## wpiea2023201-print-pdf - Section 3 provides key stylized facts on the production and trade of commodities. Section 4 ## Source details **Canonical URL:** [wpiea2023201-print-pdf - Section 3 provides key stylized facts on the production and trade of commodities. Section 4](https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023201-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2023/english/wpiea2023201-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2023/english/wpiea2023201-print-pdf.pdf.json) --- ### New dataset: scope and construction - Assembled a new annual dataset on bilateral trade flows and production data at the country and commodity level for 48 energy, mineral, and agricultural commodities (2019 data). - Key data sources: - Agricultural commodities: Food and Agriculture Organization (FAO). - Energy: International Energy Agency (IEA). - Minerals production: British Geological Survey (BGS) and US Geological Survey (USGS). - Trade: Bilateral Commodity Trade Database (BACI), based on UN Comtrade. - Innovations in dataset construction: - Developed adjustment factors to convert different unit measurements into equivalent metric tons of metal content (see Appendix Table A.1). Adjustment factors can be commodity- and country-specific. - Included both mined upstream commodities (e.g., copper ore) and refined commodities (e.g., refined copper). Refined commodities’ production and trade include recycled materials with the exception of aluminium. - Constructed concordances between HS codes and production-commodity definitions based on Fally and Sayre (2018), Bolhuis et al. (2023), consultations with BGS, and commodity-specific literature. For agricultural and energy commodities, relied on FAO and IEA concordances. ### Stylized facts on commodity markets (key empirical findings) - Factors that raise economic costs of trade disruption: production concentration, low elasticities of supply and demand, high share of production that is traded, and high trade dependencies of importers. - Concentration of production: - The top three producers account on average for: - about 65 percent of global output of agricultural commodities, - about 50 percent of energy commodities, - about 75 percent of minerals commodities. - Minerals’ production concentration occurs both at mining and often at processing stages (e.g., a few countries, notably China, dominate processing). - Low elasticities of supply and demand: - Commodities exhibit low price elasticities, particularly in the short run (see Figure B.2). - Example: average time from exploration to opening of copper mines is 16 years (IEA, 2022), illustrating long lags on supply response. - Demand is often hard to substitute because commodities are inputs for key technologies and essential household consumption. - Trade dependence and import concentration: - Share of production traded internationally (2019): - agricultural commodities: around 40 percent of output is traded, - energy commodities: around 30 percent, - mineral commodities: almost 50 percent. - For individual commodities, traded shares can be substantially higher; for example, more than 80 percent of lithium or potash produced crosses borders. - Most countries have limited import diversification: more than 60 percent of countries rely on less than three suppliers for key minerals such as cobalt, silver or nickel. - Historical trend: the share of primary goods in total goods trade declined from roughly 45 percent in the first half of the 20th century to about 13 percent from 2019 to 2021. ### Multi-country partial equilibrium single-commodity model (structure and mechanism) - Country-level supply and demand (in logs): - ln(q^s_c) = η^s ln(p_c) + γ^s_c - ln(q^d_c) = η^d ln(p_c) + γ^d_c - All countries assumed to share the same elasticities η^s (>0) and η^d (<0) but have distinct shifters γ^s_c and γ^d_c. - Two-bloc aggregation: blocs B ∈ {H, ROW} yield bloc-level relations: - ln(Q^s_B) = η^s ln(p_B) + γ^s_B - ln(Q^d_B) = η^d ln(p_B) + γ^d_B - γ^s_B and γ^d_B defined as ln of sums of exponentiated country shifters. - Equilibria: - Integrated market equilibrium: market clearing Q^s_H + Q^s_ROW = Q^d_H + Q^d_ROW and p_H = p_ROW = p_w. World price: - ln(p_w) = (Ω_d − Ω_s) / (η^s − η^d), where Ω_d ≡ ln(e^{γ^d_H} + e^{γ^d_ROW}) and Ω_s ≡ ln(e^{γ^s_H} + e^{γ^s_ROW}). - Fragmented market equilibrium: blocs fully segmented, bloc-level market clearing. Bloc price: - ln(p_B) = (γ^d_B − γ^s_B) / (η^s − η^d). - Price impact of fragmentation (with integrated world price standardized to 1 and Ω_d − Ω_s = 0): - ln(p_B) − ln(p_w) = (γ^d_B − γ^s_B) / (η^s − η^d). - Interpretation: price changes depend on (i) initial bloc-level trade imbalance (γ^d_B − γ^s_B) and (ii) supply and demand elasticities (η^s, η^d). Initially exporting blocs see price drops; initially importing blocs see price rises. More inelastic supply/demand amplifies price responses. - Welfare (surplus) changes: - Country-level changes in consumer and producer surplus are derived analytically: - ∆CS_c = −p_w q^d_{c,w} [ (p_c / p_w)^{1+η^d} − 1 ] / (1+η^d) - ∆PS_c = p_w q^s_{c,w} [ (p_c / p_w)^{1+η^s} − 1 ] / (1+η^s) - Bloc-level total surplus necessarily falls with fragmentation; country-specific surplus may increase for exporters in ex-ante importing blocs or importers in ex-ante exporting blocs. ### Calibration and baseline fragmentation scenario - Baseline bloc configuration: - Two hypothetical blocs based on the March 2nd, 2022 UN vote on Russia’s war in Ukraine: - “US-Europe+ bloc” (contains the US and most European economies). - “China-Russia+ bloc” (contains China