Skip to content Skip to navigation

Using Aggregated Relational Data To Feasibly Identify Network Structure Without Network Data

Jul 2018
Working Paper
Emily Breza, Arun G. Chandrasekhar, Tyler H. McCormick, Mengjie Pan

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 the estimation of any arbitrary node- or graph-level statistic. We characterize both theoretically and empirically for which network features the procedure works. In simulated and real-world graphs, the method performs well at matching a range of network characteristics. We replicate the results of two field experiments that used network data, and draw similar conclusions with ARD alone.

Publication Keywords: 
Social Networks
Bayesian methods
Partially observed networks
Geographic Regions: 
South Asia