方法对比

	Sanctions Effectiveness Analysis	Trade Network Analysis
领域	International Relations	International Relations
方法族	Process / pipeline	Process / pipeline
起源年份	2007	2013
提出者	Gary Hufbauer, Jeffrey Schott, Kimberly Elliott & Barbara Oegg (HSE/HSEO dataset)	Network science applied to trade (e.g., Michael Ward, John Ahlquist & Arturas Rozenas)
类型	Coding and statistical analysis of sanctions episodes and outcomes	Network and inferential-network analysis of trade flows
开创性文献	Hufbauer, G. C., Schott, J. J., Elliott, K. A., & Oegg, B. (2007). Economic Sanctions Reconsidered (3rd ed.). Washington, DC: Peterson Institute for International Economics.	Ward, M. D., Ahlquist, J. S., & Rozenas, A. (2013). Gravity's rainbow: A dynamic latent space model for the world trade network. Network Science, 1(1), 95–118.
别名	Economic Sanctions Analysis, Sanctions Success Analysis, Sanctions Outcome Analysis, Economic Statecraft Effectiveness	International Trade Network Analysis, World Trade Web Analysis, Trade Network Topology, Global Trade Graph Analysis
相关	3	3
摘要	Sanctions effectiveness analysis is the systematic study of when economic sanctions achieve their political goals. Anchored by the Hufbauer, Schott, Elliott, and Oegg dataset of sanctions episodes (Economic Sanctions Reconsidered, 3rd ed., 2007), it codes each case for its objectives, instruments, costs, and outcome, then analyzes which conditions — multilateral support, modest goals, target vulnerability — predict success. Because states choose when to impose sanctions and targets choose how to respond, the field is centrally concerned with the selection problems that complicate any simple verdict on whether sanctions 'work.'	Trade network analysis studies international trade as a weighted, directed graph in which states are nodes and trade flows are edges, then characterizes its structure and models how ties form. It moves beyond the standard dyadic gravity model by treating trade relationships as interdependent — a state's trade with one partner depends on the wider web of trade — and uses network science and inferential models such as latent space models (Ward, Ahlquist, and Rozenas 2013) to capture this dependence, identify hubs and blocs, and explain the architecture of the world trade system.