## _wp16219 - 1.      Domestic demand is shifting away

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---

### Three developments driving China’s rebalancing
- Domestic demand is shifting away from investment and towards consumption as Chinese households become wealthier and their disposable income rises.
- On the production side, services are outgrowing manufacturing.
- Within industry, lower tech sectors are growing less than high tech; China is moving up the value chain.

### Key quantitative findings and comparative impacts
- Model coverage: 41 economies × 34 sectors (calibrated to WIOD).
- China’s move up the value chain is the single most relevant development, generating the largest adverse spillovers both in absolute terms and relative to the original shock in China.
- Spillovers to Taiwan, Province of China and Korea’s welfare can be as large as -¾ of the impact of the shock on China itself.
- China’s shift towards greater final consumption entails large adverse effects to major economies (defined by largest trade exposure to China measured as half the sum of exports and imports to/from China), although some lower-to-middle-income economies may benefit.
- Productivity rebalancing towards services has limited spillovers relative to the large adverse impact within China itself.
- China is the largest exporter in the world and second largest importer; as a central node in the world trade network, rebalancing reverberates beyond first-round effects and need not be accompanied by a considerable slowdown in China to have global impacts.

### Modeling approach and scope
- Framework: Multi-country, multi-sector Ricardian trade model (based on Caliendo and Parro (2015)) extended to allow preference shocks and fundamental productivity shocks.
- Calibration and data:
  - Base year: 2011 (WIOD November 2013 update used; 34 sectors after excluding one sector with missing values).
  - Sectors: 34 sectors (2-digit ISIC Revision 3 mapping).
  - Trade treatment: because tariff data is not available for services, zero exports are imposed from those sectors yielding 17 traded sectors in calibration.
- Core shock types modeled:
  - Preference shocks ('j n α)
  - Supply shocks (j n λ)
  - Income shocks (n D′)
- Counterfactuals solved in hatted changes (changes relative to base year), avoiding estimation of some parameter levels.

### Mechanisms captured and welfare accounting
- Model captures endogenous reconfiguration of global trade networks and sectoral bilateral expenditure-share changes (Fréchet-distributed idiosyncratic productivity).
- Welfare (real income) decomposition includes contributions from:
  - Terms of trade changes,
  - Trade volume (tariff-related income effects),
  - Direct contribution of supply shocks,
  - Direct impact of changes in trade balance,
  using the explicit log-change decomposition (equation (16) and related expressions).

### Calibration details and network characterization
- Main data source: WIOD (2011).
- Network representation: global network of sectoral interlinkages; bilateral connections measured as average of inputs and outputs between sectors.
- Visualization insight: China sits at center of Asia’s supply chain; Japan and Korea link with the United States; Mexico ties mainly to the U.S.; Australia and Indonesia are mostly connected to China.
- Trade elasticities: where missing, manufacturing mean of 8.22 is used; selected sector elasticities (as used in calibration) preserved exactly where reported:
  - AtB — 9.11
  - C — 13.53
  - 15t16 — 2.62
  - 17t18 — 8.1
  - 23 — 64.85
  - 21t22 — 16.52
  - 24 — 3.13
  - 25 — 1.67
  - 29 — 1.45
  - 30t33 — 8.22

### Counterfactual experiments mapped to shocks
- 1) Move from investment to consumption → preference shocks in China ('jCHN α).
  - Implemented by assuming Chinese consumer preferences change to match U.S. consumer preferences in the base year: 'jCHN = jUSA.
  - Implies average Chinese consumes more nontraded goods (notably excluding construction) and less traded goods, particularly machinery, transport and electrical equipment.
- 2) Production-side shift towards services → modeled as productivity/sectoral composition shocks favoring services.
- 3) Within-industry move up the value chain → modeled as productivity reallocation favoring high-tech sectors over low- and medium-tech sectors (technology-intensity classification used).

