## ch1onlineannex - Sections 1–2 ## Source details **Canonical URL:** [ch1onlineannex - Sections 1–2](https://www.imf.org/-/media/files/publications/weo/2025/october/english/ch1onlineannex.pdf) ## Other formats - [Markdown version](/-/media/files/publications/weo/2025/october/english/ch1onlineannex.pdf.md) - [Structured JSON version](/-/media/files/publications/weo/2025/october/english/ch1onlineannex.pdf.json) --- ### Part I. Network-Adjusted Value-Added Share (NAVAS) - Definition and interpretation - NAVAS captures both upstream linkages (commodity sector as purchaser of inputs) and downstream linkages (commodity sector as supplier to other industries). - NAVAS is defined as the weighted sum of the value-added shares (a) of all domestic sectors that supply intermediates to the commodity sector, directly or indirectly, and imported intermediates. - Commodity sector (N+1) NAVAS formula (text description preserved): - NAVAS_{N+1} = Σ Ψ_{N+1,i} a_i - Ψ = (I − Γ)^{-1} is the Leontief inverse matrix; Γ is the input-output (IO) matrix with element Γ_{i,j} referring to the share of inputs from sector j used by sector i as a fraction of sector i’s gross output. - If Γ_{N+1,i} = 0 for all i (commodity sector buys no domestic intermediates), then NAVAS_{N+1} = a_{N+1}. - Illustrative three-sector examples (C = commodity, A, B) - Example values and vector of value-added shares: - a = (0.3, 0.3, 0.6)′ (ordering: C, A, B) - When C buys no domestic intermediates, NAVAS_C = a_C = 0.3 regardless of C’s supplier centrality. - Leontief inverse matrices (preserved exactly): - Ψ_1 = 1 0 0 0.28571 0.2857 0.2857 0.14290 0.1429 0.1429 - Ψ_2 = 1 0 0 0.47061 1.1176 0.1176 0.23530 0.05881 0.0588 - Example 2 (different supplier centrality, same customer centrality) - IO matrices: - Γ_3 = 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 - Γ_4 = 0.2 0.2 0.2 0.25 0.2 0.2 0.15 0.1 0.1 - Total domestic intermediate share = 0.6; imported intermediate input share = 0.1. - Resulting NAVAS: 0.81 for Γ_4 versus 0.78 for Γ_3. - Example 3 (different customer centrality, similar supplier centrality) - IO matrices: - Γ_5 = 0.1 0.1 0.4 0.2 0.2 0.2 0.1 0.1 0.1 - Γ_6 = 0.1 0.4 0.1 0.2 0.2 0.2 0.1 0.1 0.1 - NAVAS increases from 0.77 to 0.80 when the commodity sector allocates a larger share of purchases toward the more labor-intensive supplier (sector B). ### Part II. Empirics — data, methods, and descriptive statistics - Data and sample - Unbalanced annual panel of 66 countries (37 advanced economies (AE) and 29 emerging and developing economies (EMDE)). - Sample spans from 1990 to 2018 (extended through 2023 where data permit). - Primary data sources: - IMF Commodity Terms of Trade database — Commodity Net Export Price Index (weighted by net exports as a share of GDP), using rolling windows to capture time-varying composition of commodity trade. - Global Macro Database (GMD, Müller and others, 2025) — macro indicators and controls; all variables denominated in U.S. dollars. - OECD Input-Output Tables (IOT), 2018 edition — used to capture interlinkages and compute NAVAS. - Descriptive statistics (2018) — numeric entries preserved exactly - Advanced Economies (categories in order: Energy / Metals / Agriculture / Aggregate for Size and NAVAS): - Mean: 0.05 0.04 0.04 0.13 0.50 0.61 0.78 0.61 - Median: 0.04 0.04 0.04 0.13 0.48 0.60 0.78 0.62 - Standard Deviation: 0.05 0.03 0.03 0.06 0.23 0.13 0.07 0.15 - Min: 0.00 0.00 0.00 0.04 0.21 0.21 0.55 0.22 - Max: 0.23 0.11 0.12 0.31 0.94 0.88 0.90 0.89 - Emerging Markets (categories in order: Energy / Metals / Agriculture / Aggregate for Size and NAVAS): - Mean: 0.14 0.08 0.15 0.39 0.66 0.73 0.86 0.80 - Median: 0.10 0.05 0.11 0.32 0.67 0.77 0.88 0.83 - Standard Deviation: 0.18 0.06 0.14 0.22 0.21 0.16 0.08 0.12 - Min: 0.00 0.01 0.01 0.12 0.23 0.37 0.65 0.50 - Max: 0.95 0.23 0.74 1.05 0.98 0.95 0.96 0.97 - Empirical strategy - Local Projection regression (Jordà, 2005) for cumulative change in detrended log real consumption c_{i,t+h} − c_{i,t−1} with specification (symbols preserved): - c_{i,t+h} − c_{i,t−1} = α_h + μ_i + Σ_{s=0}^{h} δ_{h,s} Δx_{t−s} + β_h(ε_{i,t}) + β_h^{(1)}(ε_{i,t} × NAVAS_i) + γ_h(ε_{i,t} × size_i) + u_{i,t+h} - NAVAS = country i's network-adjusted value-added share of the commodity sector. - size = Domar weight of the commodity sector. - x = vector of controls including lags of the dependent variable and lags of the shock. - Country fixed effects μ_i included. - Shock ε_{i,t} identified following Schmitt-Grohe and Uribe (2018): residual from an AR(1) fitting log commodity price changes; residuals display white-noise properties (confirmed by Ljung-Box statistic). - Gomez-Gonzalez and others (2025) provide IV robustness. ### Part II. Empirics — key findings - NAVAS magnifies consumption responses to commodity price shocks - Online Annex Figure 1.1.2 (text description preserved): cumulative impulse responses of consumption following a one-standard-deviation shock in commodity prices for economies at the 10th and 90th percentiles of the NAVAS distribution. - Countries in the 90th percentile show an overall positive consumption response; countries in the 10th percentile show a negative response (NAVAS normalized by its cross-country sample mean). - Cross-country heterogeneity - NAVAS is a robust driver of cross-country heterogeneity in consumption responses. - Commodity sector size has a smaller and statistically weaker interaction effect with commodity price shocks than NAVAS (size is less relevant than network position). ### Part III. Model — structure, calibration, and mechanism - Model framework and agents - Based on multi-sector dynamic small-open economy models of Silva and others (2024) and Gomez-Gonzalez and others (2025). - Households maximize lifetime discounted utility of consumption (symbolic form preserved). - Households face budget constraint including aggregate price P_t, aggregate consumption C_t, foreign asset position B_t denominated in units of the commodity good, bond holding adjustment cost g(B_t) = (B - B̄)^2 / B̄^2 (text-preserved functional form), wage W_t, interest rate r, labor supply L fixed. - Given aggregate consumption, households allocate consumption across goods minimizing costs with sectoral consumption shares β_i and sectoral price indices P_i. - Production and shocks - Production (symbolic form preserved): - Q_i/Q̄_i = Z_i [ a_i (L_i/L̄_i)^{(σ_i−1)/σ_i} + (1−a_i)(M_i/M̄_i)^{(σ_i−1)/σ_i} ]^{σ_i/(σ_i−1)} - Intermediate bundles: imported vs domestic intermediates elasticity ε_i; domestic intermediate bundle constructed from M_{i,j} with expenditure shares ω_{i,j} and substitution elasticity ε̄_{i}. - Exogenous processes (preserved): - log P_{c,t}^* = ρ_{c} log P_{c,t−1}^* + ε_{c,t} - log Z_{i,t} = ρ_Z log Z_{i,t−1} + ε_{i,t} - Market clearing conditions preserved for goods, labor, and accumulation of net foreign assets (NFA) with exact symbolic representation as in the source. - Calibration and solution - Calibrated using OECD input-output data as in Part II. - Model has 38 non-commodity sectors and one commodity sector (N+1) that aggregates six OECD commodity sectors into one in the model. - Calibrated to match each country’s sectoral final consumption shares, IO shares, and the commodity sector’s net exports (all 2018). - Other parameters (discount rate, elasticities, shock persistence/volatility) taken from Silva and others (2024). - Solved using first-order log-linear approximation around the non-stochastic steady state. - Income and wealth channels; role of NAVAS (expressions and interpretation preserved) - Aggregate consumption expression (symbolic) links consumption to foreign assets, valuation effects, and real wage. - Global commodity price shocks influence consumption via: - Income effect through real wage (W_t/P_t). - Valuation effect of net foreign assets captured by relative commodity price (P_{c}/P), a proxy for terms of trade. - Adjustments in net foreign assets. - Role of NAVAS in these channels (preserved expressions): - d log (W_t/P_t) = (1 − β̄_ā)^{-1} ā_{c} (text-preserved relation indicating income effect decreasing in commodity sector NAVAS) - d log (P_c/P_t) = ā_{c} (β̄_ā)^{-1} (text-preserved relation indicating valuation effect increasing in commodity sector NAVAS) - Interpretation: - For a given economy-wide “average NAVAS” (β̄_ā), the income effect is decreasing in the commodity sector NAVAS, whereas the valuation (wealth) effect is increasing in the commodity sector NAVAS. ### Cross-country heterogeneity in consumption responses (Section 2) - For a given commodity sector NAVAS (푎...), differences in economy-wide “average NAVAS” (훽...) can explain why consumption reacts differently in economies with similar commodity NAVAS. - Empirical pattern: countries similar in commodity sector NAVAS may still display heterogeneous responses to commodity terms of trade shocks because of differences in average NAVAS. - Data note: there is greater cross-country heterogeneity in commodity NAVAS than in average NAVAS; thus commodity sector NAVAS is the primary driver for cross-country heterogeneity in consumption responses to terms of trade shocks in the model. ### Implications for monetary policy — extensions and intuition - Qiu and others (2025) extend Silva and others (2024) by incorporating sticky prices and endogenous labor supply and assume households do not save nor borrow, eliminating the intertemporal wealth effect. - Following Rubbo (2023), Qiu and others (2025) derive new optimal sectoral inflation weights to rebase the CPI; stabilizing this newly rebased price index following a commodity price shock is optimal to the extent that it closes the output gap. - Key difference from closed-economy result (Rubbo 2023): in a small open economy sectoral imports and exports intensities matter for how much a central bank should worry about inflation originating in a particular sector. ### Network-adjusted weight (NAW) — definition and components (symbols preserved) - High-level definition presented in the source (symbols preserved in original form). - Interpretation of the three terms: - First term (휆...): measures how sectoral wedges (due to sticky prices) increase the domestic aggregate price index and reduce the real wage, reducing labor supply and the output gap; channel is stronger when the sector is an important supplier of intermediates to domestic sectors. - Second term (휅... ⋅ ρ...): the expenditure-switching channel; increases with the NAVAS of the commodity sector and the NAVAS of the commodity sector customers, because changes in relative prices of domestic to foreign goods generate stronger domestic factor demand and create a positive output gap. A sector with high NAVAS and/or customers with high NAVAS will have a larger weight in monetary policy. - Third term (휅... 휆... (1 − NAVAS) ...): the profit-channel; describes how negative sectoral markup wedges—generated when firms cannot adjust prices even with sectoral inflation—increase domestic sectors’ costs of imported inputs relative to sectoral sales, decreasing domestic producers’ profits and contributing negatively to the aggregate output gap. ### Monetary policy mistake — formal definition and channels (symbols preserved) - Monetary policy mistake defined (expression preserved in original symbolic form). - Interpretation of terms in the mistake expression: - First term emphasizes that low NAVAS implies more reliance on imported intermediates, reducing the sector’s importance in driving the domestic aggregate output gap. - Second term reflects the export intensity of sector i, represented by the ratio of foreign network-adjusted supplier centrality divided by the Domar weight. When export intensity is high, most of that sector’s inflation ends up abroad, reducing domestic relevance. - Third term is an expenditure-switching channel where higher NAVAS amplifies reallocation of spending from foreign to domestic goods, increasing the sector’s importance for monetary policy design. *International Monetary Fund | October 2025 — ch1onlineannex* ### Section 1 ### ch1onlineannex - Section 1 ### Part I. Network-Adjusted Value-Added Share (NAVAS) - NAVAS definition and interpretation - NAVAS captures both upstream linkages (commodity