Social interactions determine many economic behaviors, but information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social effect parameters are all globally identified. While this result is relevant across different estimation strategies, we then describe how high-dimensional estimation techniques can be used to estimate the interactions model based on the Adaptive Elastic Net GMM method. We employ the method to study tax competition across US states. We find the identified social interactions matrix implies tax competition differs markedly from the common assumption of competition between geographically neighboring states, providing further insights for the long-standing debate on the relative roles of factor mobility and yardstick competition in driving tax setting behavior across states. Most broadly, our identification and application show the analysis of social interactions can be extended to economic realms where no network data exists.

翻译：社会互动决定了许多经济行为，但大多数公开可获取且广泛使用的数据集中并不包含社会关联信息。我们提出了从观测性面板数据中识别社交网络的结果，这些数据不包含主体间的社会关联信息。在经典社会互动模型的框架下，我们给出了充分条件，使得社会互动矩阵、内生性和外生性社会效应参数均可被全局识别。尽管该结果适用于多种估计策略，我们进一步描述了如何利用高维估计技术，基于自适应弹性网络广义矩方法（Adaptive Elastic Net GMM）来估计该互动模型。我们将该方法应用于美国各州间的税收竞争研究。我们发现，识别出的社会互动矩阵表明，税收竞争与常见的“地理相邻州之间竞争”假设存在显著差异，这为关于要素流动性与标杆竞争在驱动州级税收政策制定中相对作用的长期争议提供了新的见解。更广泛而言，我们的识别方法与应用表明，社会互动分析可拓展至不存在网络数据的经济领域。