## How Vulnerable Is Sub-Saharan Africa to Geoeconomic Fragmentation?

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### Key findings on SSA exposure to Geoeconomic Fragmentation (GEF)
- GEF defined as “a policy-driven reversal of integration, often guided by strategic considerations” (IMF, 2023a).
- SSA is highly vulnerable to GEF because of:
  - Higher dependence on food imports and fertilizers.
  - Highest levels of poverty globally.
  - Heavy reliance on volatile commodity exports and weak intra-regional trade linkages.
  - Limited integration into global value chains.
  - Lower financing flows and declining official development assistance (ODA).
  - Insufficient fiscal space in most countries to face fragmentation shocks.
- Historical episode: Russia’s invasion of Ukraine amplified SSA vulnerabilities in 2022:
  - Additional extreme poor between 2021 and 2022: 3.2 million people.
  - Total extreme poor reached: 463 million people.
  - At least 123 million people faced acute food insecurity (IMF, 2022a).

### Quantified macroeconomic impact from trade-channel modeling
- Methodology: Multi-country, multi-sector general equilibrium framework focused on trade-channel costs with commodity- and intermediate-input-specific demand and supply characteristics.
- Median modeled outcome for a world split into two trading blocs (EU & US versus China):
  - The median sub-Saharan African country would face a permanent decline of 4 percent of real GDP after 10 years relative to a no-fragmentation baseline.
- Heterogeneity by SSA country groups (long-term losses under GEF):
  - Non-oil resource-rich exporters: almost 5 percent of GDP loss.
  - Non-resource-rich countries: 4.2 percent of GDP loss.
  - Oil exporters: 1.6 percent of GDP loss.
- Strategic Decoupling scenario outcomes:
  - Typical SSA country: almost no change in GDP over 10 years.
  - Median SSA oil exporter: GDP increases by 0.14 percent after 10 years.
  - Other resource-rich exporters: GDP increases by 0.07 percent after 10 years.
  - Non-resource rich exporters: GDP increases by 0.04 percent after 10 years.
  - Rest of the world: minimal estimated gains of 0.04 percent of GDP.

### Modeling framework and scenario definitions
- Model environment:
  - Multi-country, multi-sector, general equilibrium model (Bolhuis et al., 2023).
  - Distinguishes commodity sectors and non-commodity sectors; commodities used as intermediate inputs to produce non-commodities.
  - Representative trading firms combine local products into a composite sectoral good; trade subject to iceberg trade costs.
  - Simulations use exact hat algebra (Dekle et al., 2007).
- Rules of thumb (preserved equation expressions):
  - dlnP^k = (1/θ^k) ln(1/(1−π^other_k))
  - Output change approximated by three additive terms involving (1/θ̄^k) ln(1−π̄_{n,other}^k), α_n(1−μ_n)/(1−α_n) * (1/θ̄^k) ln(1−π̄_{n,other}^k), and α_n μ_n/(1−α_n) * (1/θ̄^j) ln(1−π̄_{n,other}^j).
- Geoeconomic Fragmentation (GEF) scenario:
  - World split into two separate trading blocs (US-centered and China-centered).
  - Allocation to blocs based on whether SSA countries traded more with the US or with China in 2019.
  - Free trade within blocs; no trade between blocs (infinite trade costs for specified sectors).
  - Robustness check: allocation based on geopolitical closeness (‘Ideal Point Distance’ IPD) with two manual reassignments (Russia and Mali).
- Strategic Decoupling (SD) scenario:
  - USA and EU impose barriers to prohibit trade of all goods from Russia and vice versa.
  - Trade barriers in high-tech sectors between US-EU and China; rest of the world (ROW) trades freely.
  - High-tech sectors: Electrical and Machinery Equipment and Transport Equipment (OECD (2011) classification in Eora).
- Data:
  - Bilateral trade data for 2019 with 24 aggregated sectors, 82 agricultural commodities, 54 mining commodities, 145 countries, 32 SSA countries.
  - SSA country groupings: oil exporters (net oil exports ≥ 30 percent of total exports), other resource-intensive (nonrenewable natural resources ≥ 25 percent of total exports), non-resource-intensive (others).

