Unveiling the hidden costs of critical dependencies

Prepared by Maria-Grazia Attinasi, Lukas Boeckelmann, Rinalds Gerinovics and Baptiste Meunier

1 Recent trends in global trade

Trade integration, a key driver of economic growth in recent decades, is at a crossroads amid growing geopolitical rivalries and protectionist policies. By liberalising cross-border flows of goods, services, investment and technology, the multilateral rules-based trading system boosted growth and reduced global inequalities (Frankel and Romer, 1999; Feyrer, 2019). A corollary of this deeper globalisation has been the increasing sensitivity of domestic output to external shocks (Chart 1), particularly in the 1990s and the 2000s – a period marked by the proliferation of multilateral trade agreements (e.g. the North American Free Trade Agreement in 1992) and the establishment of the World Trade Organization in 1995, which China joined in 2001. While greater trade openness has enabled countries to cushion domestic shocks through greater reliance on foreign demand (International Relations Committee, 2023), it has also heightened their exposure to global economic disturbances. Recent global shocks – such as the COVID-19 pandemic and Russia’s unjustified war against Ukraine – have brought this duality into sharp focus. As a result, trade integration is viewed increasingly through the lens of vulnerability and strategic risk, prompting a shift towards more inward-looking economic policies by many governments. This shift, exemplified by events such as Brexit (2016), the US-China trade tensions (2018-19) and the recent US tariff hikes, represents a paradigm change in global trade relations, characterised by growing scepticism towards trade liberalisation and increasing strain on the open global trading system.

Chart 1

Impact of a 1% shock to foreign total factor productivity on output growth in the first year

(percentage points)

Governments are reorienting their trade strategies, shifting the focus from liberalisation and efficiency towards security and resilience. There is a growing focus on reducing reliance on foreign suppliers, especially for critical raw materials, strategic technologies and other critical goods – areas where countries with a dominant position in terms of their supply may deliberately restrict access to harm dependent countries. Recent events have underscored this shift. The Organisation for Economic Co-operation and Development (OECD, 2025) highlights a fivefold rise in the number of vital inputs (e.g. lithium, cobalt) under export restrictions since 2009, a trend which reflects the growing demand for these inputs due to the digital and green transitions (e.g. an electric vehicle requires six times the amount of mineral inputs needed for a conventional car). Examples of such restrictions include the controls on US exports of semiconductors to China, which were launched in 2022, and the restrictions on exports of critical metals introduced by China in response.^[1] In 2025 China implemented export restrictions on seven rare earth minerals, while the US Administration started to investigate risks to national security posed by reliance on imported critical minerals. In addition, the EU has introduced measures to address critical dependencies (see Box 1).

Recent data point to a reconfiguration of trade along geopolitical fault lines for some products of strategic relevance, though the situation remains fluid. Aggregate indicators of trade integration, such as participation in global value chains, suggest that integration has plateaued rather than declined (Chart 2, panel a). At the same time, as shown in Attinasi et al. (2024), aggregate trends mask some reconfiguration of trade along geopolitical fault lines, particularly for goods of strategic importance from a national security standpoint, such as advanced technology products (e.g. integrated circuits, biotechnology devices). This reconfiguration, as evidenced by a sharp drop in US imports from China and a steep decline in EU imports from Russia, has accelerated since 2021, notably as a result of western sanctions on Russia (Conteduca et al., 2024; Airaudo et al., 2025). Moreover, the recent imposition by the United States of higher tariffs on its main trading partners might reshape global trade flows more significantly.

Chart 2

Trade integration and firms’ de-risking strategies

a) Trade along global value chains

(percentage of total trade)

b) Actions to reduce exposure to China

(percentage of firms surveyed relying on critical Chinese inputs)

Surveys of manufacturing firms and supply chain analysis reveal a trend towards de-risking and supplier diversification strategies, notably among top US technological firms. Multinational companies report plans to relocate production owing to geopolitical tensions (HSBC, 2024), including in the euro area, where firms express a desire to implement more de-risking strategies in the future (Attinasi et al., 2023). This is particularly the case for firms relying on geopolitical rivals for key inputs. According to surveys conducted in 2023, in selected euro area countries, around half of the manufacturing companies sourcing inputs deemed critical from China had already implemented strategies to reduce supply chain risks, or were planning to do so by the end of 2024 (Chart 2, panel b). Beyond surveys, an analysis of supply chain data points to key US technological firms adjusting their supplier networks to reduce reliance on China. Both Apple and Tesla have significantly decreased their numbers of China-based suppliers since 2022 (Chart 3). While Chinese suppliers were replaced to some extent by firms in South-East Asia, the decline in the total number of suppliers would suggest that both firms consolidated their input providers into fewer but more reliable suppliers.

