Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data

被引:40
作者
Breza, Emily [1 ]
Chandrasekhar, Arun G. [2 ]
McCormick, Tyler H. [3 ]
Pan, Mengjie [4 ]
机构
[1] Harvard Univ, Dept Econ, Cambridge, MA 02138 USA
[2] Stanford Univ, Dept Econ, Stanford, CA 94305 USA
[3] Univ Washington, Dept Stat & Sociol, Seattle, WA 98195 USA
[4] Univ Washington, Dept Stat, Seattle, WA 98195 USA
基金
美国国家科学基金会;
关键词
FINANCIAL NETWORKS; SOCIAL NETWORKS; ARRAYS; MODELS; LIMITS; RISK;
D O I
10.1257/aer.20170861
中图分类号
F [经济];
学科分类号
02 ;
摘要
Social network data are 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.
引用
收藏
页码:2454 / 2484
页数:31
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