Navigating a fragmenting global trading system: Insights for central banks

In light of recent global economic and geopolitical shocks threatening trade openness, this report aims to shed light on geoeconomic fragmentation and develops a rich set of new tools to assess its economic effects and implications for central banks. The report shows that, although global trade integration has largely withstood recent disruptions and the rise of inward-looking policies, selective decoupling between few trading partners (United States vis-à-vis China, western economies vis-à-vis Russia) and for specific products (such as advanced technologies) is occurring. Survey data show that, although European firms are reorganising supply chains critical foreign dependencies persist. A firm-level stress test reveals that sudden disruptions in the supply of critical inputs from high-risk countries would lead to significant, albeit very heterogeneous, economic losses across firms, regions and sectors. Addressing foreign dependencies with broadbased protectionism policies, however, is self-defeating. In an extreme counterfactual scenario involving prohibitive and across-the-board trade barriers between geopolitical blocs, global GDP could decline by up to 9% coupled with an increase in global inflation of 4 percentage points in the first year, with the impact persisting for at least five years. It is conceivable that trade fragmentation will unravel over the course of a number of years, with supply disruptions becoming more frequent and severe than in the past. If this process should ultimately lead to a less interconnected global economy, countries might suffer from increased volatility and price pressures, as shocks cannot be easily diversified away through trade. The report concludes that geoeconomic fragmentation significantly increases the complexity and unpredictability of the operating environment for central banks; in addition, it emphasises the need for improved analytical tools and a better understanding of supply chains and trade interdependencies using granular data.