### Transmission channels and mechanisms
- Trade channel (primary):
  - Reconfiguration of supply chains and “friendshoring” could reduce SSA’s trade, impeding export-led growth, technology transfer, and access to cheap imports.
  - High dependence on foreign staples, agriculture products, and fertilizers implies higher domestic prices and supply shortages under GEF.
  - Sudden trade disruptions during 2020–22 added 40 million people to the pre-2020 acutely food insecure population in SSA (IMF, 2022c).
  - Pass-through from global to local food prices is “close to unity” for many SSA countries (Okou et al., 2022).
- Financial channel:
  - GEF could reduce access to financial inflows, investment, productivity growth, and knowledge spillovers.
  - IMF staff estimate: SSA could lose about $10 billion of FDI and official development assistance inflows under severe capital-cut scenarios, equivalent to about half a percent of GDP a year (based on average 2017–19).
  - Friendshoring can reduce FDI in trade-related sectors and the domestic economy; increased policy uncertainty can delay investment.
  - GEF can increase currency volatility, depreciations, and inflationary pressure, making foreign debt servicing more costly.
- Commodity channel and input-output amplification:
  - Countries with deeper supply chains and higher intermediate input shares suffer larger indirect losses.
  - Commodity dependence amplifies vulnerability; critical-mineral–rich countries may fare relatively better due to strategic demand for minerals (SSA > half of global production for some minerals).

### Structural drivers of SSA vulnerability
- Trade openness and composition:
  - Trade openness reached 50 percent of GDP in 2021.
  - Financial linkages including FDI and remittances rose to 10 percent of GDP.
  - SSA’s trade captures about 2 percent of the global share.
  - Latin America and the Caribbean: about 6 percent; Emerging Asia (excl. China and India): about 7 percent.
  - SSA exhibits the lowest economic complexity globally.
- Partner re-composition:
  - 1990s: trade with EU and US represented almost three-quarters of SSA total trade; recent years: closer to one-third.
  - Region nearly split between EU-US and China/other EMs.
  - China emerged as the single largest bilateral lender, accounting for almost 60 percent of the region’s total official bilateral debt.
  - In 2022, about 10 percent of SSA’s total external debt came from China.
- Resource endowments:
  - 23 out of the 45 countries in SSA are classified as resource-rich:
    - Eight are oil exporters.
    - 15 are rich in non-oil energy or mining production.
  - Critical-mineral advantage: SSA represents more than half of global production for some critical minerals (e.g., cobalt and manganese).
  - Global exploration investment increase: 20 percent globally in 2022, especially for lithium and nickel deposits in Africa and Latin America.

### Results: trade at risk and projected losses
- Trade at risk:
  - Almost half of SSA’s international trade would be at risk from shifting trade blocs under the extreme GEF scenario.
  - Trade at risk for SSA (~half) is larger than the estimated around 40 percent for Asia and the Pacific and the Middle East and North Africa under the extreme split scenario.
- Projected regional losses:
  - SSA region could experience a permanent decline of 4 percent of real GDP over 10 years under GEF.
  - Potential GEF losses for SSA are twice as much as those in the rest of the world (relative terms).
- Robustness and geopolitical allocation:
  - GEF Geopolitical scenario median SSA loss: 3.6 percent of GDP (versus 4.1 percent under trade-share allocation).
  - Rest of the world median losses: from -2.3 percent of GDP (trade shares assumption) to -3.3 percent of GDP (geopolitical closeness assumption).
  - Oil exporters may experience larger losses when bloc alignment is based on geopolitical closeness.

### Policy recommendations to mitigate GEF losses
- Strengthen regional integration:
  - Active implementation of the African Continental Free Trade Area (AfCFTA); reduce within-region tariff and non-tariff barriers; strengthen customs efficiency, digitalization, and infrastructure.
- Diversify trade partners and improve trade structure:
  - Diversify trade partners to position for trade diversion and new capital inflows; develop a strategic plan to elevate SSA’s participation in global value chains.
- Expand domestic resource base and human capital:
  - Foster accumulation of human capital and technology to enhance resilience.
- Improve business environment and reduce policy uncertainty:
  - Attract investment by lowering entry, regulatory, and tax barriers; use trade promotion agencies to identify and nurture sectors likely to benefit from trade diversion.
- Reduce dependency on food imports and build agricultural capacity:
  - Promote sustainable agricultural practices, invest in infrastructure to increase agricultural productivity, and improve access to finance for subsistence farmers.
- Build financial buffers and revenue capacity:
  - Deepen domestic financial markets, upgrade cross-border payments infrastructure, improve domestic revenue mobilization, and expand financial market infrastructure via digitalization and transparency.
- Multilateral action:
  - Multilateral institutions should reverse damaging restrictions on trade and capital flows, promote engagement and dialogue, and push for cooperation on food security, climate change, and debt resolution.

