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

IF 10.5 1区 经济学 Q1 ECONOMICS
Emily Breza, Arun G Chandrasekhar, Tyler H McCormick, Mengjie Pan
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引用次数: 95

Abstract

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.
在没有网络数据的情况下,利用聚合关系数据可行地识别网络结构。
社交网络数据的收集成本往往高得令人望而却步,限制了实证网络研究。我们提出了一种使用聚合关系数据(ARD)进行网络启发的廉价可行的策略:对“你的链接中有多少具有特征k?”形式的问题的回答。我们的方法使用ARD来恢复网络形成模型的参数,该模型允许从节点或图级统计的分布中进行采样。我们复制了使用网络数据的两个现场实验的结果,并单独使用ARD得出了类似的结论。
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来源期刊
CiteScore
18.60
自引率
2.80%
发文量
122
期刊介绍: The American Economic Review (AER) stands as a prestigious general-interest economics journal. Founded in 1911, it holds the distinction of being one of the nation's oldest and most esteemed scholarly journals in economics. With a commitment to academic excellence, the AER releases 12 issues annually, featuring articles that span a wide spectrum of economic topics.
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