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

• 成果类型: Article
• 署名作者: Breza, Emily; Chandrasekhar, Arun G.; McCormick, Tyler H.; Pan, Mengjie
• 署名单位: Harvard University; Stanford University; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20170861
• 发表日期: 2020
• 页码: 2454-2484
• 关键词: Financial networks; social networks; arrays; models; LIMITS; RISK

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.