## 2.2 mb/d of gradual unwinding of production cuts, combined

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**Canonical URL:** [2.2 mb/d of gradual unwinding of production cuts, combined](https://www.imf.org/-/media/files/publications/weo/2025/october/english/commodityspecialfeature.pdf)

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---

### Commodity price developments and forecasts
- Oil:
  - Production changes: "2.2 mb/d of gradual unwinding of production cuts, combined with a 0.3 mb/d higher production quota for the United Arab Emirates."
  - Price path: "in 2025, a 12.9 percent decline from the previous year, before decreasing to $65.80 in 2026 and steadily increasing to $67.30 through 2030 (Figure 1.SF.1, panel 2)."
  - Risk balance: "Risks around this forecast are balanced. While potential Russian supply disruptions present an upside risk to prices, the risk of accelerated OPEC+ supply increases, combined with the tariff-induced cloudy global economic environment, continue to pressure prices downward. All the while, higher-cost producers set a loose price floor, with some US break-even prices in the low to mid $60s."

- Natural gas:
  - TTF (Europe): "TTF trading hub prices in Europe dropped 16.6 percent between March 2025 and August 2025 to $11.0 per million British thermal units (MMBtu)."
  - Asian LNG: "Asian liquefied natural gas prices tracked the decreasing trend in European prices, falling by 12.2 percent."
  - Henry Hub (US): "US Henry Hub prices fell by 30 percent to $2.9 per MMBtu owing to trade-policy-induced demand uncertainty and record-high domestic production."
  - Futures: "Futures markets suggest that TTF prices will average $12.1/MMBtu in 2025, steadily decreasing to $8.4/MMBtu in 2030, reflecting ample global liquefied natural gas supply in the medium term, with US export capacity expected to almost double through 2027." "Henry Hub prices are expected to fluctuate around $3.5/MMBtu between 2025 and 2030."

- Metals and precious metals:
  - IMF metals price index: "rose 6.8 percent between March and August 2025."
  - Gold: "gold increasing 12.8 percent, reaching record highs above $3,400/ounce as investors sought safe haven assets amid rising geopolitical uncertainty and central banks increased gold reserves."
  - Base metals: "US import tariffs had mixed effects on base metals. While US tariffs announced in early April pressured global prices downward, 50 percent tariffs on steel, aluminum, and copper triggered front-loading by the United States, providing some support to prices. Futures markets suggest modest increases of 0.3 percent in 2025 and 3.0 percent in 2026."
  - Rare earths: "Rare earth carbonate feedstock prices also jumped 30.2 percent as reduced US raw material exports to China tightened global supplies of processed rare earths amid strengthening demand."

- Agricultural commodities:
  - IMF food and beverages price index: "fell by 4.8 percent" from March to August 2025.
  - Cereal prices: "dropped by 11.1 percent amid strong harvest prospects in major producing countries, such as the United States, Russia, Brazil, and Argentina."
  - Coffee: "plunged by 16.7 percent, with the IMF Coffee Index retreating from its February historic high..."
  - Corn: "fell 11.9 percent, pressured by Brazil’s large harvest in the second quarter and promising crop conditions in the United States."
  - Temporary reversals: "prices surged briefly in August, following US tariffs on Brazil that caused trade disruptions."

### Risks to the commodity price outlook
- Downside risks:
  - "The risk of accelerated OPEC+ supply increases"
  - "tariff-induced cloudy global economic environment" that reduces energy demand
  - "Larger-than-expected harvests and higher tariffs" for agricultural prices
- Upside risks:
  - "Potential Russian supply disruptions"
  - "new export restrictions, which might raise global prices by tightening international supply—even as they put downward pressure on food prices in some exporting countries"
  - "potential bad weather resulting from La Niña in the fourth quarter"
- Price-floor dynamics:
  - "higher-cost producers set a loose price floor, with some US break-even prices in the low to mid $60s."

