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---

### Introduction and central messages
- Geoeconomic fragmentation is reshaping economic linkages, financial markets, technological diffusion, commodity prices, and trade.
- Focus: economic outcomes related to shifting trade patterns for the CCA, MENA and Pakistan.
- Contextual observations:
  - Russia’s war in Ukraine led to a surge in trade activity for CCA countries driven primarily by increased transit trade and trade diversion.
  - MENA and Pakistan face geoeconomic uncertainties and shocks, including regional conflicts and trade security threats (for example, attacks on merchant vessels navigating the Bab el-Mandeb Strait).
- Three illustrative scenarios assessed:
  - Scenario 1: Trade diversion from targeted trade restrictions among larger economies could lead to modest output gains, particularly in the CCA region.
  - Scenario 2: Maintaining neutrality—MENA and CCA countries could act as intermediaries between blocs that have severed trade linkages, benefiting from larger gains in exports and economic output.
  - Scenario 3: A three-bloc arrangement in which many CCA and MENA countries and Pakistan would experience trade and output losses; the most affected countries would be in the CCA and the MENA region excluding the countries of the Gulf Cooperation Council (GCC).
- Central policy message:
  - Reducing trade restrictions, easing regulatory constraints, and improving infrastructure can lead to significant increases in trade and income and mitigate negative impacts of reduced trade flows.
- Recommended strategic priorities for CCA, MENA, and Pakistan:
  - Recalibrate trade policies.
  - Diversify market outreach.
  - Strengthen infrastructural frameworks.
  - Reduce trade barriers, promote product diversification, and enhance infrastructure capabilities to build resilience.

### Stylized facts and empirical patterns
- Fact 1: Trade interventions
  - Trade interventions have surged by 70 percent globally since 2019.
  - The average number of trade interventions affecting economies in the CCA and MENA regions and Pakistan has nearly doubled since 2018, with a particularly pronounced increase in the MENA region and Pakistan.
  - New measures: export bans, quotas, licensing requirements, export subsidies; import measures include tariffs, subsidies, and other import restrictions.
- Fact 2: Shifts in trade patterns (changes between 2021 and 2023 following Russia’s war in Ukraine)
  - CCA’s share in EU, Russian, and US nonhydrocarbon exports increased by 74, 45, and 118 percent, respectively.
  - CCA’s share in EU, Russian, and US nonhydrocarbon imports rose by 17, 114, and 9 percent, respectively.
  - MENA’s share in EU, Russian, and US hydrocarbon imports increased by 30, 463, and 17 percent, respectively.
  - CCA expanded share in China’s nonhydrocarbon exports via greater traffic through the Middle Corridor and surged transported volumes.
  - Trade diversion toward the CCA increased imports from and exports to major partners across extractive industries and manufacturing (iron and steel, electrical machinery, chemicals, vehicles).
- Fact 3: Large trade barriers and gaps
  - CCA, MENA, and Pakistan (notably outside the GCC) have large gaps relative to the global frontier in:
    - Trade barriers (tariffs and nontariff barriers).
    - Infrastructure quality and logistics performance.
    - Regulatory quality.
  - Removing these gaps could deliver sizable trade gains.

### Literature context and contribution
- Empirical/theoretical findings summarized:
  - Gopinath and others (2024): trade and FDI flows declined by approximately 12 percent and 20 percent, respectively, between countries in opposing geopolitical blocs versus within the same bloc.
  - Ahn and others (2023): fragmentation reduces FDI, weakens global supply chains, increases trade costs, and reduces economic welfare.
  - Cerdeiro and others (2021): technological decoupling can cause large potential GDP losses.
  - Alvarez and others (2023): fragmentation heightens commodity price volatility; commodity-dependent exporters are especially vulnerable.
  - Campos and others (2023): emerging markets may diversify partners but remain susceptible via commodity dependence.
  - Baba and others (2023): the EU faces challenges maintaining trade volumes amid protectionism and trade wars.
  - Hakobyan and others (2023); Aiyar and others (2023); Broner and others (2024); Clayton and others (2024); Javorcik and others (2024); Freund and others (2024): contributions on regional adaptation, policy recommendations, theoretical frameworks, friend-shoring tradeoffs, and supply-chain reshaping.
- This working paper:
  - Provides detailed empirical analysis of geoeconomic fragmentation impacts on CCA, MENA, and Pakistan.
  - Quantifies trade and GDP outcomes across three illustrative scenarios.
  - Emphasizes proactive measures: reducing trade barriers, improving infrastructure, and enhancing regulatory quality.