and Russia). - Baseline fragmentation scenario: full prohibition of commodity trade between blocs; intra-bloc trade costs unchanged. - Parameter calibration: - Standardized integrated market price p_w = 1. - γ^d_c and γ^s_c calibrated to match log of initial quantity demanded and supplied (quantities in metric tons content). Quantity demanded = production − net exports (volumes). - Model matches observed country and bloc-level trade flows for 2019 (pre-COVID-19 pandemic). Note: crude oil and zirconium calibrations use 2018 data due to data quality. - Elasticities (η^d, η^s): - For energy and agricultural commodities: average of the minimum and maximum short-run price elasticities in Fally and Sayre (2018). - For minerals: median of short-run elasticities in Dahl (2020). - If no commodity-specific estimate available, used the average elasticity for that commodity type. - Robustness exercises with alternative elasticities discussed in Section 9.1. ### Simulation results: fragmentation and commodity prices (high-level findings) - Price changes are proportional to bloc-level demand-supply imbalances induced by blocked cross-bloc trade. - In the baseline two-bloc simulation: - Minerals are most vulnerable to fragmentation due to highly concentrated and imbalanced production across blocs. - China-Russia+ bloc would see significant price rises for mining-stage minerals, notably key energy-transition minerals such as cobalt, lithium, copper and nickel. - US-Europe+ bloc would experience notable increases in prices of refined minerals including magnesium, platinum, palladium and aluminium, driven in part by processing concentration in China, South Africa and Russia. - Figures referenced in the source (Figure 6, Figures 7a and 7b) present distributions and commodity-by-commodity bloc-level simulated price changes (not reproduced here). ### Commodity price effects from fragmentation (additional notes) - Price effects are capped at 500 percent for readability in Figure 6. - Energy (crude oil, natural gas, and coal) and most agricultural commodities (e.g., wheat, cotton, rice, maize) show more muted price effects because of relative self-sufficiency of each bloc. - Important exceptions: palm oil and soybeans could experience large price increases in the China-Russia+ bloc. - Around 80 percent of the production of these broadly consumed commodities is concentrated in up to three countries (Indonesia and Malaysia for palm oil, the United States, Brazil and Argentina for soybeans), which are all part of the hypothetical US-Europe+ bloc in the baseline. - Figure 7 bars are capped at 150 percent for exposition when showing bloc-level commodity price changes due to fragmenting commodity trade. ### Fragmentation and commodity price volatility — channels and illustrative magnitudes - Two model channels increasing volatility: - Smaller market sizes make bloc-level prices more responsive to country-level shocks. - Countries switching blocs introduce additional supply and demand shocks. - Elasticity of the price of a commodity to country-level supply shocks given by an expression referenced in the text (∂lnp / ∂γs_c = − 1/(ηs − ηd) e^{γs_c} P_c e^{γs_c}). - Illustrative example (Figure 8): a three-standard-deviation negative shock to US wheat production has double the impact on wheat prices when the market is fragmented into two smaller blocs versus an integrated market. - The US accounts for about 7 percent of global and 15 percent of US-Europe+ bloc wheat production. - A three-standard-deviation harvest shock corresponds to about 60 percent of US wheat production, or 4 percent of global output, holding wheat prices constant. - The exercise uses a price elasticity of supply of 0.2 and a relatively high price elasticity of demand of -0.85. Lower elasticities would lead to higher prices impacts, while fragmentation would still double the price impact. ### Countries switching blocs — maximum impacts and probabilities - From the fragmented equilibrium, the change in prices from one country leaving (joining) the bloc is given by: - ln(p_B') − ln(p_B) = (γ_d_B' − γ_s_B')/(η_s − η_d) − (γ_d_B − γ_s_B)/(η_s − η_d). - Price changes in Figure 9 are capped at 800 percent for readability. - Example: South Africa produces one-third of the world’s manganese. If South Africa switched to the hypothetical US-Europe+ bloc, the price of manganese in the China-Russia+ bloc could rise more than 800 percent. - Switching probabilities: - Defined as P(switch_c) = |b_c − p̂_c| where b_c is the current bloc dummy and p̂_c is the model-implied probability from a logit. - Economic distance: E_jc = 1 − (s_jx,c + s_ji,c)/2 where s_jx,c is share of exports from c to j and s_ji,c is share of imports by c from j. - Military distance: M_jc = 1 − σ_jc where σ_jc is similarity score of military alliances; military distance normalized so cross-country standard deviation = 1. - Estimated coefficients (Logit, data from 2018) indicate military distance is a better predictor of bloc positioning than economic distance. - Figure 11 shows top 15 expected price increases from exporting countries switching, weighted by switching probabilities; top vulnerable commodities remain broadly unchanged relative to unweighted maxima. ### Fragmentation and economic surplus - Changes in consumer, producer and total surplus measure macro-relevance of each commodity; they account for price changes and commodity importance in consumption and production. - Key findings: - Restricting free trade results in losses in bloc-level surplus; global economy is worse off from fragmentation of trade in individual