### Market dynamics and trade policy effects
- Tariff impacts:
  - Tariffs reduced energy demand via business uncertainty, contributing to declines in natural gas prices.
  - US tariffs had mixed effects on base metals: downward pressure overall, but front-loading due to "50 percent tariffs on steel, aluminum, and copper" provided some support to prices.
  - US tariffs on Brazil in August caused temporary surges in some agricultural prices due to trade disruptions.
- Supply-side changes:
  - China's export controls on seven critical rare earth elements in April caused "dramatic export slowdowns during April and May," with magnet exports rebounding after a June 11 US-China trade agreement; "Rare earth carbonate feedstock prices also jumped 30.2 percent."
  - US export capacity for liquefied natural gas: "expected to almost double through 2027," supporting medium-term ample LNG supply.

### Commodity-driven macroeconomic fluctuations: size versus interconnectedness
- Purpose of analysis:
  - This Special Feature examines: "(1) How do commodity sectors’ linkages with the broader economy differ between emerging market and developing economies and advanced economies and across different commodities? (2) How do these linkages (up- and downstream) affect the propagation of commodity price shocks to the rest of the economy? and (3) How should monetary policy respond?"
- Size (Domar weights) differences:
  - "The average size, or Domar weight, of the commodity sectors in emerging market and developing economies is twice as large for metals, three times as large for energy, and almost four times as large for agriculture compared with advanced economies."
  - Aggregate comparison: "The average commodity sector is three times larger (Domar weight) in emerging market and developing economies than in advanced economies..."
- Interconnectedness (NAVAS) findings:
  - Definition: NAVAS = network-adjusted value-added share, "the sector’s total (direct and indirect) exposure to the economy’s factors of production."
  - NAVAS vs Domar: "The commodity sector NAVAS is larger than its size (Domar weight) in both advanced and emerging market economies, but the differences in NAVAS across both groups tend to be smaller than the differences in size."
  - Quantitative comparison: "...its network-adjusted value-added share (NAVAS) is only 31 percent higher [in emerging markets], with energy exhibiting the biggest difference across country groups and metals and agricultural products the smallest."
  - Distributional overlap: "There is also a large overlap between the right tail of the distribution of the NAVAS in advanced economies and the left tail in emerging market and developing economies, meaning that commodity sectors in many advanced economies are more interconnected than in emerging market and developing economies and that commodity price shocks in these advanced economies may have a larger and more persistent effect on economic activity."
- Empirical evidence on consumption responses:
  - Relationship: "countries with a more interconnected commodity sector (higher NAVAS) display stronger annual correlation between aggregate consumption and commodities terms of trade."
  - NAVAS significance: "Figure 1.SF.3, panel 2, confirms that interconnectedness (NAVAS) matters for the effect of commodity price shocks on consumption, even after controlling for the role of size (Domar weights). Coefficient estimates ... show that the NAVAS interaction coefficient—which measures the marginal impact of deeper interconnectedness on the response of consumption to terms-of-trade changes—is substantially larger than the coefficient for the size interaction and is always significant."
  - Country examples:
    - "Thailand’s commodity sector is six times larger than Switzerland’s, their NAVAS values are almost identical (0.68 in Thailand and 0.65 in Switzerland)"
    - "the Norwegian energy sector exhibits a NAVAS of 0.94, significantly larger than Vietnam’s (0.48)... shocks to energy prices are more correlated with consumption in Norway than in Vietnam."
  - Counterintuitive result: "the correlation is sometimes negative, even for commodity net exporters (for example South Africa)."

### Model-based analysis
- Model and calibration:
  - Model: small open economy dynamic stochastic general equilibrium model from Silva and others (2024) and Gomez-Gonzalez and others (2025).
  - Calibration uses Organisation for Economic Co-operation and Development input-output data covering 66 countries and 44 sectors.
  - Calibration matches each country’s sectoral final consumption shares, input-output shares, and the commodity sector’s net exports, all in 2018.
  - Benchmark aggregation: 1 commodity sector and 38 non-commodity sectors (six commodity sectors aggregated into one).
- Experiments and outcomes:
  - Simulations (Figure 1.SF.4) show a positive slope: emerging market and developing economies tend to have higher NAVAS and higher correlation of cyclical consumption and terms-of-trade shocks; some advanced economies display higher NAVAS and stronger co-movement.
  - Variation remains in the correlation of consumption with commodity price shocks for the same NAVAS, indicating complex propagation mechanisms.