### Selected exact quantitative points preserved
- Trade openness reached 50 percent of GDP in 2021.
- Financial linkages including FDI and remittances rose to 10 percent of GDP.
- SSA’s trade captures about 2 percent of the global share.
- China accounts for almost 60 percent of the region’s total official bilateral debt.
- In 2022, about 10 percent of SSA’s total external debt came from China.
- 23 out of the 45 countries in SSA are classified as resource-rich:
  - Eight are oil exporters.
  - 15 are rich in non-oil energy or mining production.
- Sudden trade disruptions during 2020–22 added 40 million people to the pre-2020 acutely food insecure population in SSA.
- At least 123 million people were estimated to be living without enough food in 2022.
- IMF staff estimate of potential loss under severe capital-cut scenarios: about $10 billion of FDI and ODA inflows, equivalent to about half a percent of GDP a year (based on average 2017–19).
- Median SSA output loss under severe GEF scenario: 4 percent of output.
- GEF Geopolitical scenario median SSA loss: 3.6 percent of GDP; original trade-based GEF scenario: 4.1 percent of GDP.
- Strategic Decoupling group medians shown in source: 0.0; 0.1; 0.1; 0.2 (Percent of GDP).

*IMF Working Paper: How Vulnerable Is Sub-Saharan Africa to Geoeconomic Fragmentation? (wpiea2024083-print-pdf)*

### 1. The last three decades of Globalization deepened the SSA region Linkages with the World and its

### 1. The last three decades of Globalization deepened the SSA region Linkages with the World and its shocks.

### Key findings on SSA exposure to Geoeconomic Fragmentation (GEF)
- GEF defined as “a policy-driven reversal of integration, often guided by strategic considerations” (IMF, 2023a).
- SSA is highly vulnerable to GEF because of:
  - Higher dependence on food imports and fertilizers.
  - Highest levels of poverty globally.
  - Heavy reliance on volatile commodity exports and weak intra-regional trade linkages.
  - Limited integration into global value chains.
  - Lower financing flows and declining official development assistance (ODA).
  - Insufficient fiscal space in most countries to face fragmentation shocks.
- Historical episode: Russia’s invasion of Ukraine amplified SSA vulnerabilities in 2022:
  - Additional extreme poor between 2021 and 2022: 3.2 million people.
  - Total extreme poor reached: 463 million people.
  - At least 123 million people faced acute food insecurity (IMF, 2022a).

### Quantified macroeconomic impact from modeling trade-channel GEF
- Methodology: Multi-country, multi-sector general equilibrium framework focused on trade-channel costs with commodity- and intermediate-input-specific demand and supply characteristics.
- Main modeled outcome for a world split into two trading blocs (EU & US versus China):
  - The median sub-Saharan African country would face a permanent decline of 4 percent of real GDP after 10 years relative to a no-fragmentation baseline.
- Heterogeneity by SSA country groups:
  - Non-oil resource-rich exporters: experience the greatest losses from GEF.
  - Non-resource-rich countries: experience substantial losses, smaller than non-oil resource-rich exporters.
  - Oil exporters: experience the smallest losses and can better cope via within-bloc expansion of energy supply.
  - Under a more benign “strategic decoupling” scenario, oil exporters could—under certain conditions—have overall macroeconomic gains due to market-share re-shuffling in the energy sector.

### Context from related empirical and modeling literature (selected exact results preserved)
- Fajgelbaum et al. (2020): 2018 US tariff hikes produced losses to consumers and importing firms amounting to 0.27 percent of GDP; aggregate real-income loss declined to 0.04 percent of GDP after accounting for tariff revenue and gains to domestic producers.
- Cerdeiro et al. (2021): Extreme decoupling leading to two blocs could yield losses up to 5 percent of a country’s GDP.
- Javorcik et al. (2022): “Friendshoring” could lower global real GDP by up to 4.6 percent.
- Goes and Bekkers (2023): Sectoral knowledge diffusion constraints can yield welfare contractions as large as 12 percent in some regions.
- Bolhuis et al. (2023): Low-income countries stand to lose the most when GEF leads to a world split into two trading blocs; commodity breakdowns increase estimated output losses relative to simpler models.

### Structural drivers behind SSA’s pronounced vulnerability
- Trade linkages: SSA’s trade is now almost evenly divided between longstanding partners (EU and US) and newer partners (notably China), increasing exposure to bloc-based disruptions.
- Financial linkages: China is now the largest official bilateral creditor to SSA, while multilateral donors remain important but have reduced roles.
- Limited domestic buffers: low fiscal space, constrained progress in physical capital accumulation and financial deepening, declining ODA, population expansion, and climate-related adversities.

