Recent shocks and disruptions have sharpened awareness of the potential risks associated with reliance on international supply. This chapter explores these concerns and uses modelling to understand where vulnerabilities may lie and the possible effects of shocks. It also assesses the likely economic impacts of attempts to “re-localise” production.
OECD Supply Chain Resilience Review
Navigating Risks
Abstract
The preceding chapter described the evolution of supply chains in international commerce and how this trend has contributed to interdependence across national economies. In this context, it is worth exploring whether greater trade interdependency contributed to the negative economic consequences of recent shocks, or whether it was in fact an attenuating factor (OECD, 2022[1]; Arriola, Kowalski and van Tongeren, 2021[2]). However, current geopolitical and trade tensions, expanding interventions by national governments, intensified international competition for scarce natural resources and greater policy uncertainty have increased awareness of the potential negative implications of trade interdependencies, and the risk that over-reliance on some trading partners could result in significant economic and societal damage in the event of unexpected disruptions, or could become a tool for economic coercion, undermining national or strategic security.
Perceptions of the negative sides of trade dependencies have resulted in calls for deglobalisation, friend-shoring, near-shoring, creating trading blocs or re-localising supply chains (e.g. Arriola (2020[3]); Crowe and Rawdanowicz (2023[4]). On the other hand, there are concerns that some of these policy responses, aimed at minimising trade risks and improving supply chain resilience, may not be well designed and could unintentionally undermine the benefits of international trade and reduce resilience (discussed further in Chapter 5). There is also the risk that such measures could trigger a process of fragmentation of the international trading system.
This chapter explores potentially vulnerable trade links, referred to here as “trade dependencies” or “critical trade dependencies” (i.e. dependencies considered to be particularly important economically, or otherwise).1 One approach to minimising trade risks and improving supply chain resilience is to scan for such trade dependencies and devise policies to increase diversification. The chapter discusses approaches used to quantify such dependencies across major trading economies and a wide range of trade products, to then analyse the geographical concentration of merchandise imports and exports for specific trade partners. Additionally, the chapter describes economic modelling approaches that try to capture some of the dependencies across a wider range of sectors, including services. It concludes with a discussion of estimates of the economic costs that could be expected if policy-induced attempts to reduce trade dependencies lowered trade integration. The chapter is linked to the country by country statistical annex on international supply chain interdependencies and trends. Annex B presents a snapshot of import and export concentration for OECD countries over time.
Perceptions of geopolitical, policy and economic uncertainty have highlighted awareness of the possible negative aspects of interconnectedness, and the propagation of shocks in international supply chains. Measuring these phenomena is a complex undertaking, and results are not always straightforward to interpret. However, the past five years were marked by several large shocks (COVID-19 and subsequent transport and supply chain disruptions, Russia’s war of aggression against Ukraine, and interruptions of traffic in Suez and Panama Canals) and several recent studies and measurement initiatives have demonstrated signs of greater perceived uncertainty.
The economic policy uncertainty index, for example, which draws on analysis of words used in major newspapers, official policy sources and surveys of professional forecasters (Baker, Bloom and Davis, 2016[5]), shows that global economic policy uncertainty has increased progressively during recent episodes of economic and geopolitical shocks. This uncertainty index suggests that global policy uncertainty increased markedly in the aftermath of the 9/11 terrorist attacks in 2001, during and in the aftermath of the 2008-10 global financial crisis, during the COVID-19 pandemic and after Russia’s war of aggression against Ukraine in 2022. In the period 2020-2023, policy uncertainty was on average significantly higher than in any of the previous decades covered by this methodology (Figure 3.1, Panel A).
Similarly, the newspaper-based index of geopolitical risk (Caldara and Iacoviello, 2022[6]) shows an increase in the perception of geopolitical risk following the 9/11 terrorist attacks in 2001, and another significant increase in the aftermath of Russia’s invasion of Ukraine in February 2022 (Figure 3.1, Panel B). Considered over a longer historical perspective, the same index shows that although the perceptions of geopolitical risk attained in the early 2020s were higher than those seen in the late 1990s, they were still markedly lower than during some previous episodes of geopolitical tensions, for example the Gulf War, Korean War and, particularly, World War I and World War II (Caldara and Iacoviello, 2022[6]).
The OECD Business and Consumer Confidence Indices, which are based on surveys of attitudes towards future developments, show increases in both business and consumer uncertainty for the OECD area in the aftermath of 9/11 and the global financial crisis, at the beginning of the COVID-19 pandemic and, particularly for consumers, following Russia’s war of aggression against Ukraine (Figure 3.2).
While assessing the resilience of global supply chains, it is important to keep in mind that the period 2020-2022 posed challenges of a previously unseen magnitude. This is apparent in Figure 3.3. It displays the Federal Reserve Bank of New York’s Global Supply Chain Pressure Index (GSCPI), a summary measure of supply chain disruption. The values of the GSCPI observed at the height of the COVID-19 pandemic crisis dwarfed anything seen in the previous two decades, a period which itself had witnessed some significant supply chain shocks (e.g. the earthquake in Japan and the floods in Thailand in 2011). Despite continuing geopolitical tensions, the GSCPI has been below its long-term average since early 2023.
Identifying potential trade dependencies, and associated economic and other risks, has been high on the policy agenda since the COVID-19 pandemic. It is nonetheless difficult to identify objective analytical criteria and measurements that would allow a clear separation of those trade links that may be a source of concern from those that are advantageous. This is in part related to the fact that relevant considerations are often non-economic and reflect country-specific perceptions of national security priorities. Thus, the potential contribution of economic analysis is limited but can nevertheless help draw a more comprehensive picture of some of the economic characteristics of global and national trade linkages which have been put forward as examples of trade dependencies. It can also help assess the economic costs and benefits associated with different policy options.
The emerging literature on this topic, as summarised in Arriola et al. (2024[8]), suggests that critical trade dependencies can be defined as trade flows that combine three characteristics: high economic (or other) importance; high risk of supply disruption; and few options for supplier or product diversification or substitution (Figure 3.4). The three characteristics and their measurement are discussed below.