### Appendix experiment A — Preference shock towards consumption in China (large assumed shock; Appendix figure A2)
- Key impacts on incomes and wages (selected results preserved exactly):
  - China real income improves by 0.30 percent.
  - Taiwan, Province of China real income drops by 0.47 percent.
  - Korea real income drops by 0.25 percent.
  - China’s total exports contract 9.46 percent.
- Channels and decomposition:
  - Most income effects attributed to terms-of-trade changes: China sees an improvement; other economies see deterioration (equation (16) decomposition).
  - Trade volumes contribute negatively to China because of a net decrease in Chinese imports and a fall in tariff income.
- Trade and sectoral patterns:
  - China cuts back imports from all major economies as consumers shift from traded to nontraded goods.
  - Chinese exports to major economies are smaller because trade balances are kept constant in the exercise; China’s exports contract across all traded sectors.
  - Major economies’ exports change heterogeneously by sector: Korea, Japan and Taiwan, Province of China see relatively better performance in petroleum, paper and wood products; machinery nec and minerals fare worse.
- Cross-country distribution:
  - Low-to-middle-income economies on average seem to benefit (particularly those in southern and southeastern Europe); some other regions do not (examples: Brazil, Indonesia).
  - Total exports are less affected than exports to China, indicating export substitution away from China.
- Contextual benchmark:
  - Final consumption share in China was around 50 percent in 2011 versus about 85 percent for the US.

### Appendix experiment B — Productivity shift: services up 1% TFP, traded sectors down 1% TFP
- Scenario mapping: positive TFP shock of 1% to services (nontraded) and negative shock of 1% to traded sectors (TFP shocks mapped using equation (20)).
- Aggregate income and spillovers (selected results preserved exactly):
  - Net impact on Chinese real income/welfare: -0.6 percent.
  - Taiwan, Province of China real income increases by 0.04 percent.
  - Korea real income increases by 0.04 percent.
  - Australia real income declines by 0.04 percent.
  - China’s exports diminish by -1.67%.
- Mechanisms:
  - Negative: lower productivity in traded sectors reduces China’s competitiveness and harms economies proportional to import dependence on China.
  - Positive: China vacates some markets improving other economies’ terms-of-trade.
  - For Korea and Taiwan, Province of China the terms-of-trade improvement dominates; for Australia the loss of competitiveness of Chinese traded sectors dominates.
- Trade volumes:
  - All economies see reductions in bilateral trade with China, particularly imports from China; exports to China decline but are less affected than imports for most major economies except Australia.
- Robustness:
  - Targeting productivity declines in oversupplied sectors (e.g., construction and base metals) yields similar qualitative spillovers but more heterogeneous sectoral impacts.

### Appendix experiment C — China moves up the value chain (±1% TFP reallocation)
- Scenario: +1% TFP in high-tech industry sectors and -1% in low-tech industry sectors in China (technology-intensity classification; Appendix table 4).
- Income and country-specific effects (selected results preserved exactly):
  - China income increases by 0.11 percent.
  - Korea income decreases by 0.08 percent.
  - Taiwan, Province of China income decreases by 0.08 percent.
  - Australia income increases by 0.06 percent.
  - China’s exports to China reported as n.a. in Table A9; China’s overall export changes in Table A9: Exports to China n.a.
- Mechanisms:
  - Direct impact: imports of inputs from China become on net cheaper for some economies.
  - Competitive displacement: China becomes competitive in industries previously occupied by Korea and Taiwan, Province of China, causing declines in those economies’ terms-of-trade.
  - Australia benefits from lower import prices and complementarity — large surge in its exports to China (Exports to China for Australia = 5.45 in Table A9).
- Sectoral patterns:
  - Lower-tech sectors in major economies increase exports (examples: food and beverages, textiles).
  - Higher-tech sectors decline (examples: machinery, electronics).
  - China increases export shares in tech-intensive industries like transport, electric equipment and chemicals, and decreases shares in lower-tech sectors (Appendix Table A10).
- Magnitude and nonlinearity:
  - Preference shock absolute effects were about three times larger than the ±1% TFP move-up-the-chain shock.
  - Simulating larger shocks: a 3 percent TFP change generating China moving up the value chain produces effects on incomes equivalent to the preference shock.
  - Spillovers are nonlinear: elasticity of other economies’ income spillovers relative to China declines as shock size increases.
    - Example elasticities (Figure 7 right panel): approximately 0.5 for Australia and -0.75 for Taiwan, Province of China, with elasticity declining as shock size grows.
  - Inferring effects of large shocks from small-shock elasticities likely overestimates spillovers.