commodities. This finding is robust to alternative bloc definitions. - Global economic losses, as captured in surplus changes, are small in magnitude. - Bloc- and country-level changes in total surplus are generally small as a share of Gross National Expenditure (GNE) with notable exceptions. - Appendix Figure B.8b shows fragmentation in palm oil and copper markets could cause declines in surplus of over 1 percent of GNE in the China-Russia+ bloc. - Two reasons for mostly moderate effects: - Changes in total surplus reflect offsetting movements in consumer and producer surplus within countries. - Many price-sensitive commodities are limited in their share of consumption or production. - Energy commodities are not particularly price-vulnerable under baseline bloc configuration, but associated declines in surplus are more significant because energy commodities are widely consumed and produced; even small price changes matter. - Minerals' trade is price-sensitive but often has subdued surplus changes due to limited relevance in aggregate consumption/production. - Heterogeneity across countries: - Bloc-level aggregation masks sizable country-level effects; some countries experience substantial surplus gains or losses. - Example: fragmentation of copper ores and concentrates would reduce surplus by 2.5-5 percent of GNE in Peru and Chile (largest copper exporters in US-Europe+ bloc) where copper prices would fall; simultaneously, fragmentation would lead to large surplus gains in Mongolia and Kazakhstan who would scale up production and export at higher prices in the copper-scarce China-Russia+ bloc. - Figure 13 distributions separate: - (i) net-commodity-exporting countries in a commodity-importing bloc and net-commodity-importing countries in a commodity-exporting bloc [blue], - (ii) net-commodity-exporting countries in commodity-exporting blocs, and net-commodity-importing countries in commodity-importing blocs [red]. - Each commodity-country combination plotted as share of GNE. ### Robustness analysis - Baseline simulation results are robust to alternative price elasticities of supply and demand. - First alternative: demand and supply elasticities set to median values among estimates in Fally and Sayre (2018); ranking of commodity price vulnerabilities similar to baseline (Figure C.10). - Five largest surplus changes across blocs (Figure C.11) qualitatively similar to baseline. - Second alternative: assume all agricultural, mineral and energy commodities have the same elasticity within their category (Table C.8); results (Figures C.12 and C.13) align with baseline. - Precise bloc configuration matters for results in highly concentrated markets; assignment of major producers across blocs can materially change outcomes. ### Alternative Blocs Configurations (summarized outcomes) - Bloc Configuration I (all emerging market and developing economies, excluding India, Indonesia and Latin American countries, are assigned to the China-Russia+ bloc): - More commodities experience price increases in the US-Europe+ bloc than under the baseline. - Crude oil and cocoa drive the largest surplus declines in the US-Europe+ bloc, implying surplus losses between 2.5 and 4.5 percent of GNE. - Crude oil would cause substantial surplus declines in the China-Russia bloc as well (over 1 percent of GNE). - Palm oil and manganese vulnerabilities shift: China-Russia+ bloc would experience milder price increases for these commodities. - Bloc Configuration II (assignment by whether a country trades more with US+EU than with China+Russia): - US-Europe+ bloc would experience large price increases in palm oil due to Indonesia and Malaysia shifting. - US-Europe+ bloc would be less vulnerable to fragmentation of graphite, platinum, and palladium because Mozambique and South Africa would be in US-Europe+ bloc. - China-Russia+ bloc would still experience large price increases of soybean, copper, manganese, zinc, and lead, but not of iron ore and lithium (Australia assigned to China-Russia+ bloc). - Changes in total economic surplus remain larger in China-Russia+ bloc, but lower in magnitude relative to the baseline. - Key takeaway: bloc assignment rules materially affect which commodities face the largest price shocks and which bloc bears the largest surplus losses; crude oil, cocoa, and palm oil frequently emerge as major drivers. ### Selected data and parameter exact values (examples preserved from appendices) - Appendix A — adjustment factors (Table A.1) (exact values): - Antimony: 0.325 - Cobalt: 0.07 - Copper: 0.3 - Graphite: 0.913 - Lead: 0.6 - Nickel: 0.015 - Rare Earths: 0.6 - Silver: 0.017 - Tin: 0.73 - Titanium: 0.5266 - Tungsten: 0.561 - Zinc: 0.65 - Appendix A — agricultural FAO mapping (Table A.2) (selected entries): - Cocoa — FAO Item Code 661 - Coffee — FAO Item Code 656 - Cotton — FAO Item Code 767 - Maize — FAO Item Code 56 - Palm oil — FAO Item Code 257 - Rice — FAO Item Code 31 - Soy beans — FAO Item Code 236 - Wheat — FAO Item Code 15 - Appendix A — sample elasticities (Table A.6) (selected exact entries): - Cocoa: Demand -0.075, Supply 0.075 - Coffee: Demand -0.305, Supply 0.285 - Cotton: Demand -0.684, Supply 0.497 - Maize: Demand -0.34, Supply 0.327 - Rice: Demand -0.24, Supply 0.167 - Aluminium (refined): Demand -0.047, Supply 0.235 - Chromium: Demand -2.622, Supply 0.265625 - Lithium: Demand -0.54, Supply 0.265625 - Nickel: Demand -0.03, Supply 0.75 - Natural gas: Demand -0.5015, Supply 0.075 - Crude oil: Demand -0.0415, Supply 0.1445 - Coal: Demand -0.5, Supply 0.0565 - Appendix C — alternative constant elasticities (Table C.8) (exact