### Policy recommendations to mitigate GEF losses
- Strengthen regional integration (including active implementation of the African Continental Free Trade Area (AfCFTA)).
- Diversify trade partners to strategically position for trade diversion and new capital inflows.
- Expand the domestic resource base and foster accumulation of human capital and technology to enhance resilience.
- Improve the business environment and reduce domestic policy uncertainty to attract investment and enable coping mechanisms.
- Develop a strategic plan to elevate SSA’s position in global value chains and reap trade integration benefits.

*IMF Working Paper: How Vulnerable Is Sub-Saharan Africa to Geoeconomic Fragmentation?*

### 1. Three decades of the Region’s Integration in the Global Economy

### 1. Three decades of the Region’s Integration in the Global Economy

### Overview: integration trends and structural features
- Since the early 1990s, trade openness in SSA increased and reached 50 percent of GDP in 2021.
- Financial linkages including FDI and remittances rose to a relatively smaller 10 percent of GDP, offsetting a declining trend in ODA.
- Despite greater trade integration since 1990, SSA’s trade captures about 2 percent of the global share.
  - Latin America and the Caribbean captures about 6 percent.
  - Emerging Asia (excluding China and India) captures about 7 percent.
- SSA exhibits the lowest economic complexity—the ability to export goods with high knowledge content—of the world.
- Major structural vulnerabilities in SSA’s global integration:
  - Concentration in commodity exports (leading exports in half of the countries).
  - Production concentrated in low value added goods.
  - Low global trade share, commodity dependence, and low export value-added make goods trade highly vulnerable to GEF-driven disruptions.

### Evolution of partner composition and implications
- Over time SSA pivoted away from EU-US concentration toward emerging markets, with China leading.
- In the 1990s, trade with the EU and US represented almost three-quarters of total trade for SSA; in recent years this declined to closer to one-third.
- The region’s integration is now nearly split between EU-US and China/other EMs, increasing exposure to output and trade losses under geoeconomic fragmentation (GEF).
- Diversification of partners in investment, debt, and aid:
  - New partners such as India and China increased their role in FDI flows.
  - The stock of FDI predominantly came from the EU and US.
  - China emerged as the single largest bilateral lender, accounting for almost 60 percent of the region’s total official bilateral debt.
  - However, in 2022 only about 10 percent of SSA’s total external debt came from China since the region’s debt still comes largely from multilateral creditors.

### Aggregate linkage index (trade, external flows, external debt)
- An economic linkage index comprising three dimensions—trade openness, external flows (FDI inflows and ODA), and total external debt (each one-third weight)—shows a transition from traditional partners to new partners between 2000–19.
- The re-composition of partners offers:
  - Opportunities: risk diversification in export markets and higher investment flows for some countries.
  - Risks: being almost equally open to two potentially antagonistic blocs increases susceptibility to geopolitical tensions and influence competition.

### The trade transmission channel
- Trade is the main channel through which GEF could affect SSA countries.
  - Reconfiguration of supply chains and “friendshoring” could reduce SSA’s trade with the rest of the world, impeding export-led growth, technology transfer, and access to cheap imports.
  - Trade restrictions and supply chain disruptions could exacerbate food and fuel shortages and worsen external balances and financing needs.
- Import-side vulnerabilities:
  - High dependence on foreign staples, agriculture products, and fertilizers from selected destinations could result in higher domestic prices and supply shortages.
  - Countries with small domestic markets cannot easily substitute foreign supply with domestic production.
  - During 2020–22, sudden trade disruptions added 40 million people to the pre-2020 acutely food insecure population in SSA (IMF, 2022c).
  - This led to an estimate of at least 123 million people—two-thirds of the worldwide food insecure population—living without enough food in 2022.
  - High pass-through from global to local food prices is an important amplifier; Okou et al. (2022) quantify pass-through as “close to unity” among many SSA countries.
- Export-side heterogeneity:
  - 23 out of the 45 countries in SSA are classified as resource-rich:
    - Eight are oil exporters.
    - 15 are rich in non-oil energy or mining production.
  - In a perfectly divided two-world economy, model results indicate losses from disrupted trade in key areas (such as food and agriculture) are not necessarily offset by gains from intra-bloc access for resource-rich countries.
  - Under selected GEF scenarios, SSA resource-rich countries could displace some suppliers to selected destinations and thereby mitigate some losses via forced gained market access.