Chart 3

Evolution of suppliers by origin

(number of direct suppliers)

This article sheds light on the evolution and economic relevance of critical dependencies across the United States, the euro area and China. Critical dependencies are defined as goods with limited import diversification, meaning that any disruption to their supply could have a severe impact on strategic sectors. We complement this importer perspective with a network analysis focused on exports in order to assess supply concentration and potential disruption risks if a few dominant players restrict access to critical dependencies. Lastly, we conduct a model-based analysis to examine the extent to which sudden supply disruptions to critical dependencies could entail significant economic costs despite their small share in total trade.

2 Evolution of critical dependencies

Critical dependencies are identified as strategically important inputs for which there is heavy reliance on a small number of foreign suppliers. This article identifies critical dependencies following the framework of Arjona et al. (2023) for the EU, which we adapt to cover the United States, China and the euro area (see Box 2).^[7] The methodology relies on product-level trade data to single out critical dependencies from 5,000 commodities by identifying products with a low import diversification, a scarcity of global supply and a limited presence of domestic capacity. Products that have those three characteristics and feature in a pre-defined list of strategic sectors (European Commission, 2021), such as health products, batteries, hydrogen and electronic chips, are pinpointed as “critical dependencies”.

Over the past 30 years, the number of critical dependencies has declined slightly for the euro area and China, and remained comparatively high for the United States. In the 2020s the number of critical dependencies was around 100 for the euro area and China, and 120 for the United States (Chart 4, panel a), representing, on average, 7% of total imports.^[10] Since the 1990s the composition of critical dependencies has varied across economies, reflecting differences in industrial policies and the positioning along global value chains. For the United States, the overall number of critical dependencies has remained largely unchanged, as reduced dependencies on intermediate inputs were offset by a steep rise in dependencies on final goods, especially consumer electronics (e.g. radios, televisions). The euro area has followed a similar pattern, albeit to a lesser extent, with fewer critical dependencies on intermediate goods and increasing dependencies on final products. In 2023 critical dependencies for the euro area included critical raw materials (e.g. uranium, manganese), pharmaceuticals (e.g. hormones, antibiotics) and household appliances (e.g. toasters, vacuum cleaners). By contrast, China saw a steady decline in critical dependencies between the 1990s and the 2010s, mainly on account of final goods, as the country expanded its manufacturing base and made efforts to reduce reliance on foreign partners (e.g. “Made in China 2025” plan in 2015, Dual Circulation Strategy in 2020). In the 2020s there was a slight rebound, driven mainly by rising demand for minerals (e.g. copper, nickel, beryllium) used in the rapidly developing technology sector.

Chart 4

Evolution of critical dependencies

a) By end use

(number of products)

b) By geographical source

(number of products)

The limited variation in total dependencies masks a reshuffling along the geographical dimension. Breaking down dependencies by origin shows that China has reduced reliance on advanced economies, while increasing reliance on emerging economies, notably Indonesia, Thailand and Russia (Chart 4, panel b). This reflects the expansion of the Chinese manufacturing sector, which has lowered its dependency on industrial products from advanced economies (e.g. electric motors, machinery) but increased reliance on raw minerals (e.g. nickel, zinc) from emerging economies. At the same time, both the United States and the euro area saw a marked increase in critical dependencies from China in the 2000s and 2010s and a move away from other advanced economies. In the 2020s signs of a trend reversal emerged in the United States, while in the euro area there was a plateauing of critical dependencies from China. To some extent, these developments reflect a strategic reconfiguration of supply chains as the United States tried to curb its exposure to China (e.g. 2018-19 trade war, the Inflation Reduction Act of 2022 targeting inputs from non-allied countries). Given the importance of geopolitical considerations, the remainder of this article focuses on reciprocal dependencies between western economies and China.