### Model and estimation: structure and calibration
- Framework:
  - Multi-country, single-output general equilibrium model combined with structural gravity.
  - Structural gravity specification follows Larch and Yotov (2016), Yotov and others (2016), and Campos and others (2023).
- Theoretical basis:
  - Armington (1969) framework: N countries, single good differentiated by origin; CES utility with elasticity σ>1.
  - Key variables: Xij, Ej, Yi, Y, tij, σ, Pj, Πi, factory-gate price, trade deficit relation Ei = φi Yi = φi pi Qi.
- Trade costs and iceberg interpretation:
  - Trade costs enter as [tij Πi Pj]1−σ.
  - Adopt iceberg trade costs (Samuelson 1952) to simulate changes in transportation costs, tariffs, and other frictions.
- Counterfactual policy channels:
  - Reduce trade barriers (tariffs and nontariff barriers).
  - Upgrade infrastructure.
  - Enhance regulatory framework and ease regulatory constraints.
- Illustrative scenarios (analytical, not forecasts):
  - Scenario 1: EU and US halt all trade with Russia; others continue.
  - Scenario 2: Three blocs—Eastern (China and Russia), Western (EU and US), Neutral (all other countries including CCA, MENA, Pakistan); trade between Eastern and Western blocs ceases; neutral bloc trades with any partner.
  - Scenario 3: Blocs based on UNGA 77th Session voting patterns; Eastern = top 25th percentile geopolitical distance from G7; Western = EU and US; Neutral = all other countries; trade between Western and Eastern blocs ceases; neutral trades with any bloc.
- Data, calibration, estimation:
  - Core data: CEPII Gravity dataset (Conte and others 2023); sample 2000–2019; coverage: all 32 countries in MENA and CCA regions.
  - Additional variables: GDP per capita, distance, border contiguity, common language, landlocked status, tariffs and nontariff barriers, regulatory quality, World Bank’s Logistics Performance Index (LPI).
  - Calibration: σ calibrated equal to 7 → trade elasticity of 6 (mean value from Bolhuis and others (2023)); short-run elasticity likely lower.
  - LPI: composite of physical infrastructure, customs performance, logistics quality, logistics efficiency.

### Reduced-form estimation results (first-stage PPML)
- Key structural parameters (Table 1):
  - 훽̂MATR: -.2274*** (.0159)
  - 훽̂infrastructure: .1533*** (.0300)
  - 훽̂regulation: .1223*** (.0443)
- Quantitative reduced-form findings:
  - A one standard deviation reduction in trade barriers → average increase of 104 percent in bilateral trade.
  - Most trade gains attributed to changes in non-tariff barriers (NTBs).
  - A one standard deviation improvement in exporter infrastructure → average increase in bilateral trade of 8.7 percent.
  - A one standard deviation enhancement in regulatory environment → average increase of 12 percent in bilateral trade.
- Gravity first-stage detailed coefficients (selected exact values from Table A1):
  - Exporter non_tariffs barriers: Column (1): 0.0195** (0.00990)
  - Importer non_tariffs barriers: Column (1): -0.0201** (0.00859)
  - Exporter Infrastructure: Column (4): 0.402** (0.167)
  - Importer Infrastructure: Column (2): 0.237*** (0.0732)
  - Exporter Regulatory: Column (5): -0.214** (0.0929)
  - Importer Regulatory: Column (4): 0.105*** (0.0358)
  - Exporter Log gdp: Column (1): 0.850*** (0.0180)
  - Importer Log gdp: Column (1): 0.815*** (0.0214)
  - Log_distance: Column (1): -0.753*** (0.0281)
  - Contiguity: Column (1): 0.520*** (0.126)
  - Common language: Column (1): 0.231** (0.0917)
  - Exporter landlocked: Column (3): -0.167* (0.0968)
  - Importer landlocked: Column (1): -0.223*** (0.0838)
  - Observations: Column (1): 27,867; Column (5): 15,044
  - R-squared: Column (1): 0.703; Column (5): 0.735

### General equilibrium results: baseline fragmentation (without additional policy actions)
- Scenario 1 outcomes:
  - CCA export gains: about 1 percent.
  - MENA economies and Pakistan: average gains of approximately 0.5 percentage point.
- Scenario 2 outcomes:
  - Exports across CCA and MENA countries and Pakistan would increase by 2−3 percent.
  - Output growing by an average of 0.25−0.4 percent.
- Scenario 3 outcomes:
  - Several CCA and MENA countries and Pakistan would experience trade and GDP losses.
  - Losses particularly large for MENA countries outside of the GCC.
- General observations:
  - Impact on GDP is roughly an order of magnitude smaller than impact on exports.
  - Reasons: exports are a relatively small share of GDP for many MENA and CCA countries; substitution toward domestic consumption offsets export declines (while reducing consumer welfare).
  - Structural gravity limitation: does not incorporate third-country intermediation; results may be a lower bound for intermediary “connector” countries.

### Policy counterfactuals and calibrated impacts (20 percent reduction in policy gaps)
- Policy calibration: reduce gap in trade restrictions, infrastructure, regulatory quality by 20 percent relative to advanced economies.
- Scenario 1 — policy impacts (relative to baseline):
  - Reducing trade restrictions:
    - Exports boost by 14 percent for CCA countries.
    - Exports boost by over 15 percent for non-GCC MENA countries and Pakistan.
  - Upgrading infrastructure:
    - Exports increase by about 7 percent in the CCA.
    - Exports increase by 8 percent in non-GCC MENA countries and Pakistan.
  - Improving regulatory environment:
    - Exports increase by more than 3 percent in the CCA.
    - Exports increase by around 6 percent in non-GCC MENA countries and Pakistan.
  - Output impacts:
    - CCA annual output increase between 1 and 2 percent.
    - Non-GCC MENA countries and Pakistan annual output increase between 1 and 3 percent.
  - GCC: smaller average impact on exports and GDP given proximity to global frontier.
- Scenario 2 — policy impacts:
  - Reducing trade barriers:
    - Exports increase by more than 17 percent for CCA countries.
    - Exports increase by over 20 percent for non-GCC MENA countries and Pakistan.
    - Exports increase by 6 percent for GCC countries.
  - Upgrading infrastructure:
    - Exports increase by 22 percent for CCA countries.
    - Exports increase by 24 percent for non-GCC MENA countries and Pakistan.
    - Exports increase by 6 percent in the GCC.
  - Improving regulatory environment:
    - Exports boost by 3 percent (CCA), 11 percent (non-GCC MENA & Pakistan), and 6 percent (GCC).
  - Output gains from policy: range from 0.4 to 6.3 percent for CCA and non-GCC MENA countries and Pakistan.
- Scenario 3 — policy impacts:
  - Reducing trade restrictions:
    - Exports rise by more than 11 percent in the CCA.
    - Exports rise by about 8 percent in the non-GCC MENA and Pakistan group—effectively eliminating baseline output losses.
  - Upgrading infrastructure and improving regulatory quality:
    - Both boost exports and output across the region, reversing adverse effects under baseline Scenario 3.