values): - Agriculture: Demand -0.376, Supply 0.259 - Energy: Demand -0.348, Supply 0.092 - Minerals: Demand -0.271, Supply 0.266 *Source: wpiea2023201-print-pdf - Section 3 provides key stylized facts on the production and trade of commodities. Section 4; canonical PDF: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023201-print-pdf.pdf* ### Section 3 provides key stylized facts on the production and trade of commodities. Section 4 ### wpiea2023201-print-pdf - Section 3 provides key stylized facts on the production and trade of commodities. Section 4 ### New dataset: scope and construction - Assembled a new annual dataset on bilateral trade flows and production data at the country and commodity level for 48 energy, mineral, and agricultural commodities (2019 data). - Key data sources: - Agricultural commodities: Food and Agriculture Organization (FAO). - Energy: International Energy Agency (IEA). - Minerals production: British Geological Survey (BGS) and US Geological Survey (USGS). - Trade: Bilateral Commodity Trade Database (BACI), based on UN Comtrade. - Innovations in dataset construction: - Developed adjustment factors to convert different unit measurements into equivalent metric tons of metal content (see Appendix Table A.1). Adjustment factors can be commodity- and country-specific. - Included both mined upstream commodities (e.g., copper ore) and refined commodities (e.g., refined copper). Refined commodities’ production and trade include recycled materials with the exception of aluminium. - Constructed concordances between HS codes and production-commodity definitions based on Fally and Sayre (2018), Bolhuis et al. (2023), consultations with BGS, and commodity-specific literature. For agricultural and energy commodities, relied on FAO and IEA concordances. ### Stylized facts on commodity markets (key empirical findings) - Factors that raise economic costs of trade disruption: production concentration, low elasticities of supply and demand, high share of production that is traded, and high trade dependencies of importers. - Concentration of production: - The top three producers account on average for: - about 65 percent of global output of agricultural commodities, - about 50 percent of energy commodities, - about 75 percent of minerals commodities. - Minerals’ production concentration occurs both at mining and often at processing stages (e.g., a few countries, notably China, dominate processing). - Low elasticities of supply and demand: - Commodities exhibit low price elasticities, particularly in the short run (see Figure B.2). - Example: average time from exploration to opening of copper mines is 16 years (IEA, 2022), illustrating long lags on supply response. - Demand is often hard to substitute because commodities are inputs for key technologies and essential household consumption. - Trade dependence and import concentration: - Share of production traded internationally (2019): - agricultural commodities: around 40 percent of output is traded, - energy commodities: around 30 percent, - mineral commodities: almost 50 percent. - For individual commodities, traded shares can be substantially higher; for example, more than 80 percent of lithium or potash produced crosses borders. - Most countries have limited import diversification: more than 60 percent of countries rely on less than three suppliers for key minerals such as cobalt, silver or nickel. - Historical trend: the share of primary goods in total goods trade declined from roughly 45 percent in the first half of the 20th century to about 13 percent from 2019 to 2021. ### Multi-country partial equilibrium single-commodity model (structure and mechanism) - Country-level supply and demand (in logs): - ln(q^s_c) = η^s ln(p_c) + γ^s_c - ln(q^d_c) = η^d ln(p_c) + γ^d_c - All countries assumed to share the same elasticities η^s (>0) and η^d (<0) but have distinct shifters γ^s_c and γ^d_c. - Two-bloc aggregation: blocs B ∈ {H, ROW} yield bloc-level relations: - ln(Q^s_B) = η^s ln(p_B) + γ^s_B - ln(Q^d_B) = η^d ln(p_B) + γ^d_B - γ^s_B and γ^d_B defined as ln of sums of exponentiated country shifters. - Equilibria: - Integrated market equilibrium: market clearing Q^s_H + Q^s_ROW = Q^d_H + Q^d_ROW and p_H = p_ROW = p_w. World price: - ln(p_w) = (Ω_d − Ω_s) / (η^s − η^d), where Ω_d ≡ ln(e^{γ^d_H} + e^{γ^d_ROW}) and Ω_s ≡ ln(e^{γ^s_H} + e^{γ^s_ROW}). - Fragmented market equilibrium: blocs fully segmented, bloc-level market clearing. Bloc price: - ln(p_B) = (γ^d_B − γ^s_B) / (η^s − η^d). - Price impact of fragmentation (with integrated world price standardized to 1 and Ω_d − Ω_s = 0): - ln(p_B) − ln(p_w) = (γ^d_B − γ^s_B) / (η^s − η^d). - Interpretation: price changes depend on (i) initial bloc-level trade imbalance (γ^d_B − γ^s_B) and (ii) supply and demand elasticities (η^s, η^d). Initially exporting blocs see price drops; initially importing blocs see price rises. More inelastic supply/demand amplifies price responses. - Welfare (surplus) changes: - Country-level changes in consumer and producer surplus are derived analytically: - ∆CS_c = −p_w q^d_{c,w} [ (p_c / p_w)^{1+η^d} − 1 ] / (1+η^d) - ∆PS_c = p_w q^s_{c,w} [ (p_c / p_w)^{1+η^s} − 1 ] / (1+η^s) - Bloc-level total surplus necessarily falls with fragmentation; country-specific surplus may increase for exporters in ex-ante importing blocs or importers in ex-ante exporting blocs. ### Calibration and baseline fragmentation scenario - Baseline bloc configuration: - Two hypothetical blocs based on the March 2nd, 2022 UN vote on Russia’s war in Ukraine: - “US-Europe+ bloc” (contains the US and most European economies). - “China-Russia+ bloc” (contains China and Russia). - Baseline fragmentation scenario: full prohibition of commodity trade between blocs; intra-bloc trade costs unchanged. - Parameter calibration: - Standardized integrated market price p_w = 1. - γ^d_c and γ^s_c calibrated to match log of initial quantity demanded and supplied (quantities in metric tons content). Quantity demanded = production − net exports (volumes). - Model matches observed country and bloc-level trade flows for 2019 (pre-COVID-19 pandemic). Note: crude oil and zirconium calibrations use 2018 data due to data quality. - Elasticities (η^d, η^s): - For energy and agricultural commodities: average of the minimum and maximum short-run price elasticities in Fally and Sayre (2018). - For minerals: median of short-run elasticities in Dahl (2020). - If no commodity-specific estimate available, used the average elasticity for that commodity type. - Robustness exercises with alternative elasticities discussed in Section 9.1. ### Simulation results: fragmentation and commodity prices (high-level findings) - Price changes are proportional to bloc-level demand-supply imbalances induced by blocked cross-bloc trade. - In the baseline two-bloc simulation: - Minerals are most vulnerable to fragmentation due to highly concentrated and imbalanced production across blocs. - China-Russia+ bloc would see significant price rises for mining-stage minerals, notably key energy-transition minerals such as cobalt, lithium, copper and nickel. - US-Europe+ bloc would experience notable increases in prices of refined minerals including magnesium, platinum, palladium and aluminium, driven in part by processing concentration in China, South Africa and Russia. - Figures referenced in the source (Figure 6, Figures 7a and 7b) present distributions and commodity-by-commodity bloc-level simulated price changes (not reproduced here). *Source: wpiea2023201-print-pdf - Section 3 provides key stylized facts on the production and trade of commodities. Section 4; canonical PDF: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023201-print-pdf.pdf* ### Appendix Figure B.6 maps net trade flows across blocs for some of the commodities that are most ### Appendix Figure B.6 maps net trade flows across blocs for some of the commodities that are most vulnerable to the event of fragmentation in the baseline simulation ### Commodity price effects from fragmentation - Price effects are capped at 500 percent for readability in Figure 6. - Energy (crude oil, natural gas, and coal) and most agricultural commodities (e.g., wheat, cotton, rice, maize) show more muted price effects because of relative self-sufficiency of each bloc. - Important exceptions: palm oil and soybeans could experience large price increases in the China-Russia+ bloc. - Around 80 percent of the production of these broadly consumed commodities is concentrated in up to three countries (Indonesia and Malaysia for palm oil, the United States, Brazil and Argentina for soybeans), which are all part of the hypothetical US-Europe+ bloc in the baseline. - Figure 7 bars are capped at 150 percent for exposition when showing bloc-level commodity price changes due to fragmenting commodity trade. ### Fragmentation and commodity price volatility — channels and illustrative magnitudes - Two model channels increasing volatility: - Smaller market sizes make bloc-level prices more responsive to country-level shocks. - Countries switching blocs introduce additional supply and demand shocks. - The elasticity of the price of a commodity to country-level supply shocks is given by: ∂lnp / ∂γs_c = − 1/(ηs − ηd) e^{γs_c} P_c e^{γs_c} (expression referenced in text). - Illustrative example (Figure 8): a three-standard-deviation negative shock to US wheat production has double the impact on wheat prices when the market is fragmented into two smaller blocs versus an integrated market. - The US accounts for about 7 percent of global and 15 percent of US-Europe+ bloc wheat production. - A three-standard-deviation harvest shock corresponds to about 60 percent of US wheat production, or 4 percent of global output, holding wheat prices constant. - The exercise uses a price elasticity of supply of 0.2 and a relatively high price elasticity of demand of -0.85. Lower elasticities would lead to higher prices impacts, while fragmentation would still double the price impact. ### Countries switching blocs — maximum impacts and probabilities - From the fragmented equilibrium, the change in prices from one country leaving (joining) the bloc is given by: ln(p_B') − ln(p_B) = (γ_d_B' − γ_s_B')/(η_s − η_d) − (γ_d_B − γ_s_B)/(η_s − η_d) (equation referenced). - Price changes in Figure 9 are capped at 800 percent for readability. - Example: South Africa produces one-third of the world’s manganese. If South Africa switched to the hypothetical US-Europe+ bloc, the price of manganese in the China-Russia+ bloc could rise more than 800 percent. - Not all countries are equally likely to switch blocs. The probability of switching is defined as P(switch_c) = |b_c − p̂_c| where b_c is the current bloc dummy and p̂_c is the model-implied probability from a logit. - Economic distance measure: E_jc = 1 − (s_jx,c + s_ji,c)/2 where s_jx,c is share of exports from c to j and s_ji,c is share of imports by c from j. - Military distance measure: M_jc = 1 − σ_jc where σ_jc is similarity score of military alliances; military distance normalized so cross-country standard deviation = 1. - Estimated coefficients (Logit, data from 2018) indicate military distance is a better predictor of bloc positioning than economic