### The financial transmission channel
- GEF could disrupt access to financial inflows and limit financing options for growth.
  - Declines in financial inflows reduce external financing, investment, productivity growth, and knowledge spillovers.
  - Less capital mobility could interrupt progress toward financial openness.
- IMF staff estimates: in a world where countries cut off capital flow ties with either bloc consistent with the preceding severe scenario, SSA could lose about $10 billion of FDI and official development assistance inflows, equivalent to about half a percent of GDP a year (based on an average 2017–19 estimate).
- Mechanisms and second-round effects:
  - “Friendshoring” can reduce FDI in trade-related sectors and the domestic economy; investors become more risk-averse and favor politically aligned environments.
  - Increased policy uncertainty can delay investment decisions, creating a self-reinforcing negative cycle.
  - GEF can increase currency volatility, depreciations, and inflationary pressure, making foreign debt servicing more costly and deterring foreign investors.
  - Higher import costs under GEF reduce profits for domestic and foreign investors, creating disincentives for investment.
- Debt-restructuring risks:
  - Several SSA countries face aggravated debt vulnerabilities and coordination problems among a diverse creditor group; these problems could worsen if geoeconomic fragmentation deepens.

### Differences in GEF transmission across country groups
- Energy (oil) exporters:
  - Tend to have stronger trade linkages with China and EU-US than other groups.
  - Under extreme GEF, oil exporters forced to choose a bloc could expand energy exports within their chosen bloc, partially offsetting weaker linkages with other partners—conditional on continued global demand for oil.
  - Resilience is conditional and subject to high commodity price volatility.
- Non-resource-intensive and other resource-intensive countries:
  - Non-resource-intensive and other-resource-intensive groups show greater trade partner diversification; ROW represents around 60 percent of trade flows for these groups versus about 20 percent for oil exporters.
  - Greater diversification implies more variance in potential GEF impacts due to differing possibilities for trade and financial diversion.
- Critical-mineral–rich countries:
  - Countries endowed with critical minerals (e.g., cobalt, manganese) may cope relatively better under GEF due to strategic demand for minerals used in renewable energy and advanced technologies.
  - SSA represents more than half of global production for some critical minerals (e.g., cobalt and manganese).
  - Global demand to secure critical mineral supply boosted exploration investments by 20 percent globally in 2022, especially for lithium and nickel deposits in Africa and Latin America.
  - Strategic importance of these minerals could confer advantages to critical-mineral–rich countries under GEF scenarios.

### Key statistics and exact figures cited
- Trade openness reached 50 percent of GDP in 2021.
- Financial linkages including FDI and remittances rose to 10 percent of GDP.
- SSA’s trade captures about 2 percent of the global share.
- Latin America and the Caribbean: about 6 percent of global trade share.
- Emerging Asia (excl. China and India): about 7 percent of global trade share.
- China accounts for almost 60 percent of the region’s total official bilateral debt (as China emerged as the single largest bilateral lender).
- In 2022, about 10 percent of SSA’s total external debt came from China.
- 23 out of the 45 countries in SSA are classified as resource-rich.
  - Eight are oil exporters.
  - 15 are rich in non-oil energy or mining production.
- Sudden trade disruptions during 2020–22 added 40 million people to the pre-2020 acutely food insecure population in SSA.
- At least 123 million people were estimated to be living without enough food in 2022 (two-thirds of the worldwide food insecure population).
- Pass-through from global to local food prices is “close to unity” for many SSA countries (Okou et al., 2022).
- IMF staff estimate of potential loss under severe capital-cut scenarios: about $10 billion of FDI and ODA inflows, equivalent to about half a percent of GDP a year (based on average 2017–19).

*IMF Working Paper chapter: “1. Three decades of the Region’s Integration in the Global Economy”*

### 1. Modeling Framework

### 1. Modeling Framework

### Model environment
- Multi-country, multi-sector, general equilibrium model of Bolhuis et al. (2023) that:
  - Accounts for unique demand and supply characteristics of commodities and cross-border trade of intermediate inputs.
  - Considers N countries, indexed by n and m.
  - Distinguishes two sector types: commodity sectors and non-commodity sectors.
  - Assumes commodities are used as intermediate inputs to produce non-commodities; non-commodities are consumed as final goods and used as intermediate inputs.
  - Local commodity producers use labor and intermediate inputs; local non-commodity producers use labor, intermediate inputs, and commodities.
  - For each non-commodity sector, downstream demand is inelastic because commodities and other inputs are imperfect substitutes.
  - Representative trading firms combine local products from different countries into a composite sectoral good; the composite sectoral good is not tradable.
  - Trade is subject to iceberg trade costs.
  - Representative household in each country exogenously supplies labor, consumes the final good, and receives/sends exogenous international transfers; household income equals labor income plus the trade balance.