### Transmission mechanism (illustration: Kazakhstan vs South Africa)
- Setup:
  - Two commodity net exporters with similar commodity sector size: 39 percent of GDP for each.
  - NAVAS: Kazakhstan 0.90; South Africa 0.73.
  - Shock: 1 percent commodity terms-of-trade shock; impulse response functions shown in Figure 1.SF.5.
- Key model outcomes:
  - Aggregate consumption response to a 1 percent shock:
    - Kazakhstan: positive and large.
    - South Africa: negative.
  - Real wages increase in both countries (nominal wages increase more than prices) because higher commodity-sector revenues boost labor demand.
  - Final consumption impact depends on labor income and change in households’ real wealth (net foreign assets denominated in units of real commodity goods).
  - In South Africa:
    - Aggregate price index increases more than commodity prices on impact (more than 1 percent), causing a decline in the real value of net foreign assets (negative wealth shock) and a decline in consumption.
- Mechanism insight:
  - Any exogenous increase in commodity prices raises marginal costs in the commodity sector until excess profit is driven to zero.
  - Higher marginal costs derive from factor prices (wages) and intermediate input prices.
  - High NAVAS implies greater interconnectedness; intermediate input price contributions dilute marginal-cost-driven wage increases, requiring a smaller wage rise for a given marginal-cost increase.
  - Low NAVAS economies (e.g., South Africa) see commodity price shocks feed more directly into factor costs, producing larger aggregate price increases, lower real net foreign assets, and a smaller or negative wealth effect on consumption.
- Summary finding:
  - Differences in commodity sector linkages (NAVAS) drive cross-country differences in macroeconomic responses to commodity price fluctuations. Wealth effects can offset or exceed income effects, regardless of sector size measured by Domar weights.

### Implications for monetary policy in small open economies
- Stylized facts:
  - Global commodity prices are flexible, but pass-through to domestic commodity sectors is incomplete; domestic commodity prices are stickier.
  - Standard closed-economy prescriptions advise responding only to inflation in sticky-price sectors.
- Policy-design critique:
  - Domar weights are useful in closed economies (Rubbo 2023) but using them in small open economies instead of the network-adjusted weight (NAW, which depends on NAVAS) leads to welfare losses inversely proportional to NAVAS (Qiu and others 2025).
  - When commodity sector NAVAS is low (sector relies more on foreign than domestic factors), commodity price fluctuations do not cause commensurate output gap fluctuations — thus limited need for monetary response.
- Quantified policy mistakes:
  - Relying on size (Domar weight) instead of NAW leads to over-weighting the commodity sector by roughly a third on average (distribution shown in Figure 1.SF.6).
  - Example calculations:
    - Advanced economies: average size of commodity sector = 13 percent; average monetary policy mistake = 34 percent; implied actual weight = 8.6 percent.
    - Emerging market and developing economies: average size = 39 percent; average monetary policy mistake = 24 percent; implied actual weight = 30 percent.
  - Group averages:
    - Advanced economies tend to overestimate importance of commodity sector by 32 percent (average).
    - Emerging market and developing economies tend to overestimate by 27 percent (average).
- Monetary policy mistake (PM) formula:
  - PM = k(1 – NAVAS) + export intensity – expenditure switching.
- Policy recommendation:
  - Central banks in small open economies should account for production network structure (NAVAS/NAW) when calibrating their response to commodity price movements to avoid overreaction and welfare losses.

### Conclusion
- The macroeconomic impact of commodity price shocks depends less on sector size and more on how interconnected the commodity sector is with the rest of the economy (NAVAS).
- NAVAS explains cross-country differences in consumption responses to commodity price fluctuations.
- Policy implication: macroeconomic frameworks and central bank reaction functions should incorporate production network structures to reduce policy miscalibration and enhance macroeconomic stability across both advanced and emerging market economies, irrespective of net commodity trade position.

*International Monetary Fund, Commodity Special Feature: Market Developments and Commodity-Driven Macroeconomic Fluctuations (excerpts).*

### 2.2 mb/d of gradual unwinding of production cuts, combined

### 2.2 mb/d of gradual unwinding of production cuts, combined with a 0.3 mb/d higher production quota for the United Arab Emirates.

