Overview
Microelectronics are essential not only for consumer technology like laptops and smartphones, but also defense systems, clean energy technology, and robotics. Manufacturing microelectronics depends on a complex network of global trade across many countries producing a wide range of commodities and products. Any disruption to these supply chains is a serious threat to global security and industry. But how can policymakers and analysts untangle complicated trade patterns to identify points of systemic vulnerability and reduce risk?
To answer this question, this paper examines global microelectronic supply chains using an algorithm that NSDPI researchers created with network analysis, machine learning, and qualitative analysis. By applying this algorithm to UN Comtrade trade data from 2017 to 2024, we produced a rigorous ranking of countries whose removal from the world’s microelectronics trading system would create the biggest shock.
Our algorithm can also show how risk exposure converges or differs across countries. We use this feature to illustrate the asymmetric risk portfolios for the United States and China. This futures analysis compares the unique vulnerabilities and partner lock-in of these nations’ microelectronics production and trade, with focus on rare earth elements and key semiconductor materials.
Our algorithmic analysis reveals microelectronics trade patterns and vulnerabilities that are difficult to uncover in traditional analyzes. Some high-level implications and key takeaways include the following.
Key Takeaways
- The U.S. is disproportionately exposed to disruption by exporters of processed inputs, especially from China, the UK, Germany, and the rest of the EU.
- While China’s supplier dependency is generally less than the U.S., China is highly reliant upon exports from Brazil, Myanmar, and Thailand.
- Brazil consistently emerges as an exporter that both the U.S. and China are highly dependent upon, appearing in consistent models across material sets.
To learn more about how we created this network algorithm and the unique findings it uncovered about microelectronics supply chains, read the full white paper.