### Partial-equilibrium estimation, algorithm, and interpretation
- Partial-equilibrium structural gravity (equation (13)):
  - X_ij = exp[θ_i + ω_j + γ_ij + β_1 Z_ij] η_ij
  - Estimator: Poisson Pseudo-Maximum Likelihood (PPML) (Santos Silva and Tenreyro 2006); sample 2000−19; bilateral trade data from CEPII.
  - First-stage estimates used to construct P_j and Π_i and baseline trade cost proxy:
    - [t̂_ij^(1−σ)]_BLN = exp[θ̂_i + ω̂_j + β̂_1 Z_ij]
- General equilibrium algorithm (steps):
  - Step 1: Solve baseline gravity with PPML; construct multilateral resistances; estimate σ̂; solve structural gravity for baseline indexes.
  - Step 2: Define counterfactual (raise trade costs between opposing blocs; simulate 20 percent policy gap reductions).
  - Step 3: Solve counterfactual structural gravity for conditional and full endowment general equilibrium.
  - Step 4: Construct %∆X̂_it and %∆GDP̂_it relative to baseline.
- Interpretation: changes in exports and GDP reported relative to baseline without policy action; output losses typically smaller than export losses due to domestic substitution.

### Annex: Trade interventions descriptions (selected)
- Export interventions:
  - Export Bans; Export Quotas; Export Licensing requirements; Export Subsidies.
- Import interventions:
  - Tariffs; Subsidies; Other import restrictions (non-tariff barriers such as import quotas, technical standards, sanitary regulations, customs procedures).

### Conclusion: synthesis and recommended policy strategies
- Geoeconomic fragmentation affects trade patterns and economic outcomes in CCA, MENA, and Pakistan; severity depends on scenario.
- Scenario-specific highlights:
  - Scenario 1: limited trade diversion yields unintended spillovers and modest gains (exports up to about 2−3 percent under Scenario 2; output up to 0.4 percent).
  - Scenario 3: significant trade and output losses possible (CCA experiencing a 1.1 percent decline in exports; MENA group excluding GCC facing a 7.4 percent decrease).
- Recommended policy strategies:
  - Reduce trade barriers: potential export increases range by scenario and region (examples: 14 percent CCA; over 15 percent non-GCC MENA in Scenario 1; over 17 percent CCA in Scenario 2).
  - Ease regulatory constraints: potential export increases of 3−6 percent depending on region and scenario.
  - Enhance infrastructure investment: potential export increases of 7−8 percent (Scenario 1); 22 percent (CCA) and 24 percent (non-GCC MENA) in Scenario 2.
  - Expected output effects: Scenario 1: annual output increases of 1−2 percent (CCA) and 1−3 percent (non-GCC MENA); Scenario 2: output increases ranging from 0.4 percent to 6.3 percent for CCA and non-GCC MENA countries and Pakistan.
- Policy takeaway:
  - Agile, forward-looking measures—recalibrating trade policies, diversifying market outreach, and fortifying infrastructure frameworks—can help CCA, MENA, and Pakistan address challenges from geoeconomic fragmentation.

*Source: wpiea2025267-source-pdf - Appendix I; Sections 4, 6, and Appendix II*

### Appendix I .............................................................................................................

### Appendix I

### Introduction
- The global economic landscape is increasingly characterized by geoeconomic fragmentation, fundamentally changing economic linkages between economies and affecting financial markets, technological diffusion, commodity prices, and trade.
- The paper focuses on economic outcomes related to shifting trade patterns for the CCA, MENA and Pakistan.
- Key contextual observations:
  - Russia’s war in Ukraine has resulted in a significant shift in global trade policy, leading to a surge in trade activity for CCA countries driven primarily by increased transit trade and trade diversion.
  - The MENA region and Pakistan face broader geoeconomic uncertainties and shocks, including ongoing regional conflicts and trade security threats (for example, attacks on merchant vessels navigating the Bab el-Mandeb Strait).
- Three illustrative scenarios assessed:
  - Scenario 1: Trade diversion from targeted trade restrictions among larger economies could lead to modest output gains, particularly in the CCA region.
  - Scenario 2: Maintaining neutrality—MENA and CCA countries could act as intermediaries between blocs that have severed trade linkages, benefiting from larger gains in exports and economic output.
  - Scenario 3: A three-bloc arrangement in which many CCA and MENA countries and Pakistan would experience trade and output losses; the most affected countries would be in the CCA and the MENA region excluding the countries of the Gulf Cooperation Council (GCC).
- Central policy message:
  - Economic policies can play a vital role in a fragmented world. Reducing trade restrictions, easing regulatory constraints, and improving infrastructure can lead to significant increases in trade and income and help mitigate negative impacts of reduced trade flows.
- Recommended strategic priorities for CCA, MENA, and Pakistan:
  - Recalibrate trade policies.
  - Diversify market outreach.
  - Strengthen infrastructural frameworks.
  - Reduce trade barriers, promote product diversification, and enhance infrastructure capabilities to build resilience.