sector as purchaser of inputs) and downstream linkages (commodity sector as supplier to other industries). - NAVAS is defined as the weighted sum of the value-added shares (a) of all domestic sectors that supply intermediates to the commodity sector, directly or indirectly, and imported intermediates. - Commodity sector (N+1) NAVAS formula (text description preserved): - NAVAS_{N+1} = Σ Ψ_{N+1,i} a_i - Ψ = (I − Γ)^{-1} is the Leontief inverse matrix; Γ is the input-output (IO) matrix with element Γ_{i,j} referring to the share of inputs from sector j used by sector i as a fraction of sector i’s gross output. - If Γ_{N+1,i} = 0 for all i (commodity sector buys no domestic intermediates), then NAVAS_{N+1} = a_{N+1}. - Illustrative three-sector examples (C = commodity, A, B) - Example values and vector of value-added shares: - a = (0.3, 0.3, 0.6)′ (ordering: C, A, B) - When C buys no domestic intermediates, NAVAS_C = a_C = 0.3 regardless of C’s supplier centrality. - Leontief inverse matrices shown (preserved exactly): - Ψ_1 = 1 0 0 0.28571 0.2857 0.2857 0.14290 0.1429 0.1429 - Ψ_2 = 1 0 0 0.47061 1.1176 0.1176 0.23530 0.05881 0.0588 - Example 2 (different supplier centrality, same customer centrality) - IO matrices: - Γ_3 = 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 - Γ_4 = 0.2 0.2 0.2 0.25 0.2 0.2 0.15 0.1 0.1 - Total domestic intermediate share = 0.6; imported intermediate input share = 0.1. - Resulting NAVAS: 0.81 for Γ_4 versus 0.78 for Γ_3. - Example 3 (different customer centrality, similar supplier centrality) - IO matrices: - Γ_5 = 0.1 0.1 0.4 0.2 0.2 0.2 0.1 0.1 0.1 - Γ_6 = 0.1 0.4 0.1 0.2 0.2 0.2 0.1 0.1 0.1 - NAVAS increases from 0.77 to 0.80 when the commodity sector allocates a larger share of purchases toward the more labor-intensive supplier (sector B). ### Part II. Empirics - Data and sample - Unbalanced annual panel of 66 countries (37 advanced economies (AE) and 29 emerging and developing economies (EMDE)). - Sample spans from 1990 to 2018 (extended through 2023 where data permit). - Primary data sources: - IMF Commodity Terms of Trade database — Commodity Net Export Price Index (weighted by net exports as a share of GDP), using rolling windows to capture time-varying composition of commodity trade. - Global Macro Database (GMD, Müller and others, 2025) — macro indicators and controls; all variables denominated in U.S. dollars. - OECD Input-Output Tables (IOT), 2018 edition — used to capture interlinkages and compute NAVAS. - Descriptive statistics (2018) — reported exactly as in source - Advanced Economies (table layout preserved as numeric entries by category Energy / Metals / Agriculture / Aggregate for Size and NAVAS): - Mean: 0.05 0.04 0.04 0.13 0.50 0.61 0.78 0.61 - Median: 0.04 0.04 0.04 0.13 0.48 0.60 0.78 0.62 - Standard Deviation: 0.05 0.03 0.03 0.06 0.23 0.13 0.07 0.15 - Min: 0.00 0.00 0.00 0.04 0.21 0.21 0.55 0.22 - Max: 0.23 0.11 0.12 0.31 0.94 0.88 0.90 0.89 - Emerging Markets (table layout preserved as numeric entries by category Energy / Metals / Agriculture / Aggregate for Size and NAVAS): - Mean: 0.14 0.08 0.15 0.39 0.66 0.73 0.86 0.80 - Median: 0.10 0.05 0.11 0.32 0.67 0.77 0.88 0.83 - Standard Deviation: 0.18 0.06 0.14 0.22 0.21 0.16 0.08 0.12 - Min: 0.00 0.01 0.01 0.12 0.23 0.37 0.65 0.50 - Max: 0.95 0.23 0.74 1.05 0.98 0.95 0.96 0.97 - Empirical strategy - Local Projection regression (Jordà, 2005) for cumulative change in detrended log real consumption c_{i,t+h} − c_{i,t−1}: - Specification (preserved symbols and structure): - c_{i,t+h} − c_{i,t−1} = α_h + μ_i + Σ_{s=0}^{h} δ_{h,s} Δx_{t−s} + β_h(ε_{i,t}) + β_h^{(1)}(ε_{i,t} × NAVAS_i) + γ_h(ε_{i,t} × size_i) + u_{i,t+h} - NAVAS = country i's network-adjusted value-added share of the commodity sector. - size = Domar weight of the commodity sector. - x = vector of controls including lags of the dependent variable and lags of the shock. - Country fixed effects μ_i included. - Shock ε_{i,t} identified following Schmitt-Grohe and Uribe (2018): residual from an AR(1) fitting log commodity price changes; residuals display