### Interpreting and simulating trade fragmentation
- Trade fragmentation is interpreted as policy-driven increases in non-tariff barriers that raise the price of imported goods and services.
- The model simulates the impact of changes in trade barriers using exact hat algebra (Dekle et al., 2007), enabling estimation of impacts on output without estimating initial trade costs and productivity levels.

### Rules of thumb for the impact of fragmentation on domestic prices and output
- Approximation for (log) change in the local price index of a country in sector k:
  - dlnP^k = (1/θ^k) ln(1/(1−π^other_k))    (equation (1) as in source)
  - π^other is the share of initial expenditures of a country on goods from countries in the other bloc.
  - θ^k is the trade elasticity.
  - Interpretation: Prices increase more where access to initial supplies from the other bloc is larger; effect scaled by 1/θ^k.
- Approximation for (log) change in output (real GDP) of country n due to fragmentation:
  - Output change approximated by three additive terms (equation (2) as in source):
    - (1/θ̄^k) ln(1−π̄_{n,other}^k)
    - α_n(1−μ_n)/(1−α_n) * (1/θ̄^k) ln(1−π̄_{n,other}^k)
    - α_n μ_n/(1−α_n) * (1/θ̄^j) ln(1−π̄_{n,other}^j)
  - Definitions:
    - α_n(1−μ_n): country’s average expenditure share on intermediate inputs from non-commodity sectors.
    - α_n μ_n: country’s average expenditure share on intermediate inputs from commodity sectors.
    - ln(1−π̄_{n,other}^k) and ln(1−π̄_{n,other}^j) are the average (log) shares of initial expenditure of country n on non-commodity and commodity goods from countries in the same bloc.
  - Economic interpretation of the three terms:
    - First term: direct effect on prices of final goods — losing access to suppliers raises domestic prices, reducing real GDP.
    - Second term: indirect effect through input-output linkages — countries using more intermediate inputs (deeper supply chains) are more affected.
    - Third term: effect of access to commodities.
  - For all terms: effect increases with the average share of initial expenditures on countries in the same trading bloc and decreases with the average trade elasticity.
- Note: In all cases these are weighted averages with expenditure shares as weights.

### Model limitations
- Does not model some important channels of global economic fragmentation, including trade-induced technology spillovers and uncertainty.
- Range of possible effects for a scenario captures some but not all outcomes related to magnitude and coverage of trade barriers and potential policy responses.
- Decisions by countries to join trading blocs depend on many factors beyond historical trade relations or geopolitical alignment; scenarios should be viewed as illustrative.

### Geoeconomic Fragmentation Scenarios
- Purpose: Quantify potential trade and production losses in SSA under two GEF scenarios, relative to a baseline of no GEF (baseline = end-2019 bilateral trade relations).
- Two scenarios (as defined in Bolhuis et al. (2023) and summarized in Table 1):

  1) Geo-Economic Fragmentation (GEF) — extreme two-bloc separation
  - World split into two separate trading blocs: one centered around the US and another around China.
  - Allocation to blocs: SSA countries that traded more with the US than with China in 2019 are put in the US-centered bloc, and vice versa.
  - Assumptions:
    - Free trade within blocs.
    - No trade between blocs (infinite trade costs for specified sectors to ensure zero trade between groups).
  - Alternative robustness check: allocation based on geopolitical closeness using 'Ideal Point Distance' (IPD) estimates (averaged over 2017-2021) — assign to US or China bloc by higher IPD rank; two manual reassignments: Russia and Mali moved from US group into China group in that check.
  - Intended to capture an autarky-like, extreme split outcome.

  2) Strategic Decoupling (SD) — targeted barriers among key partners
  - Assumptions:
    - USA and EU impose barriers to prohibit trade of all goods from Russia and vice versa.
    - Imposition of trade barriers in high-tech sectors between the US-EU and China.
    - Rest of the world (ROW), including SSA countries, trade freely under SD.
  - High-tech sectors defined following OECD (2011) classification (sectoral R&D intensities); in Eora database high-tech sectors are Electrical and Machinery Equipment and Transport Equipment.
  - Implication: Some SSA countries may gain (absorb displaced markets); effects largely transmit through substitution of partner supply and demand in disrupted markets (e.g., SSA energy exporters displacing Russian oil and gas into the EU).

- No probabilities assigned to the scenarios; they are illustrative.
- Outcomes depend on composition of traded goods and elasticity of substitution between foreign and domestic products; low elasticity (as in many SSA countries for food, agriculture, fertilizers) implies higher vulnerability.

