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

Name: Data and Code for: Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia
Published: 2020-06-08
License: This work is licensed under an Other license created by the data depositor. Please refer to the LICENSE file, which should be located alongside the project data and documentation.

Principal Investigator(s): View help for Principal Investigator(s) Vivi Alatas, World Bank; Abhijit Banerjee, MIT; Arun Chandrasekhar, Stanford University; Rema Hanna, Harvard University; Benjamin Olken, MIT

Version: View help for Version V1

Version Title: View help for Version Title Migrated from AEA website

Name	File Type	Size	Last Modified
Base			06/08/2020 11:57:AM
Data Processing			06/08/2020 11:59:AM
Final Data			06/08/2020 11:59:AM
Outputs			06/08/2020 11:57:AM
ado			06/08/2020 12:06:PM
do			06/08/2020 12:07:PM
mfiles			06/08/2020 12:07:PM
LICENSE.txt	text/plain	14.6 KB	06/08/2020 07:57:AM
NOTE.txt	text/plain	440 bytes	06/08/2020 07:57:AM
Network Data.zip	application/zip	3.6 MB	06/08/2020 07:57:AM

Project Citation:

Alatas, Vivi, Banerjee, Abhijit, Chandrasekhar, Arun, Hanna, Rema, and Olken, Benjamin. Data and Code for: Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia. Nashville, TN: American Economic Association [publisher], 2020. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-06-08. https://doi.org/10.3886/E119802V1

Project Description

Summary: 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

Scope of Project

JEL Classification:
      D14 Household Saving; Personal Finance
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
      D85 Network Formation and Analysis: Theory
      I32 Measurement and Analysis of Poverty
      O12 Microeconomic Analyses of Economic Development
      Z13 Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

Geographic Coverage: Indonesia

Related Publications

Download this project

Published Versions

V1 [2020-06-08]

Export Metadata

Dublin Core

DDI 2.5

Report a Problem

Found a serious problem with the data, such as disclosure risk or copyrighted content? Let us know.

This material is distributed exactly as it arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.