### Stylized Facts: Geoeconomic Fragmentation and New Trade Patterns
- Fact 1: The number of trade interventions has increased.
  - Trade interventions have surged by 70 percent globally since 2019.
  - The average number of trade interventions affecting economies in the CCA and MENA regions and Pakistan has nearly doubled since 2018, with a particularly pronounced increase in the MENA region and Pakistan.
  - New measures affecting these regions are varied; export bans, quotas, licensing requirements, and export subsidies are the most prevalent. New import measures include tariffs, subsidies, and other import restrictions.
- Fact 2: Trade patterns have been shifting.
  - Following the start of Russia’s war in Ukraine in 2022, changes in partner shares between 2021 and 2023 include:
    - The CCA’s share in EU, Russian, and US nonhydrocarbon exports increased by 74, 45, and 118 percent, respectively.
    - The CCA’s share in EU, Russian, and US nonhydrocarbon imports rose by 17, 114, and 9 percent, respectively.
    - In MENA, shifts were mainly among oil exporters and for hydrocarbon exports as the European Union substituted some Russian-supplied oil and gas; MENA’s share in EU, Russian, and US hydrocarbon imports increased by 30, 463, and 17 percent, respectively.
  - The CCA region also expanded its share in China’s nonhydrocarbon exports, reflecting reorientation of trade with greater traffic through the Middle Corridor and surged transported volumes.
  - Trade diversion toward the CCA entailed noticeable increases in imports from and exports to major trading partners across many product categories, particularly extractive industries and manufacturing such as iron and steel, electrical machinery, chemicals, and vehicles.
- Fact 3: MENA and CCA economies face large trade barriers.
  - Economies in the CCA and MENA regions and Pakistan (notably outside the GCC) have large gaps relative to the global frontier in:
    - Trade barriers (tariffs and nontariff barriers).
    - Infrastructure quality and logistics performance.
    - Regulatory quality.
  - These gaps are potentially associated with constrained trade potential, and their removal could deliver sizable trade gains.

### Literature Review
- Recent empirical and theoretical findings summarized in the paper:
  - Gopinath and others (2024): Using detailed bilateral data, find trade and FDI flows declined by approximately 12 percent and 20 percent, respectively, between countries in opposing geopolitical blocs versus within the same bloc.
  - Ahn and others (2023): Geoeconomic fragmentation reduces FDI, weakens global supply chains, increases trade costs, and reduces economic welfare.
  - Cerdeiro and others (2021): Technological decoupling can cause large potential GDP losses.
  - Alvarez and others (2023): Fragmentation heightens volatility in commodity prices and makes commodity-dependent exporters especially vulnerable.
  - Campos and others (2023): Emerging markets may try to diversify partners, but dependence on commodity exports increases susceptibility to external shocks.
  - Baba and others (2023): The EU faces significant challenges maintaining trade volumes amid shifting global landscape; increased protectionism and trade wars could severely affect export-dependent economies.
  - Hakobyan and others (2023): Regions adapt unevenly—some strengthen intra-regional trade while others struggle to find new markets.
  - Aiyar and others (2023): Recommend greater regional integration and maintaining open trade channels to build resilient trade networks.
  - Broner and others (2024) and Clayton and others (2024): Provide theoretical treatments of geopolitical interactions and fragmentation with hegemonic large countries.
  - Javorcik and others (2024): Discuss friend-shoring tradeoffs between supply chain resilience and economic costs.
  - Freund and others (2024): Empirical evidence on reshaping supply chains between China and the United States; China remained top US import partner in 2022 despite shifts.
- Contribution of this working paper:
  - Presents a detailed empirical analysis of economic impacts of geoeconomic fragmentation focusing on CCA and MENA regions and Pakistan.
  - Quantifies potential trade and GDP outcomes across three illustrative scenarios, highlighting both risks and opportunities.
  - Underscores importance of proactive measures—reducing trade barriers, improving infrastructure, and enhancing regulatory quality—to mitigate negative impacts and leverage opportunities.

*Source: wpiea2025267-source-pdf - Appendix I*

### 4.  Model and Estimation

### 4.  Model and Estimation

### Modeling framework and objectives
- Integrates two dimensions to assess geoeconomic fragmentation:
  - Impact of geoeconomic fragmentation scenarios (trade barriers, regional and global economic alliances) on exports and GDP.
  - Counterfactual analysis to assess effectiveness of policies (trade policies, infrastructure upgrades, improvements in the regulatory environment).
- Uses a multi-country, single-output general equilibrium model combined with structural gravity (general equilibrium analysis of structural gravity models of trade) to simulate scenarios, policy changes, and national/global responses.
- Structural gravity specification follows Larch and Yotov (2016), Yotov and others (2016), and Campos and others (2023), and closely follows the framework established by Yotov and others (2016).