### Data
- Model uses bilateral trade data (including self-trade) for all sectors with detailed disaggregation of commodity sectors for 2019.
- Dataset contains observations in values (USD) at exporter sector/commodity-importer level for:
  - 24 aggregated (manufacturing and services) sectors,
  - 82 agricultural commodities,
  - 54 mining commodities,
  - 145 countries, 32 of which are in SSA.
- SSA country groupings for differentiated GEF impacts:
  - Oil exporters: net oil exports make up 30 percent or more of total exports.
  - Other resource-intensive countries: nonrenewable natural resources represent 25 percent or more of total exports.
  - Non-resource-intensive countries: not classified as oil exporters or other resource-intensive.

### Results of the modeling exercise — key findings and projections
- Trade at risk:
  - SSA has the highest percentage of international trade at risk under GEF due to low intra-regional trade and an almost even split among potential blocs.
  - Almost half of SSA’s international trade would be at risk from shifting trade blocs under the extreme GEF scenario (Figure 11).
  - Trade at risk under GEF for SSA (~half) is larger than the estimated around 40 percent of trade at risk for both Asia and the Pacific and the Middle East and North Africa under the extreme split GEF scenario.
  - The Western Hemisphere and Europe have the lowest trade at risk due to relatively larger domestic markets.

- Projected output losses under GEF (relative to baseline of no fragmentation):
  - SSA region could experience a permanent decline of 4 percent of real GDP over 10 years under GEF.
  - Potential GEF losses for the SSA region are twice as much as those in the rest of the world (relative terms; see Figure 12(a) in source).
  - Heterogeneity across SSA groups (long-term losses under GEF):
    - Other resource-rich exporters: almost 5 percent of GDP loss.
    - Non-resource rich countries: 4.2 percent of GDP loss.
    - Oil exporters: 1.6 percent of GDP loss.
  - Reasoning: Oil exporters face lower aggregate losses because increasing energy exports within a bloc help cope with trade losses in other sectors; energy production mitigates aggregate losses for oil exporters.

- Projected outcomes under Strategic Decoupling (SD):
  - Typical SSA country experiences almost no change in GDP over 10 years under SD (Figure 12.1).
  - SD can yield modest gains for SSA via trade diversion and market openings where banned partners are displaced:
    - Median SSA oil exporter: GDP increases by 0.14 percent after 10 years.
    - Other resource-rich exporters: GDP increases by 0.07 percent after 10 years.
    - Non-resource rich exporters: GDP increases by 0.04 percent after 10 years.
    - Rest of the world: minimal estimated gains of 0.04 percent of GDP.
  - For SSA, effects under SD are driven largely by energy exporters displacing Russia’s oil and gas into the EU and by demand increases for metals or fuels when European countries cease trade with Russia.

- Mechanisms highlighted:
  - Losses increase with a larger share of initial expenditures tied to countries in the other bloc (π measures).
  - Losses are larger when trade elasticities are low (limited ease of substituting suppliers).
  - Input-output linkages amplify losses for economies with deeper supply chains (higher intermediate input shares).
  - Commodity dependence increases vulnerability to trade barriers.

*Source: IMF Working Paper section "1. Modeling Framework" (wpiea2024083-print-pdf).*

### 1. Geoeconomic Fragmentation

### 1. Geoeconomic Fragmentation

### Key findings on output losses and scenario definitions
- Under a severe “geoeconomic Fragmentation” (GEF) scenario, all trade is cut off between countries in US/EU- and China-centered blocs; countries are assigned to blocs based on whether they traded more (imports and exports in 2019) with the US or with China.
- Under a less severe “Strategic Decoupling” scenario, trade ties are cut only between Russia and the US/EU, and all trade ceases between the US/EU and China in high-tech sectors, leading to some diversion of trade flows towards Africa.
- Bars and points in the analysis highlight annual losses for the median country after 10 years relative to a no-fragmentation baseline.
- In the most severe scenario of a world fully split into two isolated trading blocs:
  - Half of the region’s value of current international trade would be affected.
  - The median sub-Saharan African (SSA) country would be expected to experience a decline of 4 percent of output (median decline of 4 percent of GDP).
  - These estimated losses are permanent, smaller than losses during the COVID-19 pandemic but larger than those during the global financial crisis.
- Robustness check using geopolitical “closeness” (Ideal Point Distance political alignment) to form blocs:
  - Output losses in this GEF Geopolitical scenario amount to 3.6 percent of GDP for the median SSA country, slightly less than under the original GEF scenario (4.1 percent).
  - Median losses in the rest of the world increase from -2.3 percent of GDP (trade shares assumption) to -3.3 percent of GDP when blocs form based on geopolitical ties.
  - Oil exporters experience larger losses when bloc alignment is based on geopolitical closeness.
  - In non-resource and other resource-rich SSA countries, the model suggests smaller losses when the GEF scenario assigns countries according to geopolitical closeness.
- Heterogeneity in impacts:
  - Declines are larger in countries that are more integrated into global trade and in countries that initially traded more with the bloc from which they are severed.
  - Declines are also larger for non-oil resource-rich countries, reflecting the extent to which they are forced to cease more trade under such a scenario.
- Under the less severe Strategic Decoupling scenario, there are:
  - Small net positive gains for SSA overall in the model (numbers shown in the source: 0.0; 0.1; 0.1; 0.2 for SSA groups and Rest of the world in the Strategic Decoupling figure).
  - Caution: small aggregate gains could incentivize shifts away from diversification and long-term sustainable development; for some small developing economies higher demand for primary products could trigger over-expansion of lower value-added sectors and harm broader economic development.

