Measuring Diffusion Over a Large Network
提出一种度量二元结果在大稀疏网络上扩散的方法，利用两期数据捕捉初始期状态转换对邻居结果的加总溢出效应，并给出识别条件和置信区间。
Abstract This article introduces a measure of the diffusion of binary outcomes over a large, sparse network, when the diffusion is observed in two time periods. The measure captures the aggregated spillover effect of the state-switches in the initial period on their neighbours’ outcomes in the second period. This article introduces a causal network that captures the causal connections among the cross-sectional units over the two periods. It shows that when the researcher’s observed network contains the causal network as a subgraph, the measure of diffusion is identified as a simple, spatio-temporal dependence measure of observed outcomes. When the observed network does not satisfy this condition, but the spillover effect is non-negative, the spatio-temporal dependence measure serves as a lower bound for diffusion. Using this, a lower confidence bound for diffusion is proposed, and its asymptotic validity is established. The Monte Carlo simulation studies demonstrate the finite sample stability of the inference across a range of network configurations. The article applies the method to data on Indian villages to measure the diffusion of microfinancing decisions over households’ social networks.