### Core theoretical structure
- Benchmark based on Armington (1969): world of N countries, each producing a single good differentiated by origin; preferences identical across countries with CES utility and elasticity of substitution σ>1.
  - Utility function notation preserved (equation (1) in the source).
  - Budget constraint (equation (2)) and consumer spending solution leading to Xij (equation (3)).
- Market-clearing condition for origin i (equation (4)) and algebraic rearrangements producing key structural gravity system equations (equations (5)–(10)).
- Key variables and interpretations:
  - Xij: trade flows from exporter i to importer j.
  - Ej: total expenditure in importer country j.
  - Yi: total production in exporting country i.
  - Y: world output.
  - tij: bilateral trade frictions between i and j.
  - σ>1: elasticity of substitution among goods from different countries.
  - Pj and Πi: inward and outward multilateral resistances that translate bilateral policy effects to country-specific consumer and producer prices.
- Factory-gate price under perfect competition (equation (11)).
- Trade deficit relation: Ei = φi Yi = φi pi Qi (equation (12)); φi exogenous parameter: trade deficit if φi>1, trade surplus if 0<φi<1.

### Trade costs and iceberg interpretation
- Trade costs enter as [tij Πi Pj]1−σ and capture the wedge between actual trade and frictionless trade.
- Trade costs encompass transportation (freight and time), policy barriers (tariffs, nontariff restrictions), information costs, currency exchange, legal/regulatory costs, cultural differences, etc.
- Adopt iceberg trade costs (Samuelson 1952): only a fraction of goods arrives, allowing straightforward counterfactual simulations of changes in transportation costs, tariffs, and other trade frictions.

### Counterfactual capability and policy channels
- Structural gravity framework (equations (8)–(12)) facilitates decomposition of channels through which trade policy and other determinants influence trade.
- Policy levers explicitly considered in counterfactuals:
  - Reducing trade barriers (tariffs and nontariff barriers).
  - Upgrading infrastructure.
  - Enhancing regulatory framework and easing regulatory constraints.
- Counterfactual adjustments achieved by changing iceberg cost magnitudes to simulate the effects on trade volumes and output.

### Illustrative geoeconomic fragmentation scenarios
- Three illustrative scenarios (not forecasts; analytical and hypothetical):
  - Scenario 1:
    - European Union and the United States halt all trade with Russia.
    - Trade among other countries continues as usual.
    - Conceptually aligned with “strategic decoupling” scenario in Bolhuis, Chen, and Kett (2023).
  - Scenario 2:
    - World divided into three blocs: Eastern bloc (China and Russia), Western bloc (European Union and the United States), and a neutral bloc (all other countries, including CCA and MENA countries and Pakistan).
    - Trade between Eastern and Western blocs ceases; neutral bloc continues trading with any partner.
    - Generates stronger trade diversion for MENA and CCA countries relative to Scenario 1.
  - Scenario 3:
    - Blocs determined using voting patterns in the United Nations General Assembly during the 77th General Assembly Session (began in September 2022).
    - Compute ideal point distance measure (Bailey and others (2017)); separate world into three blocs:
      - Eastern bloc: countries in the top 25th percentile of geopolitical distance from G7 countries.
      - Western bloc: European Union and the United States.
      - Neutral bloc: all other countries.
    - Trade between Western and Eastern blocs ceases; neutral bloc can trade with any bloc.
- Note: The scenarios are illustrative and hypothetical; results presented for groups of countries and may not apply to individual countries within each group.

### Data, calibration, and estimation details
- Core trade and geographic data: CEPII’s Gravity dataset (Conte and others 2023).
- Sample period: yearly observations from 2000 to 2019.
- Coverage: all 32 countries in the MENA and CCA regions.
- Additional data:
  - Macroeconomic variables: GDP per capita.
  - Geographic factors: distance, border contiguity, common language, landlocked status.
  - Trade barriers: tariffs and nontariff barriers.
  - Regulatory quality measures to evaluate government effectiveness in promoting trade.
  - Infrastructure quality: World Bank’s Logistics Performance Index (LPI).
- Calibration:
  - Elasticity of substitution σ calibrated as equal to 7, which implies a trade elasticity of 6.
  - This corresponds to the mean value of long-run trade elasticities surveyed in Bolhuis and others (2023).
  - Short-run elasticity of substitution likely lower, implying trade and GDP losses could be larger in the short run.
- LPI described as a composite measure of physical infrastructure, customs performance, logistics quality, and logistics efficiency; preferred for capturing trade-related aspects of infrastructure.

