Data and Code for: Using aggregate relational data to feasibly identify network structure without network data

Principal Investigator(s):  View help for Principal Investigator(s) EMILY BREZA, Harvard University; ARUN G. CHANDRASEKHAR, Stanford University; TYLER H. MCCORMICK, University of Washington-Seattle; MENGJIE PAN, University of Washington-Seattle

Version:  View help for Version V2

Name File Type Size Last Modified
  Data Revision v3 09/16/2020 12:18:PM
Changelog.txt text/plain 294 bytes 01/06/2022 03:48:PM

Project Citation: 

BREZA, EMILY, CHANDRASEKHAR, ARUN G., MCCORMICK, TYLER H., and PAN, MENGJIE. Data and Code for: Using aggregate relational data to feasibly identify network structure without network data. Nashville, TN: American Economic Association [publisher], 2022. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-01-06. https://doi.org/10.3886/E110841V2

Project Description

Summary:  View help for Summary Social network data is often prohibitively expensive to collect, limiting empirical network research. 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 links have trait $k$?" Our method uses ARD to recover parameters of a network formation model, which permits sampling from a distribution over node- or graph-level statistics. We replicate the results of two field experiments that used network data and draw similar conclusions with ARD alone.

Scope of Project

Subject Terms:  View help for Subject Terms social networks; bayesian methods; partially observed networks
JEL Classification:  View help for JEL Classification
      C83 Survey Methods; Sampling Methods
      D85 Network Formation and Analysis: Theory
      L14 Transactional Relationships; Contracts and Reputation; Networks


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Published Versions

V2 [2022-01-06]

V1 [2020-07-28]

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