected by the participation of her acquaintances.JEL Classification Codes: D85, D13, G21, L14, O12, O16, Z13

Is it possible to identify individuals who are highly central in a community without gathering any network information, simply by asking a few people? If we use people's nominees as seeds for a diffusion process, will it be successful? We explore these questions theoretically, via surveys, and via field experiments. We show via a model of information flow how members of a community can, just by tracking gossip about others, identify highly central individuals in their network. Asking villagers in rural Indian villages to name good seeds for diffusion, we find that they accurately nominate those who are central according to a measure tailored for diffusion -not just those with many friends or in powerful positions. Finally, we run a randomized field experiment in 213 other villages that tests how effective it is to use such nominations as seeds for a diffusion process. Relative to random seeds or those with high social status, hitting at least one seed nominated by villagers leads to more than a 65% increase in the spread of information.JEL Classification Codes: D85, D13, L14, O12, Z13

This chapter surveys econometric network formation models. Its goal is to acquaint the readers, in a self-contained manner, with a number of network formation models used in the graph theory, statistics, sociology, and econometrics literature, with a view to how well they map to real-world data, the sorts of economic microfoundations they implicitly assume, and their econometric properties. A major difficulty in the study of network formation is that the researcher typically has a data set consisting of a single network observed in a single period. Key questions include whether a researcher can identify, and develop consistent estimators of, the parameters driving network formation when observing a single large network. Estimation involves a number of challenges, including but not limited to the degree of correlation between linking decisions, the ability to reproduce realistic patterns of network structure, concerns about multiple equilibria, and missing data.

No abstract

We use unique data from 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 different network structures, which we show are borne out in the data. We apply our findings to a community-based targeting program, where villagers chose which households should receive aid, and show that networks the model predicts to be more diffusive differentially benefit from community targeting.

Abstract. Social network data is often prohibitively expensive to collect, limiting empirical network research. Typical economic network mapping requires (1) enumerating a census, (2) eliciting the names of all network links for each individual, (3) matching the list of social connections to the census, and (4) repeating (1)- (3) across many networks. In settings requiring field surveys, steps (2)- (3) can be very expensive. In other network populations such as financial intermediaries or high-risk groups, proprietary data and privacy concerns may render (2)- (3) impossible. Both restrict the accessibility of high-quality networks research to investigators with considerable resources.We propose an inexpensive and feasible strategy for network elicitation using Aggregated Relational Data (ARD) -responses to questions of the form "How many of your social connections have trait k?" Our method uses ARD to recover the parameters of a general network formation model, which in turn, permits the estimation of any arbitrary node-or graph-level statistic. The method works well in simulations and in matching a range of network characteristics in real-world graphs from 75 Indian villages. Moreover, we replicate the results of two field experiments that involved collecting network data. We show that the researchers would have drawn similar conclusions using ARD alone. Finally, using calculations from J-PAL fieldwork, we show that in rural India, for example, ARD surveys are 80% cheaper than full network surveys. JEL Classification Codes: D85, C83, L14

We conduct an experiment to study whether individuals save more when information about the progress toward their self‐set savings goal is shared with another village member (a “monitor”). We develop a reputational framework to explore how a monitor's effectiveness depends on her network position. Savers who care about whether others perceive them as responsible should save more with central monitors, who more widely disseminate information, and proximate monitors, who pass information to individuals with whom the saver interacts frequently. We randomly assign monitors to savers and find that monitors on average increase savings by 36%. Consistent with the framework, more central and proximate monitors lead to larger increases in savings. Moreover, information flows through the network, with 63% of monitors telling others about the saver's progress. Fifteen months after the conclusion of the experiment, other villagers have updated their beliefs about the saver's responsibility in response to the intervention.