*Source: IMF staff, “4.  Model and Estimation” from the provided PDF chapter.*

### 6.  Results

### 6. Results

### Reduced-form Model
- First-stage structural estimation: separate reduced-form gravity model for each of the three scenarios (equation (8)).
- Key structural parameters (elasticities of trade) from Table 1:
  - 훽̂MATR: -.2274*** (.0159)
  - 훽̂infrastructure: .1533*** (.0300)
  - 훽̂regulation: .1223*** (.0443)
  - Sources: IMF staff calculations. Notes: Table reports parameter values for elasticity of trade with respect to MATR (an index of trade restrictions), infrastructure, and regulatory index. Standard errors are in parentheses. *** indicates significance at the 1% level.
- Quantitative findings:
  - A one standard deviation reduction in trade barriers results in an average increase of 104 percent in bilateral trade.
  - Most trade gains are attributed to changes in non-tariff barriers (NTBs) (see Table A1 in the Appendix).
  - A one standard deviation improvement in the infrastructure of the exporting country correlates with an average increase in bilateral trade of 8.7 percent.
  - A one standard deviation enhancement in the regulatory environment is associated with an average increase of 12 percent in bilateral trade.
- Model features and implications:
  - Gravity models include importer and exporter fixed effects, absorbing many traditional gravity covariates; Appendix Table A.1 contains more detailed estimates.
  - Traditional gravity variables (income, distance, contiguity, common language, landlocked status) remain important.
  - Infrastructure improvements alone may have limited impact on exports unless accompanied by broader measures addressing systemic trade barriers.

### General Equilibrium Results
- Scenario outcomes (baseline fragmentation without additional policy actions):
  - Under Scenario 1:
    - CCA export gains: about 1 percent.
    - MENA economies and Pakistan: average gains of approximately 0.5 percentage point.
  - Under Scenario 2:
    - Exports across CCA and MENA countries and Pakistan would increase by 2−3 percent.
    - Output growing by an average of 0.25−0.4 percent.
  - Under Scenario 3 (more severe):
    - Several CCA and MENA countries and Pakistan would experience trade and GDP losses.
    - Losses particularly large for MENA countries outside of the GCC.
- Additional general findings:
  - Across all scenarios, the impact on GDP is roughly an order of magnitude smaller than the impact on exports.
  - Explanations for smaller GDP impact:
    - Exports constitute a relatively small share of GDP for many MENA and CCA countries.
    - Substitution effect: higher trade barriers make imports relatively more expensive, increasing domestic consumption and partially offsetting export declines (but reducing consumer welfare).
  - Structural gravity model limitation: only incorporates direct exporter–importer trade, not third-country intermediation; results may be a lower bound for potential intermediary “connector” countries.

### Impact of trade policies
- Policy calibration: actions calibrated to achieve a 20 percent reduction in policy gaps compared to advanced economies.
- Scenario 1 — Policy impacts relative to baseline:
  - Reducing trade restrictions:
    - Exports boost by 14 percent for CCA countries.
    - Exports boost by over 15 percent for non-GCC MENA countries and Pakistan.
  - Upgrading infrastructure:
    - Exports increase by about 7 percent in the CCA.
    - Exports increase by 8 percent in non-GCC MENA countries and Pakistan.
  - Improving regulatory environment:
    - Exports increase by more than 3 percent in the CCA.
    - Exports increase by around 6 percent in non-GCC MENA countries and Pakistan.
  - Output impacts:
    - CCA annual output increase between 1 and 2 percent.
    - Non-GCC MENA countries and Pakistan annual output increase between 1 and 3 percent.
  - GCC: smaller average impact on exports and GDP given proximity to global frontier on trade restrictions, infrastructure quality, and regulatory environment.
- Scenario 2 — Policy impacts:
  - Reducing trade barriers:
    - Exports increase by more than 17 percent for CCA countries.
    - Exports increase by over 20 percent for non-GCC MENA countries and Pakistan.
    - Exports increase by 6 percent for GCC countries.
  - Upgrading infrastructure:
    - Exports increase by 22 percent for CCA countries.
    - Exports increase by 24 percent for non-GCC MENA countries and Pakistan.
    - Exports increase by 6 percent in the GCC.
  - Improving regulatory environment:
    - Exports boost by 3 percent (CCA), 11 percent (non-GCC MENA & Pakistan), and 6 percent (GCC).
  - Output gains from policy: range from 0.4 to 6.3 percent, especially for CCA and non-GCC MENA countries and Pakistan.
- Scenario 3 — Policy impacts (stronger shocks):
  - Reducing trade restrictions:
    - Exports rise by more than 11 percent in the CCA.
    - Exports rise by about 8 percent in the non-GCC MENA and Pakistan group—effectively eliminating baseline output losses.
  - Upgrading infrastructure and improving regulatory quality:
    - Both boost exports and output across the region, reversing adverse effects under the baseline Scenario 3.

### Conclusion (policy implications and summary)
- Overall synthesis:
  - Geoeconomic fragmentation affects trade patterns and economic outcomes in the CCA and MENA regions and Pakistan, with outcomes depending on scenario severity.
  - Scenario-specific summary:
    - Scenario 1: limited trade diversion yields unintended spillovers and modest export/output gains (exports up to about 2−3 percent under Scenario 2; output up to 0.4 percent).
    - Scenario 3: significant trade and output losses possible (CCA experiencing a 1.1 percent decline in exports; MENA group excluding GCC facing a 7.4 percent decrease).
  - Coordinated policies and neutrality in geopolitical tensions yield stronger and more lasting economic gains by fostering stability and trust.
- Recommended policy strategies:
  - Reduce trade barriers:
    - Potential export increases: 14 percent (CCA) and over 15 percent (non-GCC MENA) in Scenario 1; over 17 percent (CCA) and more than 20 percent (non-GCC MENA) in Scenario 2; over 11 percent (CCA) and around 8 percent (non-GCC MENA) in Scenario 3.
  - Ease regulatory constraints:
    - Potential export increases: 3−6 percent depending on region and scenario.
  - Enhance infrastructure investment:
    - Potential export increases: 7−8 percent (Scenario 1); 22 percent (CCA) and 24 percent (non-GCC MENA) in Scenario 2; meaningful gains in Scenario 3.
  - Expected output effects:
    - Scenario 1: annual output increases of 1−2 percent (CCA) and 1−3 percent (non-GCC MENA).
    - Scenario 2: output increases ranging from 0.4 percent to 6.3 percent for CCA and non-GCC MENA countries and Pakistan.
- Policy takeaway:
  - Agile, forward-looking measures—recalibrating trade policies, diversifying market outreach, and fortifying infrastructure frameworks—can help CCA, MENA, and Pakistan better address challenges from geoeconomic fragmentation.