### Appendix: Selected quantitative outcomes (exact table excerpts and values)
- Table A5 — “China preference shock” (selected entries, values in percentage points):
  - China: Income Total 0.30, Exports 0.42, Real wage -0.13, Exports to China n.a.
  - Korea: Income Total -0.25, Exports -0.21, Real wage -0.04, Exports to China -2.09
  - Taiwan POC: Income Total -0.47, Exports -0.47, Real wage 0.00, Exports to China -9.16
  - USA: Income Total -0.03, Exports -0.03, Real wage 0.00, Exports to China -16.97
- Table A7 — “Traded to nontraded productivity shock” (selected entries):
  - China: Income Total -0.62, Exports -0.15, Real wage -0.01, Exports to China -1.67
  - Korea: Income Total 0.04, Exports 0.11, VoT 0.01, Direct -0.09, Real wage 0.01, Exports to China 0.34
  - Taiwan POC: Income Total 0.04, Exports 0.15, VoT 0.00, Direct -0.10, Real wage 0.00, Exports to China -0.07
- Table A9 — “China move up the value chain shock” (selected entries):
  - China: Income Total 0.11, ToT 0.00, VoT -0.01, Direct 0.11, Real wage 0.11, Exports to China n.a.
  - Australia: Income Total 0.06, ToT 0.05, VoT 0.00, Direct 0.01, Real wage 0.05, Exports to China 5.45
  - Russia: Income Total -0.01, ToT 0.00, VoT 0.00, Direct -0.01, Real wage 0.00, Exports to China 8.45
  - Taiwan POC: Income Total -0.08, ToT -0.15, VoT 0.00, Direct 0.07, Real wage -0.04, Exports to China -0.79

### Heterogeneity of impacts and robustness caveats
- Spillovers vary by:
  1. Shock driving rebalancing (preferences, supply/TFP reallocation, income),
  2. Receiving economy,
  3. Sector within the receiving economy.
- Model assumptions likely bias spillover magnitudes:
  - Focus solely on trade channel; financial channel omitted (could amplify shocks).
  - Abstracts from dynamics and transition costs; assumes full employment before and after shocks (wages fully flexible) and no dynamic productivity adjustments.
  - Assumes no change in trade costs in counterfactuals (0 k in τ).
  - Excludes trade in services (zero exports from services sectors) — could mitigate adverse goods-trade spillovers but unlikely to fully offset them because services trade is an order of magnitude smaller than goods trade and there is strong complementarity between goods and services trade.
- Nonlinearity: reported spillovers are conditional on shock size; model is non-linear and elasticities do not scale linearly with shock size.

### Concluding remarks and policy implications
- The multi-country, multi-sector trade model assesses rebalancing spillovers via goods trade and value-chain reconfiguration with endogenous comparative advantage.
- The analysis complements approaches that focus on financial spillovers or dynamic macro channels; those other channels are not captured here and could amplify spillovers.
- Results are counterfactual and conditional on no other responses; actual spillovers depend on concurrent global developments and policy responses.
- Policy takeaway: policy responses to large shocks originating in China should be expected and would influence final spillovers; the paper’s estimates evaluate the isolated effects of specified shocks to inform understanding of potential channels and magnitudes.

*Source: _wp16219 - 1.      Domestic demand is shifting away (IMF working paper content as provided).*

### 1.      Domestic demand is shifting away

### _wp16219 - 1.      Domestic demand is shifting away

### Three developments driving China’s rebalancing
- Domestic demand is shifting away from investment and towards consumption as Chinese households become wealthier and their disposable income rises.
- On the production side, services are outgrowing manufacturing.
- Within industry, lower tech sectors are growing less than high tech; China is moving up the value chain.