Source: Arriola et al. (2024[8]). “Towards demystifying trade dependencies: At what point do trade linkages become a concern?”, https://doi.org/10.1787/2a1a2bb9-en.
Identifying economically or other key imported products often first involves examining how a specific import (e.g. a mineral or a service) is used in the production processes of domestic industries. The next step is to determine whether those industries themselves are important national industries, or whether the import is essential from other perspectives (e.g. essential food products or medicines). In some cases, an additional criterion is added which considers whether the absence of the product would cause detrimental harm to the economy, society or a country’s strategic interests. Assessing whether this is the case requires consultation with industry experts. For economically important national industries, a disruption in their supply could have economy-wide repercussions. However, less economically sizeable industries can also be considered important, for example if they are concentrated geographically or are important for a country’s industrial, technological or social performance. To identify critical dependencies in exports, the analysis typically considers an exported product’s contribution to total exports. However, it can also include other strategic considerations, such as the essential nature of, or technologies embodied in, the exported product.
As supply or demand disruptions can have different sources, any analysis of supply or demand risk needs to explore political, geographical or natural risks which may concern specific products or wider ranges of products. The higher the probability of disruptions and the higher the supply risk, the higher the degree of criticality. Identifying the main trading partners in the trade linkages is one of the key issues in the risk assessment in the context of the trade dependency debate. This is because geographic, economic and geopolitical risks are often related to the country’s affiliation or the location of trading partners. However, the growing incidence of climate change-related disruptions has also triggered interest in risks which may be concentrated in specific geographical areas (e.g. coastal or flood areas, areas exposed to extreme temperatures or specific transport routes).
The consequences of supply or demand disruptions can be alleviated if alternative trading partners are available (or alternative types of products). In applied trade policy analysis, this possibility of substitution is typically captured by concentration of imports and exports across partner countries and products, as well as by elasticities of substitution.2
The trade dependency criteria in Figure 3.4 provide a useful conceptual distinction of the different characteristics that may underpin critical dependency, even if most of the measures that are used to quantify them in applied work may be either imperfect indicators of these characteristics, or may capture more than one of them. In recent analysis, the OECD has investigated different sources of data and key modelling frameworks which can be used to identify trade flows falling under this definition and to examine their characteristics and evolution (Arriola et al., 2024[8]). One of the conclusions was that, given the lack of a commonly accepted definition and approach to measuring trade dependencies, as well as the fact that various data and modelling approaches have different advantages and constraints, it is best to combine alternative methods to shed light on trade dependency.
One approach is to analyse traditional inter-country input-output (ICIO) data which were discussed in Chapter 1 in the context of mapping supply chain interlinkages. This can help to better capture indirect linkages and supply and demand dependencies while accounting for the whole supply chain, which may span across different economic sectors (e.g. Ayadi et al. (2021[9]), Baldwin and Freeman (2022[10]), Schwellnus et al. (2023[11]), Inomata and Hanaka (2021[12]), Arriola (2024[8])). Another approach is to use fully fledged economic models—typically computable general equilibrium (CGE) models—that not only incorporate information on trade concentrations and inter-sectoral linkages, but also allow modelling of behavioural responses across specific economic sectors as well as the whole economy (Grassia et al., 2022[13]); (Arriola et al., 2020[3]); (Arriola et al., 2023[14]); (Chepeliev, Hertel and van der Mensbrugghe, 2022[15]); (Rose, Chen and Wei, 2022[16]). However, such models are typically based on data aggregated by sector and can therefore mask some of the vulnerabilities associated with producing and trading specific products. They are also based on additional modelling assumptions and parameters (Section 3.7).
The third popular approach – and the one which is used in the section below – is to assess trade dependency using granular merchandise trade data. Despite some limitations – the most significant being that they only cover direct merchandise trade linkages3 – the approach enables a granular picture to be painted of linkages across a diverse list of merchandise (some 5 000 products) and a comprehensive coverage of trading partners (some 230 countries) over the last 20 years. Studies which follow this approach build on the concept of trade concentration, i.e. reliance on only a few suppliers for imports, or only a few markets for exports, of specific products (e.g. Bonneau and Naka (2020[17]), McKinsey Global Institute (2023[18]), Productivity Commission (2021[19]), European Commission (2022[20]), (Vicard and Wibaux (2023[21]), Barthou, Haraboure and Samek (2024[22]). While the various studies using the granular merchandise trade data approach may use somewhat different measures of concentration and criteria to determine dependency, the shared hypothesis is that if a country imports a given product from, or exports it to, only a few partners, this country may find it hard to find alternatives in case of foreign supply or demand disruptions.
The granular trade data approach described above has been used in recent OECD work to study the main features of trade dependencies and their evolution over time for the world and the OECD area (Arriola et al., 2024[8]; Kowalski and Bates, 2025 forthcoming[23]). In this work, trade dependency has been determined by analysing jointly several measures of dependency such as the overall global concentrations of exports and imports of products across all exporting and importing countries, concentrations of countries’ imports of specific products, and the main trading partners’ shares in these imports and exports.
To measure the overall concentration of imports and exports of products across trading partners, this work follows several recent studies which make use of the Herfindahl-Hirschman Index of concentration (HHI). Calculated as the sum of squared market shares, the HHI lies between 1/n (where all of the n suppliers have equal shares), and one (where there is only one monopolistic supplier of imports or one monopsonistic destination for exports). While there are no objective thresholds of HHI values that clearly indicate low and high concentration, some indicative thresholds have been used in applied work.4 Abovementioned OECD work uses an HHI value of 0.2 to indicate relatively high concentration.5
To paint a comprehensive and balanced picture of trade concentration and trade dependency, this approach calculates the HHI values for different types and levels of international trade in merchandise products:
When calculated across all countries which export (or import) a given product, the index captures global concentration of supply (or demand) for that product.
When calculated for a product and an individual importing (or exporting) country across all the corresponding export (or import) partners, the index captures national concentration of imports (or exports) of that product.