### Channels of transmission emphasized
- Direct effects of import prices.
- Amplification from input-output linkages.
- Effects on commodity prices.
- Re-composition of economic partners over time increases vulnerability given:
  - The region’s large susceptibility to commodity market fluctuations.
  - Heavy import dependence on crucial staples.
  - Shifts in trading relationships that reduce market access.

### Policy recommendations for SSA countries (main recommendation: build resilience to GEF)
- Strengthening regional trade integration, such as under the African Continental Free Trade Area (AfCFTA):
  - Reduce within-region tariff and non-tariff trade barriers.
  - Strengthen efficiency in customs, leverage digitalization, and close infrastructure gaps.
  - Prioritize improvements in the quality of institutions, especially the regulatory framework, to promote private participation in infrastructure.
- Improving the region’s integration into global value chains (GVC):
  - Increase the value-added content of trade to enhance diversification and stability of trade flows.
- Devising policies to reduce strong dependency on food imports:
  - Tap the region’s agricultural potential, promote sustainable agricultural practices and farming techniques, invest in infrastructure to increase agricultural productivity, and improve access to finance for subsistence farmers.
- Enhancing and investing in trade in services:
  - Target traditional services (tourism and hospitality) and “modern” services (transport and communications, financial intermediation, business services).
  - Lower or remove regulatory barriers to entry for traditional services; implement targeted industrial policies for modern services.
  - Potential to “leapfrog” from primary-goods-export-led economies to tech-driven services for some countries.
- Building buffers through financial-sector and revenue reforms:
  - Deepen domestic financial markets, upgrade cross-border payments infrastructure, and improve domestic revenue mobilization.
  - Develop local financial markets to broaden sources of financing and lower volatility from reliance on foreign inflows.
  - Upgrade financial market infrastructure through digitalization, increased transparency, regulation, and product diversity to expand financial inclusion and attract broader investor bases.
  - Improve domestic revenue mobilization via revenue administration and tax policy reforms (e.g., expanding the base for value-added taxes and leveraging digitalization in tax collection).
- Positioning to benefit from trade diversion and FDI:
  - Create the right environment to attract FDI (friendshoring opportunities) by improving business environment: lower entry, regulatory, and tax barriers; reduce domestic policy uncertainty; improve governance.
  - Identify and nurture sectors likely to benefit from trade diversion (e.g., commodity exporters potentially displacing parts of Russia’s energy market share in Europe).
  - Use trade promotion agencies to identify opportunities, build skills and capacity for exporters, and re-orient production toward new trade flows.
- Multilateral action:
  - Multilateral institutions should reverse damaging restrictions on trade and capital flows, promote engagement and dialogue across policymakers, and push for multilateral cooperation on food security, climate change, and debt resolution.

### Selected quantitative points preserved from the analysis
- Median SSA output loss under severe GEF scenario: 4 percent of output.
- Robustness: GEF Geopolitical scenario median SSA loss: 3.6 percent of GDP; original trade-based GEF scenario: 4.1 percent of GDP.
- Rest of the world median losses: from -2.3 percent of GDP (trade shares assumption) to -3.3 percent of GDP (geopolitical closeness assumption).
- Chart values (Strategic Decoupling figure, group medians shown in source): 0.0; 0.1; 0.1; 0.2 (group medians expressed as Percent of GDP).
- Footnote: IMF (2023c) finds that comprehensive reforms combined with AfCFTA implementation could increase the median merchandise trade flow between African countries by 53 percent and with the rest of the world by 15 percent, and as a result raise the real per capita GDP of the median African country by more than 10 percent.

*Source: IMF Working Paper “How Vulnerable Is Sub-Saharan Africa to Geoeconomic Fragmentation?” (Excerpt: 1. Geoeconomic Fragmentation).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024083-print-pdf.pdf_