### Key quantitative findings and comparative impacts
- The calibrated model includes 41 economies, each consisting of 34 sectors, allowing rich spillover predictions across income and trade and within-sector performance.
- China’s move up the value chain is quantitatively the most relevant development, generating the largest adverse spillovers both in absolute terms and relative to the original shock in China.
- Spillovers from China’s move up the value chain are particularly adverse for economies heavily involved in the Asia value chain: Taiwan, Province of China, Korea, and to a lesser extent Japan and Germany.
- Spillovers to Taiwan, Province of China and Korea’s welfare can be as large as -¾ of the impact of the shock on China itself.
- China’s shift towards greater final consumption entails large adverse effects to major economies (as defined by largest trade exposure to China measured as half the sum of exports and imports to/from China), although some lower-to-middle-income economies may benefit.
- Productivity rebalancing towards services has limited spillovers, particularly relative to the large adverse impact within China itself.
- China is now the largest exporter in the world and second largest importer; as a central node in the world trade network, rebalancing can reverberate beyond first-round effects and need not be accompanied by a considerable slowdown in China to have global impacts.

### Modeling approach and scope
- Framework: Multi-country, multi-sector Ricardian trade model (based on Caliendo and Parro (2015)) extended to allow preference shocks and fundamental productivity shocks.
- Model coverage and structure:
  - Calibrated to a base year using World Input-Output Dataset (WIOD), November 2013 update.
  - Features 34 sectors (sectors defined at 2-digit ISIC Revision 3) and captures intermediate and final goods production and input-output linkages.
  - In calibration to WIOD 2011 the model features 40 economies and a “Rest of the World” block each composed of 34 sectors; because tariff data is not available for services, zero exports are imposed from those sectors, yielding 17 traded sectors.
- Core shock types modeled:
  - Preference shocks ('j n α)
  - Supply shocks (j n λ)
  - Income shocks (n D′)
- Counterfactuals are solved in changes relative to the base year (hatted changes), avoiding the need to estimate levels of some parameters.

### Mechanisms captured and welfare accounting
- The model captures:
  - Changes in bilateral sectoral expenditure shares (equation form using Fréchet-distributed idiosyncratic productivity).
  - Endogenous reconfiguration of global trade networks and value chains when shocks are applied.
- Welfare (real income) impacts are decomposed into contributions from:
  - Terms of trade changes,
  - Trade volume (tariff-related income effects),
  - Direct contribution of supply shocks,
  - Direct impact of changes in trade balance (income via aggregate deficit changes),
  using the explicit log-change decomposition presented in the model (equation (16) and related expressions).

### Calibration details and network characterization
- Main data source: WIOD (World Input-Output tables), harmonized across time and economies; 35 industries in WIOD with one sector excluded due to missing values (resulting model sectors = 34).
- Base year for calibration: 2011 (latest WIOD year used).
- Network representation:
  - Model constructs a global network of sectoral interlinkages; bilateral connections measured as the average of inputs and outputs between sectors.
  - Visualization of East Asia and North America traded sectors (subsample) shows China at the center of Asia’s supply chain; Japan and Korea link with the United States; Mexico is mostly tied to the U.S.; Australia and Indonesia are mostly connected to China.

### Counterfactual experiments: mapping rebalancing dimensions to shocks
- Rebalancing dimensions mapped into model shocks:
  1. Move from investment to consumption → preference shocks in China ('jCHN α).
     - Preference shock scenario implemented by assuming Chinese consumer preferences change to match U.S. consumer preferences in the base year: 'jCHN = jUSA.
     - This shock implies the average Chinese consumes more nontraded goods (notably excluding construction) and less traded goods, particularly machinery, transport and electrical equipment.
  2. Production-side shift towards services → modeled as productivity/sectoral composition shocks favoring services.
  3. Within-industry move up the value chain (towards higher tech sectors) → modeled as productivity reallocation favoring high-tech sectors over low- and medium-tech sectors.