Combining global and national concentration can determine cases of “significant” national concentration where the national import (or export) concentrations are much higher than the corresponding global export (or import) concentrations, suggesting that imports are being sourced from (or exports are destined for) significantly fewer countries than is in principle possible.
Some bilateral product-level trade linkages can be further classified as bilateral trade dependencies where more than 10% of a country’s imports (or exports) come from (or are exported to) one partner, and the country has a high national concentration of imports (or exports) for this product.
One of the key findings of this analysis is that the global concentration of both exports and imports of products6 increased from the mid-1990s, before declining somewhat during the COVID-19 pandemic (Figure 3.5). In part, the growing global concentration of trade likely reflects finer levels of specialisation in international supply chains which proliferated during this period. In international supply chains, specialisation tends to occur at the level of specific specialised inputs and tasks rather than finished complex products. As a result, many more, and often smaller, actors are involved in the production of complex products, making relatively small contributions in the form of parts and components or semi-processed products, possibly leading to a higher incidence of more specialised and more concentrated trade flows. This trend is consistent with the perception of an increase in vulnerabilities to unexpected shocks transmitted through international trade and supply chains, and especially with concerns about the growing concentration of supply. However, it is also clear that, while global export and import concentrations have both grown, their average levels remain low, with their HHI values not exceeding 0.2.
Figure 3.5 also shows that global concentration of exports across supplying countries is higher than global concentration of imports across destination countries. This can be explained by the tendency of countries to specialise in the production of different products (or inputs and components), while consuming relatively similar baskets of products.
Overall, the data demonstrate that large, if not dominant, portions of trade are relatively well diversified. For example, even for global exports, which tend to be more concentrated that global imports (Figure 3.5) only about 30% of products record relatively high levels of concentration, while exports of all other products are relatively well diversified7 (Arriola et al., 2024[8]). This suggests that large portions of international markets are characterised by a reasonable amount of competition, and therefore that specific exporters and importers have limited control over supply or price formation.
In addition, the most concentrated export products do not tend to be those typically identified as being “strategic”.8 Instead they tend to include light manufacturing products, most notably textiles and footwear (Figure 3.6). Some animal and vegetable products are also among the highly concentrated exports, as are some inorganic chemicals and base metals. At the same time, global exports of several advanced manufacturing products (e.g. in the advanced machinery and motor vehicles industry) tend to be relatively highly diversified across exporting countries. This reflects multiple factors which are likely to drive production and trade concentration, including some which drive international specialisation and exchange, such as natural endowments, comparative advantage, low costs of production and processing, and economies of scale.
While global exports of products do not appear to be overly concentrated (Figure 3.5), countries tend to source their imports from fewer partners than are theoretically available globally, as illustrated by the higher values of the HHI index when calculated for national imports and exports (Figure 3.7). Countries are also exporting to fewer markets than are theoretically available globally,9 although the average concentration of national exports is lower than for imports and has declined slightly over the investigated period. The higher concentration of imports is consistent with the recent public debate in many countries on import dependencies, but for some countries export dependencies may also be significant.10
Top 30 most and least globally-export concentrated HS sectors, 2017-19
Note: The values show simple averages of HHI values across all HS6 products belonging to a given HS2 category (i.e. HS chapter, which is a more aggregated category of HS products). The higher the HHI value, the more concentrated the product’s exports. Note that for some specific HS6 product categories, HHI values can be much higher than the sector average.
Source: Arriola (2024[8]), “Towards demystifying ‘trade dependencies’: At what point do trade linkages become a concern?”, https://doi.org/10.1787/2a1a2bb9-en.
That country-level concentrations of trade tend to be higher than their corresponding global concentrations likely reflects a combination of natural factors – such as the role of geography – and trade costs, particularly in international supply chains, which remain concentrated regionally (i.e. relatively high shares of inputs sourced from abroad are sourced from countries in the same geographical region, such as within Europe, North America or Asia). They also reflect countries’ preferences and policies. These are exemplified by the expansion of regional and preferential trade agreements since the 1990s, which by design tend to lower trade costs and give other advantages to selected trade partners, contributing thereby in principle to trade concentration. Strategic economic policies of importers and exporters can also play a role. As discussed by Arriola (Arriola et al., 2024[8]) and further below in this chapter, the relatively high level of national import concentrations across trading partners has coincided, for example, with China’s export-led trade strategies and its rising role as a source of trade dependencies for many importers (Section 3.6).
Data sometimes shows large differences between the global and national trade concentrations of certain products. To understand this, it is necessary to distinguish between two scenarios: one in which the number of suppliers is inherently constrained because only a few countries export the product; and another, where even though many suppliers or customers exist globally, countries choose to rely on significantly fewer suppliers than what is potentially available. The latter occurs due to “economy-specific choices” (McKinsey Global Institute, 2023[18]), where factors specific to a given economy such as geography, trade agreements or consumer preferences, lead countries to trade with fewer partners than theoretically possible and result in national concentrations being higher than global concentrations.
Building on this observation, Arriola (2024[8]) and Kowalski and Bates (2025 forthcoming[23]) devised an approach that combines calculations of global and national concentrations to evaluate the incidence of such significantly concentrated trade. In this regard, “significantly concentrated” national imports (or exports) are defined as imports (or exports) of those products that are imported (or exported) by a given country from (to) at least twice as concentrated a range of suppliers (or markets) than is globally possible. Importantly, the determination of “significant concentration” of imports (or exports) was calculated for only products which are already highly concentrated at the global level, i.e. when the HHI value for global exports (or imports) of the given product is equal to or higher than 0.2.
Presented as the number of HS6 products for which national imports or exports are significantly concentrated (Figure 3.8, Panels A and B), this measure is comparable across countries and has the appealing feature of taking into account what is in principle possible in terms of diversification in international markets.11 This measure is therefore used in this chapter to illustrate the relative levels and trends of trade dependency across all countries. It is also used in Annex B to illustrate the evolution of the number of products with significant import and export concentration for each OECD Member.
