Random Assignment with Nonrandom Peers: A Structural Approach to Counterfactual Treatment Assessment

• 成果类型: Article
• 署名作者: Griffith, Alan
• 署名单位: University of Washington; University of Washington Seattle
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01197
• 发表日期: 2024-05
• 页码: 859-871
• 关键词: Network formation; MODEL; identification

摘要

Efforts to leverage peer effects by changing assignment have often fallen short due to endogenous peer choice. To address this, I build a two-part model: agents form networks via continuous linking decisions; conditional on realized networks, outcomes are determined. I provide results on identification of both parts of the model. Using data from a randomized study in India, I estimate the model, assess its performance in out-of-sample prediction, and simulate outcomes under preferential assignment rules. This paper contributes new methodology for identifying effects of alternative assignments in the presence of network endogeneity, as well as identification of network formation models.