### Heterogeneity of impacts and robustness caveats
- Spillovers are highly heterogeneous depending on:
  1. The shock driving rebalancing (preferences, supply/TIPF reallocation, income),
  2. The receiving economy,
  3. The sector within that economy.
- Model simplifying assumptions that likely bias spillover magnitudes:
  - Focuses solely on trade channel; financial channel omitted (could amplify shocks).
  - Abstracts from dynamics and transition costs; assumes full employment before and after shocks (wages fully flexible) and no dynamic productivity adjustments (which would amplify spillovers).
  - Assumes no change in trade costs in counterfactuals (0 k in τ).
  - Excludes trade in services (zero exports from services sectors) — this could mitigate some adverse spillovers in goods trade but is unlikely to fully offset them in the medium term given services trade is an order of magnitude smaller than goods trade and strong complementarity between goods and services trade.
- Nonlinearity note: Spillovers reported are conditional on the size of shocks used; because the model is non-linear, elasticities are relative and spillovers may not scale linearly with shock size.

### Section roadmap (as presented)
- Section II: Ricardian model details and derivations.
- Section III: Data and calibration.
- Section IV: Simulations of shocks originating in China and effects on selected economies.
- Section V: Concluding remarks.

*Source: _wp16219 - 1.      Domestic demand is shifting away (IMF working paper content as provided).*

### Appendix table 5 has the full set of results for all economies.

### _wp16219 - Appendix table 5 has the full set of results for all economies.

### Preference shock towards consumption in China
- Scenario: a preference shock towards consumption in China (assumed large; Appendix figure A2 shows assumed shock).
- Key impacts on incomes and wages:
  - Chinese real income improves by 0.30 percent.
  - Taiwan, Province of China real income drops by 0.47 percent.
  - Korea real income drops by 0.25 percent.
  - Real wages show a similar pattern to real income.
- Channels and decomposition:
  - Using equation (16), most of the effects on income are attributed to changes in the terms-of-trade: China sees an improvement; other economies see deterioration.
  - Trade volumes contribute negatively to China because the scenario implies a net decrease in Chinese imports, leading to a fall in tariff income.
- Trade patterns:
  - China cuts back imports from all major economies as consumers shift from traded to nontraded goods (nontraded goods are much less import intensive).
  - Chinese exports to major economies are also smaller because trade balances are kept constant in the exercise.
  - China’s exports contract across all traded sectors.
  - Major economies’ exports change heterogeneously by sector: Korea, Japan and Taiwan, Province of China see relatively better performance in petroleum, paper and wood products; machinery nec and minerals fare worse.
- Cross-country and sectoral outcomes:
  - Low-to-middle-income economies on average seem to benefit, particularly those in southern and southeastern Europe; some other regions do not (examples: Brazil, Indonesia).
  - Exports to China contract significantly in general, but total exports are less affected — indicating export substitution away from China.
  - China’s own total exports contract 9.46 percent.
- Contextual numbers:
  - Final consumption share in China was around 50 percent in 2011 versus about 85 percent for the US.

### Development 2: Productivity of service sectors increases while it decreases in industry
- Scenario: a positive TFP shock of 1% to services (nontraded sectors) and a negative shock of 1% to traded sectors (TFP shocks mapped using equation (20)).
- Aggregate income and spillovers:
  - Net impact on Chinese real income/welfare: -0.6 percent.
  - Taiwan, Province of China real income increases by 0.04 percent.
  - Korea real income increases by 0.04 percent.
  - Australia real income declines by 0.04 percent.
- Mechanisms producing limited spillovers:
  - Negative effect: China becoming less productive in traded sectors harms all economies proportionally to their import dependence on China (red bars in Figure 5, top right).
  - Positive effect: China vacates some markets and improves terms-of-trade for other economies (blue bars in Figure 5, top right).
  - For Korea and Taiwan, Province of China the terms-of-trade improvement dominates; for Australia the loss of competitiveness of Chinese traded sectors dominates.
- Trade volume and sectoral patterns:
  - All economies see reductions in bilateral trade with China, particularly imports from China.
  - Exports to China decline but are less affected than imports for most major economies except Australia.
  - China’s traded sectors contract; Mining and Agriculture in China expand for export as some domestically used production is shipped abroad.
  - China’s exports diminish by -1.67% under this scenario.
- Distributional outcomes:
  - Major manufacturing economies benefit relatively more than others; Korea and Taiwan, Province of China record the largest gains in real income and smallest decreases in exports to China (despite pricier imports from China).
- Robustness and alternatives:
  - Targeting productivity declines in particular oversupplied sectors (e.g., construction and base metals) yields similar qualitative spillovers but more heterogeneous sectoral impacts (Appendix Figure A3).