Overall, the incidence of significant concentration of national imports and exports has increased on average, but considerably more for imports than exports. Figure 3.8 shows the average number of products with such significant import concentration for all countries in the sample, as well as OECD Member countries, the major other economies (MOEs) grouping12 and all other countries (i.e. countries which are not members of either the OECD or MOE groupings). Across all countries, on average, there were 50% more significantly import-concentrated products in the early 2020s than there were in the late 1990s (Figure 3.8, Panel A), whereas the level of significant concentration of exports has changed little since the beginning of the investigated period (Figure 3.8, Panel B). At the beginning of the 2020s there were six times as many significantly import-concentrated products per country as there were significantly export-concentrated products. However, while the incidence of significant import concentration has increased across all countries on average since the mid-1990s, the average for OECD Member countries has remained relatively stable—and periodically even fallen.
The lowest income countries tend to have the highest incidence of significant import concentrations globally. Many of the lower income countries belong to the category “Others” in Figure 3.8. Countries in this grouping tend to have on average a considerably higher number of significantly concentrated imports than OECD or MOEs; many of them have much lower per capita incomes than the OECD and MOEs. Several of them belong to the least developed country (LDC) grouping, are landlocked and have recently suffered, or are currently suffering, from a military conflict (Arriola et al., 2024[8]).
While on average across OECD Member countries the incidence of significant import dependency has been relatively stable, there is more variation for individual OECD Members. This is also shown in more detail in Figure 3.9, Panel A, for the G7 countries. Throughout the investigated period, Japan and Canada– and to a lesser extent the United States– tended to have higher levels of significant import concentrations than the European G7 members. Interestingly, all G7 countries have recorded gradual reductions in significant import concentrations since 2007-09.
In contrast, there has been a remarkable increase in the incidence of significant import concentration in the MOE grouping (Figure 3.9, Panel B). This is accounted for mainly by India, Brazil and Indonesia, whose significant import concentration scores approximately doubled between the mid-1990s and the early 2020s. In South Africa, Russia and China the incidence of significant import concentration increased by less than 50%, and China in particular has seen the smallest increase of the group. Similar to the G7 countries, China has been gradually reducing its rate of significant import dependency since 2007-09.
Within the entire OECD Membership, some countries have more concentrated imports and exports than others (Figure 3.10). They also differ in how this measure of trade dependency has evolved over time, as illustrated for each individual OECD Member in the accompanying country by country data annex. Germany, the United Kingdom, France and Italy tend to record the lowest scores for significant import and export dependency across the OECD Membership, while there is no clear pattern when it comes to countries with the highest dependency scores. It can nevertheless be observed that smaller economies tend to have relatively high significant export dependency scores – see also Kowalski and Bates (2025 forthcoming[23]).
Geographic, economic and geopolitical risks are often related to the geopolitical affiliation or geographical location of countries’ main trading partners. Identifying which trading partners countries rely on most from among their highly concentrated trade linkages can be key to understanding the risks associated with trade dependency.
Several measures of trade concentration described in Section 3.4 indicate that, in the investigated period, one of the most prominent developments has been the rise of China as an ever more prevalent counterpart in highly concentrated trade linkages across the globe. This can be illustrated through China’s growing contribution to measures of global average national import and export concentration, accompanied by falling shares of the other major trading partners such as the United States, Germany, and Japan(Figure 3.11). In numbers, China’s contribution to countries’ significant import concentration has increased from 5% to 30% over the past 25 years while the combined contribution of the US, Germany and Japan decreased from 30% to 15% (20% to 11% for the US, 7% to 3% for Germany and 3% to 1% for Japan) (Figure 3.11 Panel A).
The extent of trade dependency on China can be further illustrated by breaking down the measures of significant import and export concentration of products by the largest trading partner. Figure 3.12 shows the average number of times a trading partner holds the highest share of imports (Panel A) or exports (Panel B) of significantly concentrated imports and exports for all countries. In the early 2020s, China was the main trading partner in 30% of cases of significantly concentrated imports (up from 5% in the late 1990s, Figure 3.12, Panel A) and for 6% of significantly concentrated exports (up from 2%, Figure 3.12, Panel B), although the average number of products for which exports were significantly concentrated was much lower than for imports.13
Reliance on China is more pronounced for the MOE grouping than for OECD Member countries (Figure 3.13 and Figure 3.14). In the early 2020s, China was the main trading partner in 60% of the MOEs’ excessively concentrated imports (up from 9% in the late 1990s; Panel B, Figure 3.13), compared with 22% of cases for OECD economies (up from 5% in the late 1990s; Panel A, Figure 3.13). In contrast, for exports China has only become a slightly more important counterpart in significant export concentration for both country groupings (Figure 3.14).14
The extent and increase in reliance on China portrayed in Figure 3.12, particularly for imports, is backed up by another recent study (Arriola et al., 2024[8]) which used an alternative approach to measuring the contribution to trade dependencies of specific bilateral trading partners.15 It found that bilateral trade dependency on China has increased considerably for several OECD Member countries and regions since the mid-1990s, and that it is now the single most important country featuring in trade dependencies for the OECD as a whole, and for several individual OECD Member countries.
At the same time, OECD economies’ trade dependencies on China can also be viewed in the context of China’s dependencies on OECD countries. The OECD as a group – and especially several individual OECD Member countries – are much more relevant counterparts in significantly concentrated import and export products for China than China is for the OECD (Figure 3.15). Throughout the investigated period, OECD Member countries were the main trading partners involved in China’s significantly concentrated imports. Japan and the United States are the two countries that account for the highest shares of China’s significantly concentrated imports throughout the period, although the shares of these two countries in China’s dependencies have been gradually declining (Figure 3.15). The European Union as a group has also become progressively more important for China. Just before the COVID-19 pandemic the European Union was the main partner in 24% of cases of China’s significantly concentrated imports, up from 10% in the late 1990s. China’s sectoral dependencies involving OECD Member countries include industries in which several OECD Member countries also depend on China, particularly advanced manufacturing (e.g. manufacture of motor vehicles, manufacture of pharmaceuticals, manufacture of bearings, gears, gearing and driving elements, manufacture of lifting and handling equipment).
