Mapping Global Value Chains at the Product Level

ArXiv ID: 2308.02491 “View on arXiv”

Authors: Unknown

Abstract

Value chain data is crucial to navigate economic disruptions, such as those caused by the COVID-19 pandemic and the war in Ukraine. Yet, despite its importance, publicly available value chain datasets, such as the World Input-Output Database'', Inter-Country Input-Output Tables’’, EXIOBASE'' or the EORA’’, lack detailed information about products (e.g. Radio Receivers, Telephones, Electrical Capacitors, LCDs, etc.) and rely instead on more aggregate industrial sectors (e.g. Electrical Equipment, Telecommunications). Here, we introduce a method based on machine learning and trade theory to infer product-level value chain relationships from fine-grained international trade data. We apply our method to data summarizing the exports and imports of 300+ world regions (e.g. states in the U.S., prefectures in Japan, etc.) and 1200+ products to infer value chain information implicit in their trade patterns. Furthermore, we use proportional allocation to assign the trade flow between regions and countries. This work provides an approximate method to map value chain data at the product level with a relevant trade flow, that should be of interest to people working in logistics, trade, and sustainable development.

Keywords: Value Chain Analysis, Machine Learning, Trade Theory, Input-Output Tables, Supply Chain Economics, Macro / Trade

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper uses an optimization model and trade theory concepts, but the math is not excessively dense or advanced, focusing on a practical method. The empirical component is strong, leveraging fine-grained international trade data for over 300 regions and 1200+ products, with clear data cleaning and validation steps against OECD data, making it highly applicable for real-world analysis.

  flowchart TD
    A["Research Goal:<br>Map Global Value Chains<br>at the Product Level"] --> B{"Methodology"}
    B --> C["Input Data:<br>Trade Data<br>300+ Regions & 1200+ Products"]
    C --> D["ML & Trade Theory:<br>Infer Product-Level Relationships"]
    D --> E["Proportional Allocation:<br>Assign Trade Flows<br>Regions & Countries"]
    E --> F["Key Outcomes:<br>Granular Value Chain Maps<br>Actionable Logistics Insights"]