### Development 3: China moves up the value chain
- Scenario: a positive TFP shock of 1 percent in high-tech industry sectors and a simultaneous negative shock of 1 percent in low-tech industry sectors in China (technology-intensity classification used; see Appendix table 4 for sector classifications).
- Income and country-specific effects:
  - China’s income increases by 0.11 percent.
  - Korea’s income decreases by 0.08 percent.
  - Taiwan, Province of China’s income decreases by 0.08 percent.
  - Australia’s income increases by 0.06 percent.
  - Effects for all economies reported in Appendix table A9.
- Spillover mechanisms:
  - Direct impact: imports of inputs from China become on net cheaper for major economies (red bars, Figure 6, top right).
  - Competitive displacement: China becomes competitive in industries previously occupied by Korea and Taiwan, Province of China, producing sharper declines in those economies’ terms-of-trade.
  - Australia benefits from lower prices on imports and complementarity — large surge in its exports to China.
- Sectoral export patterns:
  - Lower-tech sectors in major economies increase exports (examples: food and beverages, textiles).
  - Higher-tech sectors decline (examples: machinery, electronics).
  - China increases export shares in tech-intensive industries like transport, electric equipment and chemicals, and decreases shares in lower-tech sectors (Appendix Table A10).
- Cross-country distribution:
  - Korea and Taiwan, Province of China suffer the sharpest falls in real incomes.
  - Australia and the Rest Of the World benefit most; lower and middle income economies on average benefit (examples: Indonesia, Brazil, some economies in Southern and Southeastern Europe).
  - Export changes vary by economy depending on production concentration in higher-tech industries.
- Magnitude and nonlinearity:
  - The effects of this shock are more moderate than the preference shock in subsection A; preference shock absolute effects were about three times larger.
  - Simulating larger shocks: a 3 percent TFP change generating China moving up the value chain produces effects on incomes equivalent to the preference shock.
  - Spillovers are nonlinear: the elasticity of other economies’ income spillovers relative to China declines as shock size increases.
    - Example elasticities (right panel of Figure 7): approximately 0.5 for Australia and -0.75 for Taiwan, Province of China, with elasticity declining as shock size grows.
  - Inferring effects of large shocks from small-shock elasticities likely overestimates spillovers.

### Concluding remarks and policy implications
- Model and scope:
  - A multi-country, multi-sector trade model was used to assess spillovers from China rebalancing; model captures shifts in production value chains and goods trade patterns with endogenous comparative advantage.
  - The analysis complements approaches that focus on financial spillovers or dynamic macro channels; those other channels are not captured here and could amplify spillovers.
- Limitations and omitted channels:
  - Trade in services is assumed nontradable in the model due to lack of data on services trade costs; services could mitigate some goods-trade spillovers but would likely not fully offset them given the smaller size of services trade.
  - The results are counterfactual and conditional on no other responses; actual spillovers depend on concurrent global developments and policy responses.
- Policy takeaway:
  - Policy responses to large shocks originating in China should be expected and would influence final spillovers; the paper’s estimates evaluate the isolated effects of specified shocks to inform understanding of potential channels and magnitudes.

*Source: _wp16219 - Appendix table 5 has the full set of results for all economies.*

### References

### _wp16219 - References

### Major references cited
- Ahuja, A. and A. Myrvoda (2012), “The Spillover Effects of a Downturn in China’s Real Estate Investment,” IMF Working Paper WP/12/226.
- Ahuja, A. and M. Nabar (2012), “Investment-Led Growth in China: Global Spillovers,” IMF Working Paper WP/12/267.
- Allen, Treb, Costas Arkolakis and Yuta Takahashi, 2014, “Universal Gravity,” NBER Working Papers 20787.
- Anderson, Derek, Jorge Ivan Canales Kriljenko, Paulo Drummond, Pedro Espaillat, and Dirk Muir 2015, “The Flexible System of Global Models – FSGM,” IMF Working Paper No. 15/64.
- Caliendo, Lorenzo and Fernando Parro, 2015, “Estimates of the Trade and Welfare Effects of NAFTA,” Review of Economic Studies, vol. 82(1), pages 1-44.
- Cashin, P., K. Mohaddes, and M. Raissi, 2016, “China’s Slowdown and Global Financial Market Volatility: Is World Growth Losing Out?” IMF Working Paper No. 16/63.
- Kireyev, Alexei and Andrei Leonidov, 2016, “China’s Imports Slowdown: Spillovers, Spillins, and Spillbacks,” IMF Working Paper 16/51.
- Timmer, Marcel P., Erik Dietzenbacher, Bart Los, Robert Stehrer and Gaaitzen J. de Vries, 2015, “An Illustrated User Guide to the World Input–Output Database: The Case of Global Automotive Production,” Review of International Economics, vol. 23, pp. 575–605.
- World Bank (2016), “Global Economic Prospects”.