The high shares of the OECD in China’s import dependencies, and vice versa, underscore the mutual character of some of these trade dependencies. For the MOE grouping, the growth in import dependency appears more one-sided, as China’s import dependencies on other members of this country grouping have increased only slightly, while these countries have become considerably more dependent on China.
Given the diverse nature of shocks that have the potential to disrupt supply chains, policymakers need to understand how different events are likely to impact domestic and international stakeholders. Anticipating risk by understanding the nature of disruptions is one of the OECD’s keys to resilient supply chains (Figure 3.16). While the analysis of trade dependency based on gross trade flows concerning individual products, as presented above, helps to unravel the main relevant trends over recent decades, it does not give the full picture. This is because the exposure of national economies to potential shocks, as well as their ability to adjust to these shocks, depends on these economies’ specialisation and integration into international supply chains. These characteristics are not easy to gauge without a fully-fledged model of the global economy which accounts for intra and inter-industry linkages, while also adequately capturing economy-wide adjustments. This section describes OECD analysis which employed data and modelling tools to track possible effects across a full supply chain to better inform government and business efforts to enhance supply chain resilience.
OECD policy toolkit to increase supply chain resilience
Source: OECD (2024[25]), Keys to Resilient Supply Chains. OECD Policy Toolkit: Increasing supply chain resilience, Resilient-Supply-Chains_Brochure-2024.pdf.
How shocks are transmitted along the whole supply chain, and the main market adjustments, were studied using the OECD’s global trade computable general equilibrium (CGE) model METRO (Arriola, Kowalski and van Tongeren, 2024[7]).16 Incorporating ICIO relationships, the model can capture the exposure to shocks of concentrated trade linkages that are due to direct import-export relationships, as well as those that may result from indirect trade links (e.g. when a product exported from one country to another embeds a component produced in a third country). Importantly, the approach also allows for the analysis of direct and indirect dependencies in the services sectors, unlike the analysis of merchandise trade data described in Sections 3.4 and 3.5 above. The model also accounts for conventional adjustment mechanisms such as price adjustments, substitution between different inputs, the re-balancing of intermediate and final product markets, as well markets for different kinds of labour and capital, and therefore gives a relatively comprehensive picture of possible economic impacts.
Focusing on national economic sectors’ output responses to production shocks occurring in other domestic and foreign sectors connected vertically through supply chains and horizontally through competition in product markets, the analysis considered which countries and broad sectors may be particularly vulnerable to shocks or could be a more significant source of risk for others. While the analysis relies on several assumptions which require care when drawing out policy implications (as for any other exercises using this type of methodology), a few broad findings and policy consequences are of interest and are summarised below.
The analysis shows that the current structure of domestic and international linkages, and economic adjustment mechanisms that operate through them, tend to dampen the impacts of shocks rather than amplify them. This was revealed by the finding that, in most cases, production disruptions somewhere in the global economy cause relatively small output responses elsewhere. That said, there were also some large outliers, indicating that shocks in some segments of the global economy may have more consequential effects than others.
The impacts of shocks occurring in other domestic sectors tend to be larger than impacts of shocks occurring in foreign sectors. This is because in most sectors the reliance on foreign inputs and foreign markets for final products is still less than reliance on domestic inputs and product and factor markets17. In addition, international markets offer broader adjustment and diversification options than domestic ones.
Production disruptions originating in foreign vertically-linked sectors – the kind of shocks that are at the centre of the debate on risks in international supply chains – do not appear to be the main source of disruptions. While the results confirm that disruptions in upstream sectors of the value chain can constrain access to intermediate inputs, and output declines downstream can lower demand for inputs, most such vertical impacts are two orders of magnitude smaller than the original shocks. The dispersion of impacts is also smaller than for domestic shocks. Again, this reflects the current levels of diversification and beneficial opportunities for adjustment in international supply chains.
The results also suggest that a wide variety of domestic and international economic adjustment mechanisms play an important role in shaping responses to shocks. These include price signals leading to substitution towards other suppliers or other market outlets, and responses by labour and capital markets. Such mechanisms should therefore be included in assessments of trade dependency and resilience to shocks.
Impacts of shocks across national economies tend to be smaller when factors of production cannot move across sectors (i.e. in the short term) than when they can move freely (medium to long term). This underscores, among other things, that short-lived disruptions may matter less than disruptions that last longer and which allow more time for factor markets to react and pass on the impacts to other sectors. It also suggests that policies protecting employment from temporary shocks or restricting short-term capital movements may help to attenuate the impact of such shocks.18
While most of the impacts of shocks propagated through supply chains are much smaller than the initial shocks, in a small portion of cases responses can be more than three times larger than the original shocks. In addition, an accumulation of multiple adverse shocks can have more significant implications. Statistics summarising responses to such highly adverse constellations of shocks presented in this work suggest that some sectors and countries may be exposed more than others. For example, economies with strong vertical links to major foreign economies tend to be more exposed to international supply chain shocks, with Canada, France, Germany, and the United Kingdom being the most exposed across all economies considered in this modelling study. The United States, Brazil, and China are less exposed due to their greater reliance on domestic product and factor markets in most sectors (Figure 3.17).19
There are also important differences across countries and sectors in terms of which shocks contribute the most to exposure. For example, Germany’s motor vehicles sector, which is found in this analysis to be only moderately exposed to international supply chain shocks compared to other sectors, tends to be more exposed than the motor vehicles sector of the United States, and a greater share of this exposure can be attributed to shocks originating in China (Arriola, Kowalski and van Tongeren, 2024[7]).
However, shocks that occur in other sectors of the national economy (i.e. domestic shocks) tend to dominate the exposure metrics, suggesting the most important sources of risks are within domestic economies. This is illustrated using the example of South Africa in Figure 3.18, which shows that most of the country’s sectors (right scale) are exposed to shocks originating in other sectors of the domestic economy (left scale). However, China tends to be the country accounting for the greatest share of exposure that can be attributed to shocks originating abroad.