The dependence of the United States and the euro area on China increased the most in the electronics and chemicals sectors, reflecting China’s growing role as a global manufacturing hub (Chart 5, panel a). For most of the products in these sectors, the United States was not critically dependent in the 1990s, but progressively became so as China gained a central position as an exporter of items such as graphite, plastics and consumer electronics. For the euro area, critical dependencies from China followed a broadly similar pattern, although it was more pronounced for chemicals than electronics. At the product level, the United States and the euro area share some similar critical dependencies from China, in particular consumer electronics and health products (e.g. vitamins and hormones, cooking appliances, data storage units). For China, critical dependencies from the euro area and the United States are lower, decreasing and less concentrated. This partly reflects China’s position as a manufacturing superpower, as well as the Chinese government’s efforts to gain strongholds in strategic sectors (Baldwin, 2024). For instance, China significantly reduced its dependencies on vehicles, machinery and aircraft.

Chart 5

Critical dependencies between China and western economies

a) By sector

(number of products)

b) By supply chain position

(number of products)

From a supply chain perspective, the United States and the euro area have become increasingly reliant on Chinese downstream products (Chart 5, panel b). This is most noticeable in the United States, where dependence on downstream goods has grown fivefold since the 1990s, while reliance on upstream products has remained relatively constant, meaning that the critical exposure of the United States to China is mainly in consumption goods, in particular consumer electronics. By contrast, for the euro area, dependencies on upstream and downstream products have increased in a broadly similar way. In the meantime, China has lowered its dependency on downstream products from the euro area and the United States. This signals a shift in supply chains and shows how China has become a key hub for the assembly of consumer goods shipped to advanced economies.

3 Risk of supply disruptions: a network analysis

A network analysis complements the assessment of critical dependencies by identifying exporters with the capacity to influence access to critical goods. While the analysis of critical dependencies focused on the importer perspective, the network analysis looks at the exporter perspective, capturing whether some countries hold dominant positions in the supply of critical products. In line with Arjona et al. (2023), we rely on two metrics: export concentration and network centrality (see Box 3). The two complement each other, as looking at export concentration alone does not necessarily fully capture a country’s influence within the global production network. In fact, while a country with a high share of global exports would be in an influential position, countries that act as distribution intermediaries can play an equally important role. For this reason, considering both metrics facilitates a more accurate identification of what Arjona et al. (2023) refer to as “single point of failures” (SPOFs), which are the nodes in trade networks where supply disruptions can be the most harmful. We first document risks for critical dependencies in general and then zoom in on critical raw materials.

Chart 6

Single point of failures among critical dependencies between China and western economies

(concentration and centrality indices for each product deemed a critical dependency)

For the euro area and the United States, around 30% and 40% respectively of critical dependencies from China were at risk of being SPOFs in 2023. Critical dependencies at risk of being SPOFs are those products that have both high export concentration and high network centrality (top right-hand quadrants of panels a) and b) in Chart 6). Focusing on critical dependencies from China in 2023 (panel b), SPOFs are identified in 30% and 40% of cases for the euro area and the United States (blue and yellow dots) respectively. Notably, the high-risk products include health products (e.g. antibiotics, vitamins) and consumer electronics (e.g. radios). The number of SPOFs for critical dependencies from China has grown substantially since 1995 (panel a), reflecting both the increase in critical dependencies from China and the more central role played by China in global trade. By contrast, China managed to eliminate almost all of its high-risk critical dependencies from the euro area and the United States – with the exception of some key industrial chemicals (e.g. cellulose, hexamethylenediamine).

Since the 2000s China has expanded its role as a key hub for critical raw materials. China dominates the supply chain of minerals that are essential for modern technologies. It refines around 73% of the cobalt and 40% of the lithium in the world (Vivoda, 2023) and accounts for over 95% of global rare earth production. As a result, China’s central role in the supply chain of critical minerals makes the country a key player in the security of supply.

