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.
|Number of pages||32|
|State||Published - 1 Jan 2020|
Bibliographical noteFunding Information:
We are grateful to Daron Acemoglu, Abhijit Banerjee, Esther Duflo, Ben Golub, Matthew O. Jackson, Markus Mobius, Omer Tamuz, Adam Szeidl, and Chris Udry for extremely helpful discussions. Essential feedback was provided by Kalyan Chatterjee, Juan Dubra, Rema Hanna, Ben Olken, Evan Sadler, Rob Townsend, Xiao Yu Wang, Luis Zerme?o, and participants at numerous seminars and conferences. We also thank Devika Lakhote, Gowri Nagaraj, Adriana Paz, Mounu Prem, Alejandra Rogel, Diego Dominguez, Jos? Ram?n Enr?quez, Piotr Evdokimov, Andrei Gomberg, and Juan Pablo Micozzi for their assistance. We thank the Russell Sage Behavioral Economics Grant, the NSF GRFP (Chandrasekhar), and the Bank of Spain and Caja Madrid (Larreguy) for financial support.
© 2020 The Econometric Society
- Bayesian learning
- DeGroot learning
- social learning