white-noise properties (confirmed by Ljung-Box statistic). - Gomez-Gonzalez and others (2025) provide IV robustness. - Key empirical findings - NAVAS amplifies consumption responses to commodity price shocks: - Online Annex Figure 1.1.2 shows cumulative impulse responses of consumption following a one-standard-deviation shock in commodity prices for economies at the 10th and 90th percentiles of the NAVAS distribution. - Countries in the 90th percentile show an overall positive consumption response; countries in the 10th percentile show a negative response (NAVAS normalized by its cross-country sample mean). - NAVAS is a robust driver of cross-country heterogeneity in consumption responses. - Commodity sector size has a smaller and statistically weaker interaction effect with commodity price shocks than NAVAS (size is less relevant than network position). ### Part III. Model - Model framework - Based on multi-sector dynamic small-open economy models of Silva and others (2024) and Gomez-Gonzalez and others (2025). - Agents and structure: - Households maximize lifetime discounted utility of consumption (preserved symbolic form). - Households face budget constraint including aggregate price P_t, aggregate consumption C_t, foreign asset position B_t denominated in units of the commodity good, bond holding adjustment cost g(B_t) = (B - B̄)^2 / B̄^2 (text-preserved functional form indicated in source), wage W_t, interest rate r, labor supply L fixed. - Given aggregate consumption, households allocate consumption across goods minimizing costs with sectoral consumption shares β_i and sectoral price indices P_i. - Production: - Each sector i produces using labor and a bundle of intermediates (domestic and imported); production function preserved symbolically: - Q_i/Q̄_i = Z_i [ a_i (L_i/L̄_i)^{(σ_i−1)/σ_i} + (1−a_i)(M_i/M̄_i)^{(σ_i−1)/σ_i} ]^{σ_i/(σ_i−1)} - Intermediate bundles: imported vs domestic intermediates elasticity ε_i; domestic intermediate bundle constructed from M_{i,j} with expenditure shares ω_{i,j} and substitution elasticity ε̄_{i}. - Exogenous processes: - log P_{c,t}^* = ρ_{c} log P_{c,t−1}^* + ε_{c,t} - log Z_{i,t} = ρ_Z log Z_{i,t−1} + ε_{i,t} - Market clearing conditions preserved for goods, labor, and accumulation of net foreign assets (NFA) with exact symbolic representation as in the source. - Calibration and solution - Calibrated using OECD input-output data as in Part II. - Model has 38 non-commodity sectors and one commodity sector (N+1) that aggregates six OECD commodity sectors into one in the model. - Calibrated to match each country’s sectoral final consumption shares, IO shares, and the commodity sector’s net exports (all 2018). - Other parameters (discount rate, elasticities, shock persistence/volatility) taken from Silva and others (2024). - Solved using first-order log-linear approximation around the non-stochastic steady state. - Income and wealth effects — mechanism and role of NAVAS - Aggregate consumption expression (preserved symbolic form) links consumption to foreign assets, valuation effects, and real wage. - Global commodity price shocks influence consumption via: - Income effect through real wage (W_t/P_t). - Valuation effect of net foreign assets captured by relative commodity price (P_{c}/P), a proxy for terms of trade. - Adjustments in net foreign assets. - Role of NAVAS in these channels (preserved expressions): - d log (W_t/P_t) = (1 − β̄_ā)^{-1} ā_{c} (text-preserved relation indicating income effect decreasing in commodity sector NAVAS) - d log (P_c/P_t) = ā_{c} (β̄_ā)^{-1} (text-preserved relation indicating valuation effect increasing in commodity sector NAVAS) - Interpretation: - For a given economy-wide “average NAVAS” (β̄_ā), the income effect is decreasing in the commodity sector NAVAS, whereas the valuation (wealth) effect is increasing in the commodity sector NAVAS. *International Monetary Fund | October 2025 — ch1onlineannex - Section 1* ### Section 2 ### ch1onlineannex - Section 2 ### Cross-country heterogeneity in consumption responses - For a given commodity sector NAVAS (푎 ேାଵ ෧), differences in economy-wide “average NAVAS” (훽 ᇱ 푎෤) can explain why consumption reacts differently in economies with similar commodity NAVAS. - Empirical pattern: countries similar in commodity sector NAVAS may still display heterogeneous responses to commodity terms of trade shocks because of differences in average NAVAS. - Data note: there is greater cross-country heterogeneity in commodity NAVAS than in average NAVAS; thus commodity sector NAVAS is the primary driver for cross-country heterogeneity in consumption responses to terms of trade shocks in the model. ### Implications for monetary policy — model extensions and intuition - Qiu and others (2025) extend Silva and others (2024) by incorporating sticky prices and endogenous labor supply and assume households do not save nor borrow, eliminating the intertemporal wealth effect. - Following Rubbo (2023), Qiu and others (2025) derive new optimal sectoral inflation weights to rebase the CPI; stabilizing this newly rebased “divine coincidence” price index following a commodity price shock is optimal to the extent that it closes the output gap. - Key difference from closed-economy result (Rubbo 2023): in a small open economy sectoral imports and exports intensities matter for how much a central bank should worry about inflation originating in a particular sector. ### Definition and interpretation of the network-adjusted weight (푁퐴푊 ௜ ) - High-level definition presented in the source: 푁퐴푊 ௜ ൌλ ஽,ప ෪ ด ஼௉ூ ௖௛௔௡௡௘௟ ൅κ ௌ ⋅ρ ாௌ,ప ෧ ᇣ ᇧᇤᇧᇥ ா௫௣௘௡ௗ௜௧௨௥௘ ௌ௪௜௧௖௛௜௡௚ െκ ௌ λ ௜ ሺ 1െNAVAS ୧ ሻ ᇣ ᇧᇧᇧᇧᇧᇤᇧᇧᇧᇧᇧᇥ ௉௥௢௙௜௧ ஼௛௔௡௡௘௟ - Interpretation of the three terms: - First term, 휆 ஽,ప ෪ : measures how sectoral wedges (due to sticky prices) increase the domestic aggregate price index and reduce the real wage, reducing labor supply and the output gap; channel is stronger when the sector is an important supplier of intermediates to domestic sectors. - Second term, 휅 ௌ ⋅휌 ாௌ,ప ෧: the expenditure-switching channel; increases with the NAVAS of the commodity sector and the NAVAS of the commodity sector customers, because changes in relative prices of domestic to foreign goods generate stronger domestic factor demand and create a positive output gap. A sector with high NAVAS and/or customers with high NAVAS will have a larger weight in monetary policy. - Third term, 휅 ௌ 휆 ௜ ሺ 1െNAVAS ୧ ሻ : the profit-channel; describes how negative sectoral markup wedges—generated when firms cannot adjust prices even with sectoral inflation—increase domestic sectors’ costs of imported inputs relative to sectoral sales, decreasing domestic producers’ profits and contributing negatively to the aggregate output gap. ### Monetary policy mistake: formal definition and channels - Monetary policy mistake defined as: ቀ ఒ ೔ ିே஺ௐ ೔ ఒ ೔ ቁൌ휅 ௦ ሺ 1െ푁퐴푉퐴푆 ௜ ሻ ൅ ఒி ഢ ෩ ఒ ೔ െ휅 ௦ ሺ ఘ ಶೄ,ഢ ෧ ఒ ೔ ሻ. - Interpretation of terms in the mistake expression: - First term, 휅 ௦ ሺ 1െ푁퐴푉퐴푆 ௜ ሻ : highlights that domestic producers rely on labor, domestic inputs, and imported inputs which free up domestic factors; low NAVAS implies more reliance on imported intermediates, reducing the importance of that sector in driving the domestic aggregate output gap. - Second term, 휆 ி ෨ ೔ : reflects the export intensity of sector i, represented by the ratio of foreign network-adjusted supplier centrality (the interaction of sectoral export shares and the Leontief inverse for the sector as a supplier, ఒి ഢ ෩ ఒ ೔ ) divided by the Domar weight. When export intensity is high, most of that sector’s inflation ends up abroad, reducing domestic relevance. - Third term, 휅 ௦ ሺ ఘ ಶೄ,ഢ ෧ ఒ ೔ ሻ: an expenditure-switching channel where 휌෤ ாௌ,ప ෫ measures how deflation in sector i leads to reallocation of spending from foreign to domestic goods (directly and indirectly) as consumer of domestic labor; higher NAVAS amplifies this channel, increasing the sector’s importance for monetary policy design. *International Monetary Fund | ch1onlineannex - Section 2* --- _Source: https://www.imf.org/-/media/files/publications/weo/2025/october/english/ch1onlineannex.pdf_