### Commodity price developments and forecasts
- Oil:
  - Production changes: "2.2 mb/d of gradual unwinding of production cuts, combined with a 0.3 mb/d higher production quota for the United Arab Emirates."
  - Price path noted in source: "in 2025, a 12.9 percent decline from the previous year, before decreasing to $65.80 in 2026 and steadily increasing to $67.30 through 2030 (Figure 1.SF.1, panel 2)."
  - Risk balance: "Risks around this forecast are balanced. While potential Russian supply disruptions present an upside risk to prices, the risk of accelerated OPEC+ supply increases, combined with the tariff-induced cloudy global economic environment, continue to pressure prices downward. All the while, higher-cost producers set a loose price floor, with some US break-even prices in the low to mid $60s."

- Natural gas:
  - TTF (Europe): "TTF trading hub prices in Europe dropped 16.6 percent between March 2025 and August 2025 to $11.0 per million British thermal units (MMBtu)."
  - Asian LNG: "Asian liquefied natural gas prices tracked the decreasing trend in European prices, falling by 12.2 percent."
  - Henry Hub (US): "US Henry Hub prices fell by 30 percent to $2.9 per MMBtu owing to trade-policy-induced demand uncertainty and record-high domestic production."
  - Futures: "Futures markets suggest that TTF prices will average $12.1/MMBtu in 2025, steadily decreasing to $8.4/MMBtu in 2030, reflecting ample global liquefied natural gas supply in the medium term, with US export capacity expected to almost double through 2027." "Henry Hub prices are expected to fluctuate around $3.5/MMBtu between 2025 and 2030."

- Metals and precious metals:
  - IMF metals price index: "rose 6.8 percent between March and August 2025."
  - Gold: "gold increasing 12.8 percent, reaching record highs above $3,400/ounce as investors sought safe haven assets amid rising geopolitical uncertainty and central banks increased gold reserves."
  - Base metals: "US import tariffs had mixed effects on base metals. While US tariffs announced in early April pressured global prices downward, 50 percent tariffs on steel, aluminum, and copper triggered front-loading by the United States, providing some support to prices. Futures markets suggest modest increases of 0.3 percent in 2025 and 3.0 percent in 2026."
  - Rare earths: "Rare earth carbonate feedstock prices also jumped 30.2 percent as reduced US raw material exports to China tightened global supplies of processed rare earths amid strengthening demand."

- Agricultural commodities:
  - IMF food and beverages price index: "fell by 4.8 percent" from March to August 2025.
  - Cereal prices: "dropped by 11.1 percent amid strong harvest prospects in major producing countries, such as the United States, Russia, Brazil, and Argentina."
  - Coffee: "plunged by 16.7 percent, with the IMF Coffee Index retreating from its February historic high..."
  - Corn: "fell 11.9 percent, pressured by Brazil’s large harvest in the second quarter and promising crop conditions in the United States."
  - Temporary reversals: "prices surged briefly in August, following US tariffs on Brazil that caused trade disruptions."

### Risks to commodity price outlook
- Downside risks:
  - "The risk of accelerated OPEC+ supply increases"
  - "tariff-induced cloudy global economic environment" that reduces energy demand
  - "Larger-than-expected harvests and higher tariffs" for agricultural prices
- Upside risks:
  - "Potential Russian supply disruptions"
  - "new export restrictions, which might raise global prices by tightening international supply—even as they put downward pressure on food prices in some exporting countries"
  - "potential bad weather resulting from La Niña in the fourth quarter"
- Price-floor dynamics: "higher-cost producers set a loose price floor, with some US break-even prices in the low to mid $60s."

### Market dynamics and trade policy effects
- Tariff impacts:
  - Tariffs reduced energy demand via business uncertainty, contributing to declines in natural gas prices.
  - US tariffs had mixed effects on base metals: downward pressure overall, but front-loading due to 50 percent tariffs on steel, aluminum, and copper provided some support to prices.
  - US tariffs on Brazil in August caused temporary surges in some agricultural prices due to trade disruptions.
- Supply-side changes:
  - China’s export controls on seven critical rare earth elements in April caused "dramatic export slowdowns during April and May," with magnet exports rebounding after a June 11 US-China trade agreement; "Rare earth carbonate feedstock prices also jumped 30.2 percent."
  - US export capacity for liquefied natural gas: "expected to almost double through 2027," supporting medium-term ample LNG supply.

