September 22, 2026 – October 27, 2026 – Online
Dr. Robert Koopman, Professor at American University’s School of International Service; former World Trade Organization Chief Economist & Director of the Economic Research and Statistics Division
This course is a combination of Live Online and Asynchronous sessions that explores how trade data are used in policy analysis to assess exposure, dependence, and risk in global value chains. Students examine how measurement choices shape policy narratives, moving from gross trade data to trade in value added (TiVA), ownership and multinational enterprise data, network-based risk analysis, and emerging AI-enabled methods. By repeatedly analyzing the same strategic product through different data lenses, participants learn how conclusions about vulnerability, leverage, and resilience can change. The course builds practical fluency in key trade and supply chain datasets for economic security and industrial policy analysis.
What You Will Learn :
- Explain how trade and value chain data are used in trade, national security, and industrial policy contexts
- Distinguish clearly between gross trade exposure and value-added exposure
- Interpret TiVA statistics and articulate their key assumptions and limitations
- Assess how ownership, multinational enterprises, and firm heterogeneity might affect policy-relevant exposure
- Identify concentration risks and chokepoints using network-based logic
- Evaluate emerging AI-enabled and firm-level data tools as complements to TiVA
- Match analytical tools to specific policy questions rather than treating any single dataset as definitive
Session 1: Trade Policy Framing and Gross Trade Data
This opening session shows how trade data is used in real policy contexts. You’ll examine how gross trade balances and bilateral dependence shape policy narratives and influence decisions on tariffs, investigations, sanctions, and export controls, while assessing what gross trade data reveals and what it can obscure.
Session 2: Introduction to Trade in Value Added (TiVA)
In today’s global economy, products often move through complex value chains across multiple countries. This session explains why gross export measures can misrepresent trade patterns and how Trade in Value Added, or TiVA, better shows where value is created. You’ll examine domestic and foreign value added, backward and forward linkages, the Koopman–Wang–Wei framework, and the OECD TiVA approach, while considering the trade-offs between precision and broader structural insight.
Session 3: National and Firm Dimensions of Value Chains
This session shifts from products to the firms and countries shaping global value chains. You’ll explore how ownership, multinational activity, tariffs, export controls, and industrial strategy affect where value is created. You’ll also compare U.S. BEA TiVA and OECD TiVA frameworks to understand how multinational and domestic firms shape value chains at national and firm levels.
Session 4: Identifying Risks and Chokepoints in Global Value Chains
This session examines global value chains as interconnected networks where risk spreads unevenly. You’ll explore how industry and country relationships create pathways for disruption, why averages can hide vulnerabilities, and how input-output tables reveal hidden dependencies. You’ll distinguish simple concentration from systemic risk and apply concepts such as pass-through frequency and chokepoint logic to understand how shocks cascade through global systems.
Session 5: New Frontiers in GVC Data and Risk Detection
This module explores next-generation data and methods for detecting risk in global value chains. You’ll examine the limits of TiVA and MRIO frameworks, especially for firm-level dynamics, real-time disruptions, and complex networks. The session introduces firm-level data, AI-enabled network inference, and tools such as AIPNET, with a focus on matching analytical approaches to policy and decision-making needs.