Regarding critical raw materials, a key concern has been the supply security of dual-use minerals, such as cobalt, magnesium and lithium. Dual-use minerals are those that have both military and civilian applications. For instance, the uses of cobalt include battery technology for electric vehicles and in the defence sector – and the euro area relies on Asia for 75% of its cobalt imports. Magnesium, a critical component in the defence, aerospace and automotive sectors, is sourced mainly from China for the euro area (85% of imports) and from Israel for the United States (58% of imports). In the case of lithium, which is vital for clean energy and defence, the United States is dependent mainly on Chile (94% of imports), while the euro area is dependent on Chile and China (39% and 18% of imports respectively).^[13] The evolution of trade in these dual-use minerals provides an illustration of the relevance of network centrality, with China having secured a dominant position in each case.

• China’s network centrality for cobalt does not stem from high export concentration. The cobalt supply is highly concentrated in the Democratic Republic of the Congo (DRC), which dominates global exports (Chart 7, panel a). However, China has emerged as a key distribution intermediary despite its limited domestic extraction capacities (Chart 7, panel b), as the DRC exports mostly to China – its share in the DRC’s cobalt exports rose from below 10% in the 1990s to 75% in the 2020s.^[14] By re-exporting to a growing number of partners, China has become as central a player as the DRC and now rivals the United States, despite the latter’s dominance in the 1990s (Chart 7, panel c).^[15]
• Over time China has expanded its central position in exports of magnesium. In the 1990s China was already a key exporter of unwrought magnesium, with a 30% global share. Since then it has significantly increased its export share, to 75% in the 2020s, leading to a sharp rise in export concentration (Chart 7, panel a). China has also consolidated its central position in the network to much higher levels than those of the United States and the euro area (Chart 7, panel b). This increase in centrality is explained by both penetration of new markets and the strengthening of existing trade links (Chart 7, panel c).
• China has consolidated its dominant position in exports of lithium in more recent years. During the 1990s China was a marginal exporter of lithium, whereas in the 2020s its share in global lithium exports rose to 75%, overtaking that of western suppliers (Chart 7, panel a). Until the 2010s this diversification meant that export concentration was limited, but China’s growing dominance pushed concentration to record levels in the 2020s owing to massive production and refining expansion which, to some extent, has crowded out other producers. Starting from a low network centrality (Chart 7, panel b), China expanded its trade network and deepened ties with existing partners (Chart 7, panel c) to become the most central global player by the 2020s, displacing the United States.

Chart 7

Network analysis of selected critical raw minerals

a) Export concentration

(index)

b) Network centrality

(index)

c) Changes in China’s network centrality

(index)

4 Costs of critical dependencies: a model-based assessment

We model the economic costs of potential disruptions to the supply of critical dependencies using the multi-country, multi-sector model of Baqaee and Farhi (2024). The model simulates the effects of supply shocks and their propagation through global production networks, including to downstream consumers and suppliers, also taking into account non-linear effects of shocks across countries and sectors. To allow for the low degree of substitutability of critical inputs, the Baqaee-Farhi model is calibrated as in Attinasi et al. (2024), which notably embeds low elasticities of substitution from Boehm et al. (2023).

To model the impact of shocks related to shortages of critical dependencies, we rely on a new methodology for building granular input-output (IO) tables. Models such as Baqaee-Farhi are usually calibrated with standard IO tables, which have a high level of sectoral aggregation, making it challenging to simulate shocks to specific products such as those identified in the analysis of critical dependencies. For example, a standard IO table bundles cobalt with many non-critical commodities (e.g. marble, sandstone) in a “mining and quarrying” sector, hence making it impossible to simulate targeted shocks and subsequent propagation across sectors and countries. To overcome these limitations, Conteduca et al. (2025) propose a data-driven methodology to disaggregate IO tables and isolate relevant niche products. We apply this methodology to build an IO table tailored to the dependencies identified in Box 2.

For each country, we study a sudden halt to the supply of products for which that country is critically dependent on foreign suppliers. The simulations assume a large increase in the trade costs of imports from the China-led eastern bloc for the United States or the euro area, and vice versa.^[16] To gauge how the economic significance of these critical dependencies has evolved over time, we run this scenario twice for each country, using the dependencies and IO tables for 1995 and 2023.