The two examples – of motor vehicles in Germany and South Africa’s entire economy – both reinforce the findings from the analysis of granular data in preceding sections: China is the second-most important source of shocks for most economies (after their own domestic economies).
Modelling results also show that there is more variation in the measures of exposure across sectors than there is across countries, suggesting potential for sectoral initiatives to address exposure to shocks. In this context, it is important to note that manufacturing sectors are found to be on average much more exposed to foreign output shocks than services sectors or agriculture and food. This is because manufacturing sectors are more internationalised in terms of output destinations, as well as in sourcing of intermediate inputs. For this reason, manufacturing of electronics, metals, iron and steel, machinery and equipment, and chemicals appear to be the most exposed (Figure 3.19).20
Shocks originating in services sectors, which in some countries account for a large share of employment (e.g. hospitality and recreation, retail trade, construction, and warehousing and support activities), can have relatively big impacts across the global economy. However, the simulations reveal that the impacts of shocks originating in large services sectors tend to not be transmitted through constrained access to, or demand for, intermediate inputs (i.e. via vertical links in international supply chains), but rather through domestic economy-wide impacts involving factor markets. In the medium to long run, an output reduction in those sectors tends to be associated with a release of labour and capital that finds employment in other parts of the economy, which impacts other sectors. For example, shocks to business services – a sector which has strong upstream and downstream linkages to manufacturing sectors – are characterised by a more classical transmission of vertical foreign shocks through international supply chains (i.e. between vertically linked upstream and downstream sectors located in different countries).
Aggregations may mask some impacts on critical products. The modelling that underlies this analysis does a satisfactory job of mapping broad shock exposure and transmission channels across the global economy. However, it is constrained by modelling assumptions, which are often linked to the availability of the data, and which are associated with important caveats for this analysis. One of the issues is the aggregation of sectors used in the analysis,21 which may mask impacts linked to some more specific strategic categories of products which are at the centre of debates on the propagation of shocks and supply chain resilience (e.g. semiconductors, minerals used in environmentally sustainable technologies, or specific food and medical product categories). This may be particularly problematic if, in reality, there is a lot of heterogeneity within the broader sectors. For example, various raw materials which may be critical inputs into production in a variety of industrial sectors are aggregated together, which may result in an overly optimistic assessment of substitution possibilities.22 Nevertheless, if signs of exposure to shocks are detected in the analysis conducted at the broad sector level, it suggests that at least some of the more specific activities or products that are covered by this broad category are also likely to be exposed. The analysis, and its implications, should therefore not be seen as exhaustive, but as a first filter for identifying those broad economic sectors which could be studied further in detail using methodologies that would allow for more product detail.
Over-simplification of complex relationships is another area to be aware of. In general, this type of modelling, which was historically used to quantify important aspects of agricultural, extractive and manufacturing sectors, makes simplifying assumptions about incredibly complex and potentially consequential linkages that underlie today’s global economy. One example is the financial and payment system. In the analysis presented above, financial services are either not accounted for at all (this is the case in the analysis of the granular merchandise trade data) or only accounted for to the extent that their use by consumers is an input into production in other sectors (in the ICIO and CGE approaches) that do not include the money and currency markets or the payment system. The associated dependencies (e.g. on specific currencies or payment system links) that may be equally—or more—important than conventional product trade dependencies are therefore not taken into account. Yet the emerging literature suggests, for example, that reliance on a concentrated provision of financial services may be an important source of dependencies for some countries (Maggiori, Clayton and Schreger, 2024[26]).
The analysis presented in this chapter has indicated that some trade linkages appear highly concentrated and that this may increase the probability of economic or other damage by shocks, including policy induced shocks. This confirms the merit of monitoring measures of trade concentration in order to anticipate impacts of possible shocks and disruptions. Businesses will likely have an interest in incorporating such measures into their risk management practices. Strategic business decisions may not only be determined by dependencies but also by the likelihood of shocks, including policy shocks. However, if policymakers consider the private sector mitigation measures to be sub-optimal, they may intervene (e.g. Felbermayr and Janeba (2024[27]). Some governments could intervene by proposing the “near-shoring” or “friend-shoring” of supply chain partners. It is possible that some beneficial trade interdependencies might be lost from such an approach.
Some geopolitical tensions and policy uncertainties are already being factored into business decisions (Evenett, 2023[28]). A business survey of a large number of operations (Accenture, 2023[29]) and another survey of German companies (EconPol, 2024[30]) showed that, among other types of resilience-improving strategies, an increasing number of companies plan to make significant investments in re-shoring, re-locating and broadening their supplier base. These trends can also be seen at the economy-wide level, such as in the reorientation of bilateral trade between China, and the United States and the European Union, as well as in the growing importance of third economies (OECD, 2024[31]).
Recent analysis of economy-wide impacts shows that reversal of global economic integration could be costly. An IMF summary of model-based analyses of the costs of trade fragmentation undertaken by different authors and institutions in the aftermath of the COVID-19 pandemic and Russia’s war of aggression against Ukraine (IMF, 2023[32]) concluded that the cost of impeding the trade and technology diffusion channel23 could reach as much as 12% of national GDP for some economies.24
The OECD has also explored these impacts. A model-based OECD comparison of two scenarios – a fully “interconnected” trading regime and a “localised” regime—showed that some policies aiming to make value chains more domestic could be costly in terms of efficiency and would not necessarily offer more stability in the face of shocks (Figure 3.20). Compared to the interconnected regime, re-localisation involving higher import tariffs, subsidies to domestic production and additional constraints on international sourcing in all countries25, could decrease global trade by more than 18% and global real GDP by more than 5%, with individual countries losing between 1.1 and 12.2% of GDP depending on the extent and nature of their GVC integration. In addition, when the effects of a stylised set of supply chain shocks were modelled26 in the localised regime, there was little difference in the stability of GDP, production and consumption compared to the interconnected regime. In fact, for more than half of the economies, the stability of GDP decreased in the localised regime. This runs counter to some of the claims in the general debate on the risks of GVCs, and illustrates the reality that openness and geographical diversification of input sources and output destinations in GVCs can offer important options for adjusting to disruptions, as well as exposure to shocks from a greater number of sources.