### Appendix: Derivations (key steps and outcomes)
- Derivation of equilibrium equation (13)
  - Combine equations (7) and (8) and define intermediate term (21).
  - Use (21) to compute changes in equation (7) and simplify through multiple summations and substitutions to arrive at equation (13).
  - The appendix shows the algebraic manipulations and cancellations leading to equation (13).

- Derivation of Changes in Income, equation (16)
  - Welfare/real income defined as W = Iw/ P (equation (22)); total differentiation yields (23).
  - Tariff revenue differentiated; with tariffs unchanged, obtains expression (24).
  - Overall price level differentiated as (25); substitution into (23) gives (26).
  - Sectoral price level differentiation in (27); substitution and reordering produce (28).
  - Labor market clearing condition: wL = sum_{j,i} E_{jni} (equation (29)).
  - Using definitions (4) and (9), algebraic rearrangements and cancellations lead to equation (16) and final expression (32).

- Key numbered equations referenced in derivations: (2), (4), (7), (8), (9), (13), (16), (21), (22), (23), (24), (25), (26), (27), (28), (29), (30), (31), (32).

### Appendix: Detailed data sources and adjustments
- Primary data source: WIOD in 2011 — used for:
  - Gross output,
  - value added coefficients (j n γ),
  - Input-output coefficients (, kj n γ),
  - Final consumption shares in China for counterfactual scenario ( ' j CHN α),
  - Bilateral exports ( j ni E).

- Adjustments to WIOD data (explicit treatments):
  1) Missing gross output data:
     - Excluded P sector due to missing data, sector has limited linkages.
     - Luxembourg sectors 19 and 23: used STAN which reports zero gross output.
     - Cyprus and Latvia sector 23: missing replaced with zero.
     - Sweden sector 19 missing after 2008: replace with value in 2008.
     - China and Indonesia sector 50 missing: assume zero following entry in IO tables.
  2) Missing value added shares for sectors listed above: replaced by mean share for that sector in that year across all economies.
  3) Tariff data only available for the first 17 sectors (up to sector E): export data for all other sectors ignored and assumed to produce nontradable goods.

- Tariff and trade elasticity data:
  - Data from UNCTAD: bilateral tariffs at 2-digit ISIC level (in ISIC rev. 3).
  - Transformations: aggregate into WIOD sectors weighting sub-sectors by import value.
  - For few missing observations: if exporting from Europe, assume average tariff an EU export faces in same sector.
  - No data for services sectors: assume missing sectors are nontraded, resulting in 17 traded and 17 nontraded sectors.

- Trade elasticities:
  - Sourced from Caliendo and Parro (2015), mapped to WIOD sectors using “99% sample” estimates.
  - If sector missing, use mean estimate for a manufacturing sector: 8.22.
  - Table excerpts (trade elasticity per WIOD sector as used in calibration):
    - AtB (Agriculture, Hunting, Forestry and Fishing) — 9.11
    - C (Mining and Quarrying) — 13.53
    - 15t16 (Food, Beverages and Tobacco) — 2.62
    - 17t18 (Textiles and Textile Products) — 8.1
    - 23 (Coke, Refined Petroleum and Nuclear Fuel) — 64.85
    - 21t22 (Pulp, Paper, Paper , Printing and Publishing) — 16.52
    - 24 (Chemicals and Chemical Products) — 3.13
    - 25 (Rubber and Plastics) — 1.67
    - 29 (Machinery, Nec) — 1.45
    - 30t33 (Electrical and Optical Equipment) — 8.22
    - (Remaining sectors largely use 8.22 where mapped or mean applied.)