Over time the costs of a sudden halt to the supply of critical dependencies has risen for the euro area and the United States but declined for China, although they remain much higher for China (Chart 8, panel a). While China remains more vulnerable to western supply disruptions to critical dependencies, its push for self-reliance (see Section 2) has reduced its vulnerability over the past 30 years. Losses in Chinese final demand from a sudden halt to the supply of western-produced critical dependencies is estimated to have decreased from 2.1% in 1995 to 1.4% in 2023. However, the opposite holds for the euro area and the United States. Since 1995 losses in final demand owing to a sudden halt to the supply of critical dependencies from the eastern bloc have risen tenfold in the euro area (from 0.04% to 0.41%), driven by a higher dependency on Chinese inputs and a more widespread use of these inputs in production. Such losses also increased significantly for the United States (from 0.08% to 0.32%), although to a lesser extent than in the euro area given the higher starting point and efforts by US Administrations to curb dependency on China. At the same time, losses in Chinese final demand from a sudden halt to the supply of critical dependencies remain well above final demand losses in the euro area and the United States.

Chart 8

Impact of a supply shock to critical dependencies

a) Final demand

(percentage deviations from steady state)

b) Shares of supply disrupted and final demand affected

(percentages)

The key role of critical dependencies as production inputs, as well as their low degree of substitutability, amplifies the effects of sudden shortages. While critical dependencies represent only a tiny fraction of the total intermediate inputs used for production (0.01% in the United States and 0.07% in China), the impact of a sudden halt to their supply on final demand is disproportionate, being around 20 times larger than the proportion of inputs that is disrupted (Chart 8, panel b).

The model-based results are likely lower-bound estimates. Results from the Baqaee-Farhi model abstract from short-term amplification mechanisms, especially if disruptions to the supply of critical inputs give rise to uncertainty or to episodes of financial turmoil. In addition, the loss of critical inputs can cause temporary production stoppages, which are not taken into account in the model. Lastly, elasticities of substitution for highly specialised inputs (e.g. rare earth minerals) might be smaller than assumed in our calibration. Irrespective of the list of critical dependencies, all these effects would exacerbate losses.

5 Conclusions

This article sheds light on the economic risks associated with critical dependencies. Since the 1990s trade liberalisation and specialisation of production across geographically dispersed networks have enabled substantial efficiency gains. However, this has resulted in asymmetric dependencies because some countries have secured dominant positions in global supply chains (e.g. China in strategic raw minerals such as lithium, magnesium and cobalt), while others have become acutely reliant on foreign inputs. These dependencies create strategic vulnerabilities, as their disruption by geopolitical rivals can entail significant economic costs.

As advanced economies have deepened their exposure to foreign inputs, the impact of trade disruptions has intensified considerably. Trade conflicts now have far greater economic repercussions than in earlier decades, as the euro area and the United States have seen rising costs associated with their dependencies on Chinese inputs.^[17] Conversely, China has reduced its dependence on foreign inputs.

In the wake of recent global shocks, governments are reassessing their approach to trade, which is increasingly subject to geopolitical influence. Focus is placed on critical inputs which, though small in value, are difficult to replace and to which supply disruptions can severely amplify inflation and dampen demand.

Despite these risks, the macroeconomic costs of a full decoupling of geopolitical blocs would likely exceed those associated with critical dependencies – while failing to eliminate them. Model simulations suggest that a comprehensive decoupling of geopolitical blocs could reduce global GDP by up to 12% in the long run and temporarily push up inflation by as much as 4 percentage points in the first year (Goes and Bekkers, 2022; Attinasi et al., 2025a; 2025b). These costs stem from output losses, rising input prices and inefficiencies due to fragmented trade flows. Moreover, protectionist measures may prove ineffective at eliminating dependencies, as trade often reroutes through neutral third countries (Attinasi et al., 2024).

Policymakers therefore face a trade-off between strengthening supply chain resilience and preserving the benefits of openness. To navigate this dilemma, rather than resorting to blanket protectionism, governments should adopt targeted and coordinated de-risking strategies. Such strategies should aim to address specific vulnerabilities while preserving the economic gains of global integration. This balanced approach is key to ensuring both resilience and long-term prosperity. To help in the tailoring of policies to relevant dependencies, the type of data-driven analysis carried out in this article provides some insight into how to unveil critical dependencies that are not visible in aggregate data.

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