Measuring Diffusion Over a Large Network

• 成果类型: Article
• 署名作者: He, Xiaoqi; Song, Kyungchul
• 署名单位: Central University of Finance & Economics; University of British Columbia
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdad115
• 发表日期: 2024
• 页码: 3468-3503
• 关键词: Causal Inference; social-influence; models; identification; contagion; interference; INNOVATION

摘要

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.