- Industry production complexity and tech/factor intensity:
  - China’s move into more sophisticated industries is evaluated using tech-, factor-, complexity- and labor-intensity classifications.
  - Tech-intensity classification used as primary counterfactual; alternative classifications produce qualitatively similar results.
  - Sectors classified as “High” or “Low” sophistication per indices; example: sector 29 (Machinery, nec) tech = MH, factor = K, complexity = 1.0, labor- = 36.8 -> classified High across tech/factor/complexity.

### Appendix: Additional results — selected quantitative outcomes (values preserved exactly as in source)
- Table A5. Change in key outcomes for all economies in “China preference shock” (values in percentage points). Selected entries:
  - China: Income Total 0.30, Exports 0.42, Real wage -0.13, Exports to China n.a.
  - Korea: Income Total -0.25, Exports -0.21, Real wage -0.04, Exports to China -2.09
  - Taiwan POC: Income Total -0.47, Exports -0.47, Real wage 0.00, Exports to China -9.16
  - USA: Income Total -0.03, Exports -0.03, Real wage 0.00, Exports to China -16.97
  - Notes: Table values are presented as contiguous numbers in the source (Total ToT VoT Direct Income Real wage Exports Exports to China); values above are the identified columns mapped to Income, Total Exports, Real wage, Exports to China as in the table header.

- Table A7. Change in key outcomes for all economies in “Traded to nontraded productivity shock” (values in percentage points). Selected entries:
  - China: Income Total -0.62, Exports -0.15, Real wage -0.01, Exports to China -1.67
  - Korea: Income Total 0.04, Exports 0.11, VoT 0.01, Direct -0.09, Real wage 0.01, Exports to China 0.34
  - Taiwan POC: Income Total 0.04, Exports 0.15, VoT 0.00, Direct -0.10, Real wage 0.00, Exports to China -0.07

- Table A9. Change in key outcomes for all economies in “China move up the value chain shock” (values in percentage points). Selected entries:
  - China: Income Total 0.11, ToT 0.00, VoT -0.01, Direct 0.11, Real wage 0.11, Exports to China n.a.
  - Australia: Income Total 0.06, ToT 0.05, VoT 0.00, Direct 0.01, Real wage 0.05, Exports to China 5.45
  - Russia: Income Total -0.01, ToT 0.00, VoT 0.00, Direct -0.01, Real wage 0.00, Exports to China 8.45
  - Taiwan POC: Income Total -0.08, ToT -0.15, VoT 0.00, Direct 0.07, Real wage -0.04, Exports to China -0.79

- Sectoral export-share tables (Tables A6, A8, A10) and figures provide before/after sectoral export-share values and normalized Herfindahl indices for major economies. Example from Table A6 (China preference shock):
  - Germany (Transport sector): Before 40.34, After 40.03.
  - USA (Chemicals): Before 6.55, After 6.88.
  - Normalized Herfindahl (Germany): Before 0.181, After 0.180.

- Figures:
  - Figure A1: Exports of goods and services across major economies in 2011 in percentage points of GDP (before/after sectoral shares presented).
  - Figure A2: Shock to final demand shares in China: Initial and Final.
  - Figure A3: Productivity shock to oversupply sectors in China — chart of initial and final sectoral shares (percent).

### Data and calibration notes (explicit numeric facts)
- Tariff data available for first 17 sectors (up to sector E); rest treated as nontradable.
- Manufacturing mean trade elasticity used when sector missing: 8.22.
- WIOD year used: 2011.
- Sectoral mapping uses ISIC rev. 3 to WIOD codes (see table mappings in source).
- Complexity classification threshold: sectors considered sophisticated if complexity index is above 0.4; below 0 considered low.
- Labor-intensity classification thresholds: “High” if labor intensity < 30; “Low” if labor-intensity > 38.

*Italic: Source document — _wp16219 - References (IMF PDF content provided).*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2016/_wp16219.pdf_