distance. - Logit model reported coefficients and fit: NR 156, R^2 0.22 (coefficients and standard errors listed in table; statistical significance indicated). - Figure 11 shows top 15 expected price increases from exporting countries switching, weighted by switching probabilities; top vulnerable commodities remain broadly unchanged relative to unweighted maxima. ### Fragmentation and economic surplus - Changes in consumer, producer and total surplus measure macro-relevance of each commodity; they account for price changes and commodity importance in consumption and production. - Key findings: - Restricting free trade results in losses in bloc-level surplus; global economy is worse off from fragmentation of trade in individual commodities. This finding is robust to alternative bloc definitions. - Global economic losses, as captured in surplus changes, are small in magnitude. - Bloc- and country-level changes in total surplus are generally small as a share of Gross National Expenditure (GNE) with notable exceptions. - Appendix Figure B.8b shows fragmentation in palm oil and copper markets could cause declines in surplus of over 1 percent of GNE in the China-Russia+ bloc. - Two reasons for mostly moderate effects: - Changes in total surplus reflect offsetting movements in consumer and producer surplus within countries. - Many price-sensitive commodities are limited in their share of consumption or production. - Energy commodities are not particularly price-vulnerable under baseline bloc configuration, but associated declines in surplus are more significant because energy commodities are widely consumed and produced; even small price changes matter. - Minerals' trade is price-sensitive but often has subdued surplus changes due to limited relevance in aggregate consumption/production. - Heterogeneity across countries: - Bloc-level aggregation masks sizable country-level effects; some countries experience substantial surplus gains or losses. - Example (Figure 12b): fragmentation of copper ores and concentrates would reduce surplus by 2.5-5 percent of GNE in Peru and Chile (largest copper exporters in US-Europe+ bloc) where copper prices would fall; simultaneously, fragmentation would lead to large surplus gains in Mongolia and Kazakhstan who would scale up production and export at higher prices in the copper-scarce China-Russia+ bloc. - Figure 13 distributions separate: - (i) net-commodity-exporting countries in a commodity-importing bloc and net-commodity-importing countries in a commodity-exporting bloc [blue], - (ii) net-commodity-exporting countries in commodity-exporting blocs, and net-commodity-importing countries in commodity-importing blocs [red]. - Each commodity-country combination plotted as share of GNE. ### Robustness analysis - Baseline simulation results are robust to alternative price elasticities of supply and demand. - First alternative: demand and supply elasticities set to median values among estimates in Fally and Sayre (2018); ranking of commodity price vulnerabilities similar to baseline (Figure C.10). - Five largest surplus changes across blocs (Figure C.11) qualitatively similar to baseline. - Second alternative: assume all agricultural, mineral and energy commodities have the same elasticity within their category (Table C.8); results (Figures C.12 and C.13) align with baseline. - Precise bloc configuration matters for results in highly concentrated markets; assignment of major producers across blocs can materially change outcomes. *Source: Appendix excerpts, wpiea2023201-print-pdf* ### 9.2 Alternative Blocs Configurations ### 9.2 Alternative Blocs Configurations ### 9.2.1 Bloc Configuration I - Definition: - All emerging market and developing economies, excluding India, Indonesia and Latin American countries, are assigned to the China-Russia+ bloc (as listed in Table C.9 in the Appendix). - Price implications: - More commodities experience price increases in the hypothetical US-Europe+ bloc than under the baseline (see Figure C.14 in the Appendix). - Crude oil: price would increase by more in the US-Europe+ bloc than in the baseline due major oil producers being in the China-Russia+ bloc (United Arab Emirates, Libya, Nigeria, Qatar, Saudi Arabia, Kuwait). - Cocoa: price would increase in the US-Europe+ bloc because Ivory Coast, the largest world producer of cocoa, would become part of the China-Russia+ bloc. - Cobalt: price would rise in the US-Europe+ bloc because the Democratic Republic of Congo, the world largest producer of cobalt, would be in the China-Russia bloc. - Palm oil and manganese: China-Russia bloc would experience milder price increases for these commodities. - Palm oil: important producers such as Malaysia and Thailand are assigned to the China-Russia+ bloc. - Manganese: would become less vulnerable in the China-Russia+ bloc because India, a major importer of this commodity, is now assigned to the US-Europe+ bloc. - Total surplus implications: - Crude oil and cocoa are the commodities causing the largest surplus declines in the US-Europe+ bloc (see Figure C.15 in the Appendix). - They imply surplus losses in the US-Europe+ bloc between 2.5 and 4.5 percent of GNE. - Crude oil would cause substantial surplus declines in the China-Russia bloc as well (over 1 percent of GNE), driven by producers surplus declining due to large reduction in prices for exporting countries in this bloc. ### 9.2.2 Bloc Configuration II - Definition: - A country is assigned to the US-Europe+ bloc if it trades more with the US and the EU combined than with China and Russia combined. Otherwise it is assigned to the