Other studies aim to illustrate the risks of a more significant disintegration of world trade for global trade and development. A WTO study draws on the WTO global trade model and the methodology developed in Goes and Bekkers (2022[33]) in the context of the possible implications of Russia’s invasion of Ukraine (WTO, 2022[34]). The study models a range of scenarios in a hypothetical situation where increasing geopolitical tensions result in a division of the global economy into Eastern and Western blocks, with countries assigned to the blocks based on how they voted at the United Nations General Assembly on motions condemning Russia’s invasion of Ukraine. It explores impacts on technology diffusion between advanced and developing economies. The study indicates a wide range of possible economic effects, which range from below 1% of GDP for some countries to 12% for others, depending on their level of development and dependence on the technology diffusion channel.27
A related study by the IMF focused on Asia-Pacific and considered the effect of a hypothetical elimination of trade in high-tech manufacturing and energy across similarly defined geopolitical rival blocs (IMF, 2022[35]). The study found global output losses of up to 1.5% of global GDP, but suggested that trade-intensive countries in the Asia-Pacific region would be affected disproportionately, with losses estimated at about 3.3% of GDP. A subsequent assessment by IMF staff considered different fragmentation scenarios ranging from partial to complete trade restrictions between different blocs. Depending on the scenario and trade elasticities which determine the costs of adjustment to trade shocks in the model, output losses ranged from below 1% to 7% of GDP globally in the most disruptive scenarios (Bolhuis, Chen and Kett, 2023[36]).
Another recent OECD modelling study further illustrates some of the economic costs that may be associated with scenarios of global trade fragmentation (Arriola et al., 2024[8]). Looking more closely at possible implications of global trade fragmentation for OECD Member countries, the study modelled the possible economy-wide impacts of a reduction of trade between OECD and MOEs.28 A moderate and highly stylised scenario assumed that all goods and services trade flows between each of the OECD Member countries and each of the MOEs were reduced by 10% in real terms (referred to as a “trade reduction shock” or “trade shock”).29 This scenario was analysed using both the OECD’s Inter‑Country Input-Output (ICIO) data and techniques and the OECD’s CGE trade model METRO.30
The analysis shows that most individual OECD and MOEs lose from this trade reduction scenario, although there is a significant amount of inter-country variation. Depending on the modelling framework used and the country considered, GDP declines range from nil to about 1.7%. OECD Member countries and sectors with stronger trade linkages with MOEs rather than with the OECD fare worse, while stronger linkages within the OECD help mitigate the impacts of the trade shock. OECD Member countries in Asia-Pacific, in particular Korea and Australia, are affected the most, while the OECD Member countries of North America remain largely unaffected. Across all OECD regions, the main driver of these GDP reductions is the decrease in trade with China, even though some OECD Member countries also have noticeable exposure to other MOEs. This is hardly surprising given that China accounts for almost two-thirds of the MOEs’ overall trade with the OECD.
The modelled trade shock is found to hurt the GDP of some MOEs more than those of OECD Member countries. This is because the export and import links which are being disrupted in this scenario represent a larger share of the economy in MOEs. Negative impacts on mining and quarrying reflect important OECD-MOEs mineral resource flows.
Not all sectors of the economies are exposed to the modelled trade shock to the same degree.31 The list of the most impacted industries varies from one country to another, but it is fairly common to find the highest levels of exposure in the primary sector and, more specifically, in industries belonging to the mining and quarrying cluster. This is because the trade shock constrains several important flows of mineral resources between the OECD and MOEs.
Overall, the results of these various modelling studies considering different scenarios of fragmentation in international trade suggest that the debate on trade dependency and de-risking needs to consider carefully the possible costs and benefits of different policy choices. The different methodologies used to produce evidence all demonstrate a relatively high degree of trade interdependency between the OECD and other major economies (and especially between OECD Member countries and China), as well as potentially high economic costs of significant trade disruptions between these trading partners.
References
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← 1. This is in line with much of the recent literature, as summarised in Arriola et al. (Arriola et al., 2024[8]).
← 2. These depend on the technologies used in production and on consumer preferences, and are typically estimated econometrically and included in model-based assessments as model parameters.
← 3. Relying solely on granular trade data does not typically allow one to capture the direct trade-production (or “input-output”) linkages, which may lead to underestimating dependencies and the exposure of national industries. Furthermore, gross trade does not capture vulnerabilities from indirect linkages embedded in global supply chains. For example, computer monitors produced in Korea may indirectly depend on copper produced in Chile that is used to produce the wiring of the LEDs from China used in the Korean computer monitors. Due to limitations of a single methodology, studies often use a mix of methodologies or find innovative ways to address these limitations. For example, Arriola (2024[8]) used a mix of granular trade data analysis and input-output and computable general equilibrium (CGE) modelling to get a more comprehensive picture of the global evolution of trade dependency.
← 4. The US Department of Justice and US Federal Reserve, for example, consider markets with a HHI of between 0.15 and 0.25 to be moderately concentrated, and markets with a HHI equal to or greater than 0.25 to be highly concentrated. See: https://www.justice.gov/atr/herfindahl-hirschman-index.
← 5. This would be obtained if there were only five suppliers and each of them supplied an equal share of 20%, while a value of 0.1 would be obtained if there were ten suppliers with equal shares of 10%. These are only illustrative examples as, in reality, many different constellations of unequal market shares can yield a given HHI value.
← 6. In the analysis, products are defined at the 6-digit level of the United Nation’s Harmonised System (HS) of trade classification. See https://www.wcoomd.org/en/topics/nomenclature/overview/what-is-the-harmonized-system.aspx.
