Social network data is often prohibitively expensive to collect, limiting empirical network research. Typical economic network mapping requires (1) enumerating a census, (2) eliciting the names of all network links for each individual, (3) matching the list of social connections to the census, and (4) repeating (1)-(3) across many networks. In settings requiring field surveys, steps (2)-(3) can be very expensive. In other network populations such as financial intermediaries or high-risk groups, proprietary data and privacy concerns may render (2)-(3) impossible. Both restrict the accessibility of high-quality networks research to investigators with considerable resources. 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 social connections have trait k?” Our method uses ARD to recover the parameters of a general network formation model, which in turn, permits the estimation of any arbitrary node- or graph-level statistic. The method works well in simulations and in matching a range of network characteristics in real-world graphs from 75 Indian villages. Moreover, we replicate the results of two field experiments that involved collecting network data. We show that the researchers would have drawn similar conclusions using ARD alone. Finally, using calculations from J-PAL fieldwork, we show that in rural India, for example, ARD surveys are 80% cheaper than full network surveys.

中文翻译：

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使用聚合的关系数据在没有网络数据的情况下可行地识别网络结构

社交网络数据的收集成本通常非常昂贵，限制了实证网络研究。典型的经济网络映射需要 (1) 枚举人口普查，(2) 得出每个人的所有网络链接的名称，(3) 将社会联系列表与人口普查相匹配，以及 (4) 重复 (1)-(3) ）跨越许多网络。在需要现场调查的环境中，步骤（2）-（3）可能非常昂贵。在其他网络人群中，例如金融中介机构或高风险群体，专有数据和隐私问题可能会使 (2)-(3) 变得不可能。两者都限制了拥有大量资源的研究人员获得高质量网络研究的机会。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且可行的策略——回答“你的社交关系中有多少人具有特征 k？”这样的问题。我们的方法使用 ARD 来恢复一般网络形成模型的参数，这反过来又允许估计任何任意节点或图形级别的统计数据。该方法在模拟和匹配 75 个印度村庄的现实世界图表中的一系列网络特征方面效果良好。此外，我们复制了两个涉及收集网络数据的现场实验的结果。我们表明，仅使用 ARD，研究人员就会得出类似的结论。最后，通过 J-PAL 实地调查的计算，我们发现，例如在印度农村地区，ARD 调查比全面网络调查便宜 80%。