My da|ra Login

Detailed view

metadata language: English

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

Version
V0
Resource Type
Dataset
Creator
  • BREZA, EMILY (Harvard University)
  • CHANDRASEKHAR, ARUN G. (Stanford University)
  • MCCORMICK, TYLER H. (University of Washington-Seattle)
  • PAN, MENGJIE (University of Washington-Seattle)
Publication Date
2020-07-28
Free Keywords
social networks; bayesian methods; partially observed networks
Description
  • Abstract

    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.
Availability
Download
This study is freely available to the general public via web download.
Relations
  • Has version
    DOI: 10.3886/E110841V1
Publications
  • Breza, Emily, Arun G. Chandrasekhar, Tyler H. McCormick, and Mengjie Pan. “Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data.” American Economic Review 110, no. 8 (August 2020): 2454–84. https://doi.org/10.1257/aer.20170861.
    • ID: 10.1257/aer.20170861 (DOI)

Update Metadata: 2020-07-29 | Issue Number: 1 | Registration Date: 2020-07-29

BREZA, EMILY; CHANDRASEKHAR, ARUN G.; MCCORMICK, TYLER H.; PAN, MENGJIE (2020): Data and Code for: Using aggregate relational data to feasibly identify network structure without network data. Version: V0. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E110841