*IMF staff calculations, “6. Results” from wpiea2025267-source-pdf*

### Appendix II

### Appendix II

### Partial Equilibrium: Trade Potential and Determinants of Trade
- Purpose: Estimate a theory-consistent structural gravity equation (simplified version of equation (8)) to identify baseline trade patterns and determinants before general equilibrium analysis of geoeconomic fragmentation.
- Estimation framework:
  - Structural gravity specification (equation (13)):
    - X_ij = exp[θ_i + ω_j + γ_ij + β_1 Z_ij] η_ij
    - Dependent variable X_ij: gross bilateral trade flows between exporter i and importer j (includes domestic trade when i = j).
    - Z_ij: vector of variables facilitating or restricting trade (trade restrictions, infrastructure, regulatory quality, etc.).
    - θ_i and ω_j: exporter and importer fixed effects (absorb country-level covariates such as GDP, population, landlocked status and represent theory-consistent multilateral trade cost controls).
    - γ_ij: exporter–importer fixed effects to account for trade imbalances and asymmetric trade costs.
    - β_1: vector of elasticities to be estimated.
    - η_ij: lognormally distributed error term, independent of regressors with constant variance σ_i^2.
- Estimation method and data:
  - Poisson Pseudo-Maximum Likelihood (PPML) estimator (Santos Silva and Tenreyro 2006).
  - Sample period: 2000−19.
  - Data: bilateral trade data from CEPII gravity dataset.
- Role of first-stage estimates:
  - Estimated elasticities for trade restrictions (tariffs and non-tariffs), infrastructure, and regulatory environment are used to calibrate the general equilibrium model.
  - With estimated importer and exporter fixed effects, inward and outward multilateral resistances (P_j and Π_i) are constructed to calculate baseline trade costs and other general equilibrium indexes.
- Baseline trade cost proxy (equation (14)):
  - [t̂_ij^(1−σ)]_BLN = exp[θ̂_i + ω̂_j + β̂_1 Z_ij]

### Gravity Models – First Stage Results (Detailed)
- Main finding: Evidence that trade restrictions (tariffs and nontariff barriers), infrastructure, and regulatory quality matter for boosting trade flows.
- Table A1: Gravity Models – First Stage Results Detailed (columns labeled (1) No Policies; (2) T & NTB; (3) Infrastructure; (4) Regulatory; (5) All Policies)
  - Coefficients (robust standard errors in parentheses). Significance: *** p<0.01, ** p<0.05, * p<0.1.
  - Exporter non_tariffs barriers:
    - Column (1): 0.0195** (0.00990)
    - Column (4): 0.00640 (0.0168)
  - Importer non_tariffs barriers:
    - Column (1): -0.0201** (0.00859)
    - Column (4): -0.000551 (0.0158)
  - Importer Tariffs:
    - Column (1): 0.00645 (0.00835)
    - Column (4): -0.0221 (0.0151)
  - Exporter Infrastructure:
    - Column (2): -0.0206 (0.0794)
    - Column (4): 0.402** (0.167)
  - Importer Infrastructure:
    - Column (2): 0.237*** (0.0732)
    - Column (4): -0.176 (0.174)
  - Exporter Regulatory:
    - Column (4): -0.0670 (0.0409)
    - Column (5): -0.214** (0.0929)
  - Importer Regulatory:
    - Column (4): 0.105*** (0.0358)
    - Column (5): 0.151 (0.0937)
  - Exporter Log gdp:
    - Column (1): 0.850*** (0.0180)
    - Column (2): 0.847*** (0.0202)
    - Column (3): 0.843*** (0.0288)
    - Column (4): 0.853*** (0.0198)
    - Column (5): 0.806*** (0.0269)
  - Importer Log gdp:
    - Column (1): 0.815*** (0.0214)
    - Column (2): 0.811*** (0.0195)
    - Column (3): 0.768*** (0.0321)
    - Column (4): 0.802*** (0.0221)
    - Column (5): 0.802*** (0.0403)
  - Log_distance:
    - Column (1): -0.753*** (0.0281)
    - Column (2): -0.768*** (0.0340)
    - Column (3): -0.704*** (0.0287)
    - Column (4): -0.737*** (0.0286)
    - Column (5): -0.692*** (0.0319)
  - Contiguity:
    - Column (1): 0.520*** (0.126)
    - Column (2): 0.521*** (0.140)
    - Column (3): 0.555*** (0.125)
    - Column (4): 0.538*** (0.124)
    - Column (5): 0.592*** (0.131)
  - Common language:
    - Column (1): 0.231** (0.0917)
    - Column (2): 0.211* (0.114)
    - Column (3): 0.237** (0.0944)
    - Column (4): 0.219** (0.0894)
    - Column (5): 0.214** (0.106)
  - Exporter landlocked:
    - Column (1): -0.130 (0.0882)
    - Column (2): -0.0895 (0.0929)
    - Column (3): -0.167* (0.0968)
    - Column (4): -0.129 (0.0885)
    - Column (5): -0.103 (0.0957)
  - Importer landlocked:
    - Column (1): -0.223*** (0.0838)
    - Column (2): -0.215** (0.0913)
    - Column (3): -0.298*** (0.0951)
    - Column (4): -0.247*** (0.0850)
    - Column (5): -0.279*** (0.0990)
  - Constant:
    - Column (1): -8.548*** (0.520)
    - Column (2): -8.385*** (0.445)
    - Column (3): -8.914*** (0.561)
    - Column (4): -8.662*** (0.468)
    - Column (5): -8.758*** (0.651)
  - Observations:
    - Column (1): 27,867
    - Column (2): 19,329
    - Column (3): 20,430
    - Column (4): 26,174
    - Column (5): 15,044
  - R-squared:
    - Column (1): 0.703
    - Column (2): 0.736
    - Column (3): 0.687
    - Column (4): 0.722
    - Column (5): 0.735