China-Russia+ bloc. - Price implications: - US-Europe+ bloc would experience large price increases in palm oil due to the shift of Indonesia and Malaysia, which account for 80 percent of global production, and due to the shift of the DRC to the China-Russia+ bloc. - US-Europe+ bloc would be less vulnerable to trade fragmentation of graphite, platinum, and palladium because Mozambique and South Africa would be in the US-Europe+ bloc. - China-Russia+ bloc would still experience large price increases of soybean, copper, manganese, zinc, and lead, but not of iron ore and lithium, as Australia would be assigned to the China-Russia+ bloc (see Figure C.16 in the Appendix). - Total surplus implications: - Changes in total economic surplus remain larger in the China-Russia+ bloc, but lower in magnitude relative to the baseline (see Figure C.17 in the Appendix). ### Key takeaways across alternative configurations - Bloc assignment rules materially affect which commodities face the largest price shocks and which bloc bears the largest surplus losses. - Crude oil, cocoa, and palm oil emerge as major drivers of bloc-level surplus changes under different configurations. - Commodity market vulnerabilities depend on producer concentration, major importers’ bloc membership, and reallocation of key producers (e.g., Indonesia, Malaysia, Ivory Coast, DRC, Australia). *Source: wpiea2023201-print-pdf - 9.2 Alternative Blocs Configurations* ### References ### wpiea2023201-print-pdf - References ### Key bibliographic sources - Contains an extensive reference list including, among others: - Aiyar, S., Chen, J., Ebeke, C., Garcia-Saltos, R., Gudmundsson, T., Ilyina, A., Kangur, A., Kunaratskul, T., Rodriguez, S., Ruta, M., Schulze, T., Soderberg, G., and Trevino, J. (2023a). Geoeconomic fragmentation and the future of multilateralism. IMF Staff Discussion Note, 23/0001. - Bolhuis, M., Chen, J., and Kett, B. (2023). Fragmentation in global trade: Accounting for commodities. IMF Working Paper, 2023-073. - Dahl, C. A. (2020). Dahl mineral elasticity of demand and supply database (MEDS). Working papers, Colorado School of Mines, Division of Economics and Business. - Fally, T. and Sayre, J. (2018). Commodity trade matters. NBER Working Papers 24965. - IMF (2023a). Geoeconomic fragmentation and foreign direct investment. World Economic Outlook, April 2023, Chapter 4. - IEA (2023). Critical minerals market review 2023. Report, International Energy Agency. - Full reference list spans foundational papers on commodity price dynamics, supply-chain disruptions, critical minerals, and geoeconomic fragmentation. ### Appendix A — Data: adjustment factors and mappings - Table A.1: Adjustment Factors Used to Convert Gross Quantities Trade Data for Minerals into Metal Content - Antimony: 0.325 - Cobalt: 0.07 - Copper: 0.3 - Graphite: 0.913 - Lead: 0.6 - Nickel: 0.015 - Rare Earths: 0.6 - Silver: 0.017 - Tin: 0.73 - Titanium: 0.5266 - Tungsten: 0.561 - Zinc: 0.65 - Note: "There are country specific adjustment factors." and "Country- and commodity-specific adjustment factors are available from the authors upon request." - Table A.2: Mapping Table for the Trade and Production of Agricultural Commodities (FAO codes) - Cocoa — FAO Item Code 661 - Coffee — FAO Item Code 656 - Cotton — FAO Item Code 767 - Maize — FAO Item Code 56 - Palm oil — FAO Item Code 257 - Rice — FAO Item Code 31 - Rubber — FAO Item Code 836 - Soy beans — FAO Item Code 236 - Sugar — FAO Item Code 162 - Sunflower seeds — FAO Item Code 267 - Tobacco — FAO Item Code 826 - Wheat — FAO Item Code 15 - Source note: "Data is obtained from the FAO crops and livestock products dataset, except for rice and sugar, which is obtained from the FAO supply utilization accounts dataset." - Table A.3: Mapping Table for Mineral Commodities - Provides HS Code ↔ Production Name mappings for a long list of minerals (examples): - Aluminium (refined) — HS 760110 / Primary aluminium - Bauxite — HS 260600 / Bauxite - Copper (refined) — HS 740311 etc. / Refined copper - Lithium — HS 283691 / Lithium minerals - Nickel (refined) — HS 750210 / Nickel (smelter/refinery) - Zinc (refined) — HS 790111 / Slab zinc - Sources: "Production data are obtained from the British General Survey. For Titanium, Silicon, and Potash, we rely on production data from the U.S. Geological Survey. Trade data are from Bilateral Commodity Trade Database (BACI), which draws on UN Comtrade." - Table A.4: Mapping Table for Energy Commodities (IEA ↔ HS codes) - Coal: Anthracite (HS 270111) → IEA Anthracite (ANTCOAL); Bituminous coal (HS 270112) → IEA Otherbituminouscoal (BITCOAL); Sub-bituminous coal (HS 270119) → IEA Sub-bituminous coal (SUB-COAL); Lignite (HS 270210) → IEA Lignite (LIGNITE); Agglomerated lignite (HS 270220) → IEA Coking coal (COKCOAL). - Crude oil: Petroleum oils crude (HS 270900) → IEA Crude/NGL/feedstocks (CRNGFEED). - Natural gas: Natural gas, liquefied (HS 271111) → IEA Natural gas (NATGAS); Natural gas in gaseous state (HS 271121). - Sources: "Production data are from the International Energy Agency (IEA) world energy balances database, except for coal, which is obtained from the IEA world energy statistics database. Bilateral trade data for energy are from the Bilateral Commodity Trade Database (BACI), which draws on UN Comtrade." - Table A.5: Economies Included and the Composition of Blocs in the Baseline Scenario - Lists member economies assigned to two blocs used in the baseline scenario: - US-Europe+ Bloc: a long list including United States; United Kingdom; United States; United Kingdom; European economies; many others (full listing in Table