Social disruption occurs when a policy creates or destroys many network connections between agents. It is a costly side effect of many interventions and so a growing empirical literature recommends measuring and accounting for social disruption when evaluating the welfare impact of a policy. However, there is currently little work characterizing what can actually be learned about social disruption from data in practice. In this paper, we consider the problem of identifying social disruption in a research design that is popular in the literature. We provide two sets of identification results. First, we show that social disruption is not generally point identified, but informative bounds can be constructed using the eigenvalues of the network adjacency matrices observed by the researcher. Second, we show that point identification follows from a theoretically motivated monotonicity condition, and we derive a closed form representation. We apply our methods in two empirical illustrations and find large policy effects that otherwise might be missed by alternatives in the literature.

翻译：社会破坏是指某项政策在主体之间创造或摧毁许多网络联系时发生的情况。这是许多干预措施的昂贵副作用，因此日益增长的实证文献建议在评估政策的福利影响时衡量并考虑社会破坏。然而，目前很少有研究探讨在实践中能从数据中真正了解社会破坏的哪些方面。在本文中，我们考虑在文献中流行的研究设计中识别社会破坏的问题。我们提供两组识别结果。首先，我们证明社会破坏通常无法点识别，但可以利用研究者观测到的网络邻接矩阵的特征值构建信息性边界。其次，我们证明点识别源自一个理论驱动的单调性条件，并推导出一个封闭形式的表示。我们将我们的方法应用于两个实证示例中，发现这些政策效应在文献中的替代方法下可能会被遗漏，而我们的方法能够捕捉到这些显著的效应。