Econometrica: Jan 2020, Volume 88, Issue 1

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

https://doi.org/10.3982/ECTA14407
p. 1-32

Arun G. Chandrasekhar, Horacio Larreguy, Juan Pablo Xandri

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.



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Supplement to "Testing Models of Social Learning on Networks: Evidence from Two Experiments"

In this appendix, we describe the algorithm for computing the actions in the complete and incomplete information Bayesian model.

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Supplement to "Testing Models of Social Learning on Networks: Evidence from Two Experiments"

This zip file contains the replication files for the manuscript. It also includes an additional appendix with material not found within the manuscript.

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