Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials

Banerjee, Abhijit; Chandrasekhar, Arun G.; Duflo, Esther; Jackson, Matthew O.

doi:10.1093/restud/rdz008

Cited by 183 publications

(181 citation statements)

References 55 publications

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“…We initially chose the village setting because social learning through networks is of the utmost importance in rural environments; information about new technologies (Conley and Udry (2010)), microfinance (Banerjee, Chandrasekhar, Duflo, and Jackson (2013)), politics (Alt, Jensen, Larreguy, Lassen, and Marshall (2019), Cruz, Labonne, and Querubin (2018), Duarte, Finan, Larreguy, and Schechter (2019)), among other things, propagates regularly through social networks. The villages range from 1.5 to 3.5 hours' drive from Bangalore.…”

Section: Settingmentioning

confidence: 99%

“…Henceforth we omit the designation of coarse communication which is to be assumed unless noted otherwise 5. These technical choices are made to preserve the sparseness of the overall network.…”

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confidence: 99%

“…This dynamic of reduced triadic closure, even among information links and not just informal insurance links, due to the entry of microcredit is documented inBanerjee, Chandrasekhar, Duflo, and Jackson (2018).8 Eyster and Rabin (2014) showed that "social learning-rules that are strictly and boundedly increasing in private signals as well as neglect redundancy, in which no player anti-imitates any other have, with positive probability, that society converges to the action that corresponds to certain beliefs in the wrong state. "…”

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Testing Models of Social Learning on Networks: Evidence From Two Experiments

Chandrasekhar

Larreguy

Xandri

2020

ECTA

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We theoretically and empirically study an incomplete information model of social learning. Agents initially guess the binary state of the world after observing a private signal. In subsequent rounds, agents observe their network neighbors' previous guesses before guessing again. Agents are drawn from a mixture of learning types-Bayesian, who face incomplete information about others' types, and DeGroot, who average their neighbors' previous period guesses and follow the majority. We study (1) learning features of both types of agents in our incomplete information model;(2) what network structures lead to failures of asymptotic learning; (3) whether realistic networks exhibit such structures. We conducted lab experiments with 665 subjects in Indian villages and 350 students from ITAM in Mexico. We perform a reduced-form analysis and then structurally estimate the mixing parameter, finding the share of Bayesian agents to be 10% and 50% in the Indian-villager and Mexican-student samples, respectively.A. G. CHANDRASEKHAR, H. LARREGUY, AND J. P. XANDRI information from their network neighbors when engaging in learning. This constraint may be for many reasons, including but not limited to operating in settings where learning is through observations of others' actions or the costs of communicating very complex information are too high and therefore only summaries are transmitted. 2 We study such a coarse communication environment. We focus on a setting in which individuals receive signals about an unknown binary state of the world in the first period and, in subsequent rounds, they communicate to their neighbors their best (binary) guess about the state of the world. 3 In this ubiquitous setting, if all agents are Bayesian, and there is common knowledge of this, under mild assumptions, learning will be asymptotically efficient in large networks (see Gale and Kariv (2003) and Mossel and Tamuz (2010) for a myopic learning environment and Mossel, Sly, and Tamuz (2015) for a strategic learning environment). However, if all agents update their guess as the majority of their neighbors' prior guesses-as modeled by a coarse DeGroot model of learning, also known as the majority voting model (Liggett (1985))-then it is possible that a non-trivial set of agents will end up stuck making the wrong guess. In practice, it might be that there is a mix of sophisticated (Bayesian) and naive (DeGroot) learners, and that Bayesians are aware of this and incorporate it in their calculations. Such an incomplete information model, its relevance, and implications for asymptotic learning have not been studied in our coarse communication environment.This paper develops an incomplete information model of social learning with coarse communication on a network in which agents can potentially be Bayesian or DeGroot, and agents have common knowledge of the distribution of Bayesian or DeGroot types in the population. Bayesian agents then learn in an environment of incomplete information. The model nests the two extreme cases-complete information all-Ba...

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Section: Settingmentioning

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Testing Models of Social Learning on Networks: Evidence From Two Experiments

Chandrasekhar

Larreguy

Xandri

2020

ECTA

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“…We chose to set our experiment in villages that coincide with the social network and demographic data set previously collected, in part by the authors (and also described in Banerjee et al (forthcoming )). In our field experiment, we match participants to this unique data set.…”

Section: Data and Experimental Designmentioning

confidence: 99%

“…Equipped with this framework, we pair our experiment with detailed network data collected in part by the authors in previous work (Banerjee, Chandrasekhar, Duflo, and Jackson ( forthcoming )). These household‐level network data comprise 12 dimensions of interactions across all potential pairs of households in each of the 60 study villages.…”

Section: Introductionmentioning

confidence: 99%

Social Networks, Reputation, and Commitment: Evidence From a Savings Monitors Experiment

Breza¹,

Chandrasekhar²

2019

Econometrica

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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.

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