A.5). - China-Russia+ Bloc: a long list including China; Russia; India; many other economies (full listing in Table A.5). ### Appendix A — Price elasticities (Table A.6) - Presents Short-Run Demand Elasticity and Short-Run Supply Elasticity by commodity with sources. - Selected exact entries (preserved numeric precision): - Cocoa: Demand -0.075, Supply 0.075 — Source: Fally and Sayre (2019) - Coffee: Demand -0.305, Supply 0.285 — Source: Fally and Sayre (2019) - Cotton: Demand -0.684, Supply 0.497 — Source: Fally and Sayre (2019) - Maize: Demand -0.34, Supply 0.327 — Source: Fally and Sayre (2019) - Rice: Demand -0.24, Supply 0.167 — Source: Fally and Sayre (2019) - Aluminium (refined): Demand -0.047, Supply 0.235 — Source: Dahl (2020) - Chromium: Demand -2.622, Supply 0.265625 — Source: Dahl (2020) - Lithium: Demand -0.54, Supply 0.265625 — Source: Dahl (2020) - Nickel: Demand -0.03, Supply 0.75 — Source: Dahl (2020) - Natural gas: Demand -0.5015, Supply 0.075 — Source: Fally and Sayre (2019) - Crude oil: Demand -0.0415, Supply 0.1445 — Source: Fally and Sayre (2019) - Coal: Demand -0.5, Supply 0.0565 — Source: Fally and Sayre (2019) - Note: Several entries use "Average mineral elasticities" or "Average agriculture elasticities" where commodity-specific estimates are not available. ### Appendix B — Additional Figures (descriptions) - Figure B.1–B.9: Visualizations of concentration and trade patterns, including: - Refined Metals: Production, Export and Import Concentrations (top three shares in 2019). - Distribution of Demand and Supply Elasticities. - Share of Production That Is Traded (2019 and 2000). - Share of Global Exports/Imports Accounted for by the Top 3 Exporters/Importers (2019). - Production Across Blocs (share of global production by bloc in 2019). - Trade Flows Across Blocs for selected vulnerable commodities (Cobalt, Copper, Lithium, Nickel, Palm Oil, Soybean). - Concentration of trade flows for Palm oil and Soybean (aggregate exporter/importer diagrams). - Bloc-Level changes in total economic surplus from fragmentation (Top 5 largest changes, percent of Bloc-Level GNE). - Distribution of country-level changes in total surplus from fragmentation (capped at +/- 5 percent of GNE). ### Appendix C — Robustness exercises (tables and figures) - Table C.7: Alternative Calibration with Median Elasticities from Fally and Sayre (2018) - Provides alternative Short-Run Demand and Supply Elasticities for many commodities (exact values preserved). Examples: - Cocoa: Demand -0.08, Supply 0.08 - Coffee: Demand -0.2, Supply 0.19 - Aluminium (refined): Demand -0.27, Supply 0.14 - Copper: Demand -0.31, Supply 0.34 - Crude oil: Demand -0.04, Supply 0.14 - Table C.8: Constant Elasticities within Each Category of Commodities - Agriculture: Demand -0.376, Supply 0.259 - Energy: Demand -0.348, Supply 0.092 - Minerals: Demand -0.271, Supply 0.266 - Figures C.10–C.18: Alternative-calibration and bloc-configuration visual results - Price changes by commodity across blocs (using median elasticities from Fally and Sayre (2018); constant elasticities; alternative bloc configurations I and II). - Top 5 largest changes in bloc-level total economic surplus under alternative calibrations and bloc configurations. - Comparison of fragmentation-induced price changes across baseline and alternative bloc configurations (price effects capped at 500 percent; energy = coal, natural gas, crude oil). ### Appendix D — The Effects of Fragmentation on Total Surplus (theoretical results) - Supply and demand specification (exact functional forms): - ln qd_c = ηd ln p + γd_c (Equation 15) - ln qs_c = ηs ln p + γs_c (Equation 16) - qd_c = e^{γd_c} p^{ηd} (Equation 17) - qs_c = e^{γs_c} p^{ηs} (Equation 18) - Consumer surplus change (exact expression): - ∆CS_c = − e^{γd_c} / (1 + ηd) [ p^{1+ηd} ]_{p0}^{p1} = − 1/(1 + ηd) p0 qd0 [ (p1/p0)^{1+ηd} − 1 ] (Equation 19) - Producer surplus change (exact expression): - ∆PS_c = e^{γs_c} / (1 + ηs) [ p^{1+ηs} ]_{p0}^{p1} = 1/(ηs + 1) p0 qs0 [ (p1/p0)^{1+ηs} − 1 ] (Equation 20) - Total surplus change (exact expression): - ∆TS_c = − 1/(1 + ηd) p0 qd0 [ (p1/p0)^{1+ηd} − 1 ] + 1/(ηs + 1) p0 qs0 [ (p1/p0)^{1+ηs} − 1 ] (Equation 21) - Main formal results (preserved conclusions): - Proposition D.1: If the country is a commodity exporter (qs_c > qd_c) and belongs to a commodity importing bloc, then the change of price from the global price to the bloc price increases the total surplus of the country. - Lemma D.2: If a bloc is a commodity importer (exporter) in the global economy, then the equilibrium price within the bloc would be higher (lower) than the global price. - Lemma D.3: If a country (or bloc) is a commodity exporter so that qs_c > qd_c, then a rise in the price of the commodity raises the total surplus of the country. Analytical derivative: - ∂∆TS_c/∂ p̃ = − p0 qd0 p̃^{ηd} + p0 qs0 p̃^{ηs} (Equation 23) where p̃ ≡ p1/p0. - Proposition D.4: Total surplus in each bloc is declining from fragmentation. Proof outline: price in exporting bloc declines and in importing bloc rises (Lemma D.2); for importing bloc the integrand of total-surplus change is negative on the relevant interval, implying overall negative total-surplus change. - Exact functional relationships and inequalities used in proofs are provided (Equations 22–30) and show the dependence of total-surplus changes on elasticities, initial quantities, and relative price changes. *Geoeconomic Fragmentation and Commodity Markets — Working Paper No. WP/2023/201 (References and Appendices).* --- _Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023201-print-pdf.pdf_