### Commodity-driven macroeconomic fluctuations: size versus interconnectedness
- Purpose of analysis:
  - This Special Feature examines: "(1) How do commodity sectors’ linkages with the broader economy differ between emerging market and developing economies and advanced economies and across different commodities? (2) How do these linkages (up- and downstream) affect the propagation of commodity price shocks to the rest of the economy? and (3) How should monetary policy respond?"
- Size (Domar weights) differences:
  - "The average size, or Domar weight, of the commodity sectors in emerging market and developing economies is twice as large for metals, three times as large for energy, and almost four times as large for agriculture compared with advanced economies."
  - Aggregate comparison: "The average commodity sector is three times larger (Domar weight) in emerging market and developing economies than in advanced economies..."
- Interconnectedness (NAVAS) findings:
  - Definition: NAVAS = network-adjusted value-added share, "the sector’s total (direct and indirect) exposure to the economy’s factors of production."
  - NAVAS vs Domar: "The commodity sector NAVAS is larger than its size (Domar weight) in both advanced and emerging market economies, but the differences in NAVAS across both groups tend to be smaller than the differences in size."
  - Quantitative comparison: "...its network-adjusted value-added share (NAVAS) is only 31 percent higher [in emerging markets], with energy exhibiting the biggest difference across country groups and metals and agricultural products the smallest."
  - Distributional overlap: "There is also a large overlap between the right tail of the distribution of the NAVAS in advanced economies and the left tail in emerging market and developing economies, meaning that commodity sectors in many advanced economies are more interconnected than in emerging market and developing economies and that commodity price shocks in these advanced economies may have a larger and more persistent effect on economic activity."
- Empirical evidence on consumption responses:
  - Relationship: "countries with a more interconnected commodity sector (higher NAVAS) display stronger annual correlation between aggregate consumption and commodities terms of trade."
  - NAVAS significance: "Figure 1.SF.3, panel 2, confirms that interconnectedness (NAVAS) matters for the effect of commodity price shocks on consumption, even after controlling for the role of size (Domar weights). Coefficient estimates ... show that the NAVAS interaction coefficient—which measures the marginal impact of deeper interconnectedness on the response of consumption to terms-of-trade changes—is substantially larger than the coefficient for the size interaction and is always significant."
  - Notable country examples: "Thailand’s commodity sector is six times larger than Switzerland’s, their NAVAS values are almost identical (0.68 in Thailand and 0.65 in Switzerland)"; "the Norwegian energy sector exhibits a NAVAS of 0.94, significantly larger than Vietnam’s (0.48)... shocks to energy prices are more correlated with consumption in Norway than in Vietnam."
  - Counterintuitive result: "the correlation is sometimes negative, even for commodity net exporters (for example South Africa)."

*International Monetary Fund, Commodity Special Feature: Market Developments and Commodity-Driven Macroeconomic Fluctuations (excerpts).*

### Annex Figure 1.SF.1).

### Annex Figure 1.SF.1)

### Model-Based Analysis
- Model: small open economy dynamic stochastic general equilibrium model from Silva and others (2024) and Gomez-Gonzalez and others (2025).
- Calibration:
  - Uses Organisation for Economic Co-operation and Development input-output data covering 66 countries and 44 sectors.
  - Calibration matches each country’s sectoral final consumption shares, input-output shares, and the commodity sector’s net exports, all in 2018.
  - Benchmark aggregation: 1 commodity sector and 38 non-commodity sectors (six commodity sectors aggregated into one).
- Experiments:
  - Relationship between NAVAS and co-movement between consumption and commodity terms of trade:
    - Model simulations (Figure 1.SF.4) show a positive slope: emerging market and developing economies tend to have higher NAVAS and higher correlation of cyclical consumption and terms-of-trade shocks; some advanced economies display higher NAVAS and stronger co-movement.
    - Variation remains in the correlation of consumption with commodity price shocks for the same NAVAS, indicating complex propagation mechanisms.