← 7. It should be noted that there is considerable variation around the mean values of the concentration indices across all HS6 products. For example, in the period 2017-19, fifty-two out of 4 839 HS6 products with active trade links, which were equivalent to approximately 1% of all active links, were exported by only one country (i.e. they had HHI=1). More than ninety exported products had HHI readings equal or higher than 0.75 (2% of all active links). In contrast, 3 502 products — or 72% of exported products — had global export HHIs lower than or equal to 0.2 (Arriola et al., 2024[8])
← 8. For example, a list of strategic products adopted in Arriola et al. (2024[8]) followed a study of fragmentation of FDI (IMF, 2023[32]) which had built on a list of advanced natural resources and manufacturing sectors designated as strategic in a study by the Atlantic Council (Tran, 2023[37]).
← 9. Note that this does not take into account market access barriers or preferential trade agreements.
← 10. As argued in Kowalski and Bates (2025 forthcoming[23]) export dependency tends to be important in small open economies, such as Lithuania.
← 11. The measure is also highly correlated with other measures of national trade concentration that can be calculated using the same data and the HHI-trade shares methodology to monitor and assess the extent of trade dependency across countries and over time.
← 12. The MOE grouping is composed of Brazil, China, India, Indonesia, Russia, and South Africa.
← 13. Note also that the relatively high share of the Netherlands as a destination of significantly concentrated exports is explained by the important role played by the Netherlands’ port of Rotterdam as the main entry point of merchandise imports to the European Union. This also illustrates some of the above foreshadowed limitations of using gross trade data to determine trade dependency.
← 14. Here, too, the relatively high share of the Netherlands as a destination of significantly concentrated exports is explained by the important role played by the Netherlands’ port of Rotterdam as the main point of entry of merchandise imports to the European Union.
← 15. The study measured bilateral trade dependency as follows (also introduced in Section 3.4 above): a country’s trade in a product with a specific partner is considered dependent in a given period if: (1) at least 10% of the country’s imports (or exports) of that product come from (are exported to) that specific partner; and at the same time (2) the country cannot easily replace these specific bilateral imports (or exports) with alternative sources (or markets) because the country imports from (or exports to) relatively few suppliers (or markets) or, in other words, the overall concentration of their national imports (exports) of these products is high. This is considered to be the case when the HHI of import concentration across supplying countries (or export concentration across destination countries) is equal to or higher than 0.2.
← 16. The OECD’s trade model, METRO, is a multi-sector, multi-region computable general equilibrium (CGE) model of the world economy that uses data to explore the economic impact of changes in policy, technology, and other factors. METRO tracks the myriad ways economies are connected, how production and trade are linked through global value chains, and how resources are best allocated across all economic activities. For details see www.oecd.org/en/topics/metro-trade-model.html.
← 17. Refers to the markets of factors of production (raw materials, labour, capital, and land) that businesses have to purchase, rent or hire in order to produce goods and services.
← 18. One example here are the employment-protecting policies adopted during the COVID-19 pandemic in several EU countries. That said, the degree of impact under different factor mobility assumptions varies across sectors and countries, and depends on whether the impacted sector has a significant weight in domestic labour and capital markets. For more, see Arriola, Kowalski and van Tongeren (2024[7]).
← 19. Note that due to data and computational limitations the coverage of individual countries in this analysis is more restricted than the detailed trade data analysis presented in earlier parts of this chapter.
← 20. However, when production factors are assumed to not be able to move across sectors, extractive industries as well as manufacturing sectors linked to them (metals, iron and steel, chemicals) move towards the top of the shock exposure rankings. For a discussion of this effect, see Arriola, Kowalski and van Tongeren (2024[7]).
← 21. The analysis distinguishes between 23 sectors (including 15 manufacturing and 8 services sectors), i.e. those displayed in Figure 3.19.
← 22. Note that related OECD analysis looked at an alternative methodology which combined granular trade data and ICIO analysis to further unpack some of this important detail (Berthou, Haramboure and Samek, 2024[22]).
← 23. Other channels include migration, and cross-border capital flows and financial integration, but these are less explored.
← 24. The study also revealed that the estimated costs depend very much on strong and often uncertain assumptions about not only how to represent the complex economic interlinkages characterising the world trading system, but also what types of de-risking or fragmentation should be considered. These could range from small increases in trade policy barriers, through discriminatory regional integration, to the formation of more or less autarchic geopolitical trading blocs.
← 25. The localised economies regime simulated a suite of hypothetical and stylised re-localisation policy responses, in which all countries decided to reduce their supply chain connectedness through an imposition of a 25% import tariff on all imported goods a combination of higher import tariffs, subsidies to domestic production (through imposition of value added subsidies equivalent to 1% of GDP directed to labour and capital in domestic non-services sectors) and additional constraints on international sourcing (achieved through halving export transformation elasticities and import substitution elasticities between domestic and foreign varieties of products and elasticities of substitution between different varieties of foreign products).
← 26. The spectrum of shocks included equally probable and spatially uncorrelated increases and decreases in the cost of bilateral trade (for both imports and exports) between each country or region included in the model and all their trading partners.
← 27. In this study, developing and emerging economies would lose the most from trade and technological decoupling because their economic performance depends the most on the technology diffusion channel, which is affected negatively by decoupling of trade between the blocks.
← 28. Brazil, China, India, Indonesia, Russia, and South Africa.
← 29. All the other trade flows were assumed to remain unaffected directly, but could be affected indirectly, for example through interruption of indirect links involving OECD-MOE trade if such links exist, or through redirection of trade and other economic adjustments.
← 30. As described earlier, both these approaches enable an assessment of economy-wide implications of trade dependencies, though with different levels of country and industry detail and different emphases on various economic adjustment mechanisms. They also take a broader supply chain perspective, and capture not only those trade dependencies that stem from direct import-export relationships, but also those that may result from indirect trade links (e.g. when a product exported from one country to another embeds a component produced in a third country).
← 31. In several cases, the sectors identified as the most heavily dependent on OECD-MOE trade represent only small shares of their country’s economy. On the other hand, the list of highly impacted industries also includes those of great significance from both a domestic and a global point of view.