### General Equilibrium
- Extension of methodology:
  - Builds on Yotov and others (2016) structural gravity general equilibrium framework.
  - Extends single-policy-change analysis to two dimensions: fragmentation scenarios and corresponding policy responses.
- Economic channels and expected effects:
  - Geoeconomic fragmentation (formation of trade blocs) increases trade costs between opposing blocs, making exports less competitive and reducing export volumes and sectoral output.
  - Consequences include reduced market access, limited export diversification, increased vulnerability to market-specific shocks, reallocation of resources, adjustment costs, and temporary inefficiencies that reduce GDP and export capacity.
- General equilibrium algorithm steps:
  - Step 1: Solve the baseline gravity model using PPML to obtain point estimates and construct inward/outward multilateral resistances P_j and Π_i. Proxy bilateral trade cost using observable covariates (distance, borders, common language, bilateral tariffs, trade agreement, etc.) via equation (14) shown above. Estimate trade elasticity of substitution σ̂ and solve structural gravity system (equations 8–12) for baseline indexes including consumer prices and multilateral resistances.
  - Step 2: Define the counterfactual scenario:
    - Raise trade costs between countries in opposing geoeconomic blocs to capture fragmentation scenarios.
    - Simulate hypothetical policy actions: reduce the gap in trade restrictions, infrastructure, and regulatory quality between MENA and CCA countries and advanced economies by 20 percent. The 20 percent reduction is hypothetical and chosen because larger reductions would be costly and non-feasible for most countries in the region.
    - All counterfactual policy variables are in Z_ij; the adjustment delivers a new matrix of counterfactual bilateral trade costs:
      - [t̂_ij^(1−σ)]_CFL = exp[θ̂_i + ω̂_j + β̂_1 Z_ij_CFL]
  - Step 3: Solve the counterfactual model:
    - Use estimates from Steps 1 and 2 and trade elasticities to solve the structural gravity system in the counterfactual scenario.
    - Obtain counterfactual indexes (exports and output) in the “conditional” (Equations 8–10) and in the “full endowment” general equilibrium (Equations 8–12) assumptions.
    - Full employment general equilibrium reactions reflect alterations in factory-gate prices via shifts in outward multilateral resistances affecting output and expenditures and indirectly multilateral resistances.
  - Step 4: Construct indexes of interest (percentage changes relative to baseline):
    - %∆X̂_it = (X̂_it_CFL − X̂_it_BLN) / X̂_it_BLN × 100
    - %∆GDP̂_it = (GDP̂_it_CFL − GDP̂_it_BLN) / GDP̂_it_BLN × 100
- Interpretation:
  - Changes in exports and GDP are reported relative to the baseline without policy action.
  - Output losses are typically smaller than export losses because countries substitute exports with increased domestic consumption of domestic production, which also reduces import demand.

### Annex III: Trade Interventions (Descriptions)
- Export Interventions:
  - Export Bans: Government-imposed prohibitions on the sale of certain goods to foreign markets.
  - Export Quotas: Limits on the quantity of a specific good that can be exported during a given time period.
  - Export Licensing requirements: Regulations requiring exporters to obtain government authorization before shipping certain goods abroad.
  - Export Subsidies: Financial support provided by governments to domestic producers to encourage exports, such as direct payments, tax relief, or subsidized credit.
- Import Interventions:
  - Tariffs: Taxes imposed on imported goods, aimed at making foreign products more expensive to protect domestic industries or generate revenue.
  - Subsidies: Government financial assistance to local producers, which can indirectly affect trade by making domestic goods more competitive against imports.
  - Other import restrictions: Non-tariff barriers such as import quotas, technical standards, sanitary regulations, or customs procedures that limit or complicate the entry of foreign goods.

*Source: Appendix II, "Partial Equilibrium: Trade Potential and Determinants of Trade" and accompanying tables and annexes, from the IMF Working Paper Unlocking MENA and CCA Trade in a Fragmented World (Working Paper No. WP/2025/267).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025267-source-pdf.pdf_