### Transmission Mechanism (Illustration: Kazakhstan vs South Africa)
- Setup:
  - Two commodity net exporters with similar commodity sector size: 39 percent of GDP for each.
  - NAVAS: Kazakhstan 0.90; South Africa 0.73.
  - Shock: 1 percent commodity terms-of-trade shock; impulse response functions shown in Figure 1.SF.5.
- Key model outcomes:
  - Aggregate consumption response to a 1 percent shock:
    - Kazakhstan: positive and large.
    - South Africa: negative.
  - Real wages increase in both countries (nominal wages increase more than prices) because higher commodity-sector revenues boost labor demand.
  - Final consumption impact depends on labor income and change in households’ real wealth (net foreign assets denominated in units of real commodity goods).
  - In South Africa:
    - Aggregate price index increases more than commodity prices on impact (more than 1 percent), causing a decline in the real value of net foreign assets (negative wealth shock) and a decline in consumption.
- Mechanism insight:
  - Any exogenous increase in commodity prices raises marginal costs in the commodity sector until excess profit is driven to zero.
  - Higher marginal costs derive from factor prices (wages) and intermediate input prices.
  - High NAVAS implies greater interconnectedness; intermediate input price contributions dilute marginal-cost-driven wage increases, requiring a smaller wage rise for a given marginal-cost increase.
  - Low NAVAS economies (e.g., South Africa) see commodity price shocks feed more directly into factor costs, producing larger aggregate price increases, lower real net foreign assets, and a smaller or negative wealth effect on consumption.
- Summary finding:
  - Differences in commodity sector linkages (NAVAS) drive cross-country differences in macroeconomic responses to commodity price fluctuations. Wealth effects can offset or exceed income effects, regardless of sector size measured by Domar weights.

### Implications for Monetary Policy in Small Open Economies
- Stylized facts:
  - Global commodity prices are flexible, but pass-through to domestic commodity sectors is incomplete; domestic commodity prices are stickier.
  - Standard closed-economy prescriptions advise responding only to inflation in sticky-price sectors.
- Policy-design critique:
  - Domar weights are useful in closed economies (Rubbo 2023) but using them in small open economies instead of the network-adjusted weight (NAW, which depends on NAVAS) leads to welfare losses inversely proportional to NAVAS (Qiu and others 2025).
  - When commodity sector NAVAS is low (sector relies more on foreign than domestic factors), commodity price fluctuations do not cause commensurate output gap fluctuations — thus limited need for monetary response.
- Quantified policy mistakes:
  - Relying on size (Domar weight) instead of NAW leads to over-weighting the commodity sector by roughly a third on average (distribution shown in Figure 1.SF.6).
  - Example calculations:
    - Advanced economies: average size of commodity sector = 13 percent; average monetary policy mistake = 34 percent; implied actual weight = 8.6 percent.
    - Emerging market and developing economies: average size = 39 percent; average monetary policy mistake = 24 percent; implied actual weight = 30 percent.
  - Group averages:
    - Advanced economies tend to overestimate importance of commodity sector by 32 percent (average).
    - Emerging market and developing economies tend to overestimate by 27 percent (average).
- Monetary policy mistake (PM) formula:
  - PM = k(1 – NAVAS) + export intensity – expenditure switching.
- Policy recommendation:
  - Central banks in small open economies should account for production network structure (NAVAS/NAW) when calibrating their response to commodity price movements to avoid overreaction and welfare losses.

### Conclusion
- The macroeconomic impact of commodity price shocks depends less on sector size and more on how interconnected the commodity sector is with the rest of the economy (NAVAS).
- NAVAS explains cross-country differences in consumption responses to commodity price fluctuations.
- Policy implication: macroeconomic frameworks and central bank reaction functions should incorporate production network structures to reduce policy miscalibration and enhance macroeconomic stability across both advanced and emerging market economies, irrespective of net commodity trade position.

*Source: commodityspecialfeature - Annex Figure 1.SF.1).*

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_Source: https://www.imf.org/-/media/files/publications/weo/2025/october/english/commodityspecialfeature.pdf_