## 1. The Chinese economy is undergoing a transition with policymakers putting increasing emphasis on the quality rather than the quantity of growth

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### Overview of the transition and Supply-Side Structural Reforms (SSSR)
- Years of rapid growth driven by investment and credit expansion led to structural imbalances: a large debt overhang, financial system risks, proliferation of “zombie firms”, rapid buildup of residential housing inventories, and excess capacity in certain industrial sectors.
- Annual growth declined to "6-7 percent" as China entered a "new normal."
- In December 2015 the government announced Supply-Side Structural Reforms (SSSR); SSSR was elevated as a main national strategy at the 19th Party Congress in October 2017.
- Five areas of focus under SSSR: capacity reduction, housing inventory destocking, corporate deleveraging, reducing corporate costs, and improving “weak links” (industrial upgrading, infrastructure investment, etc.). Known in Chinese as the “three cuts, one reduction, and one improvement.”
- SSSR departs from traditional demand-side stimulus and aims to improve growth quality by addressing structural imbalances; some supply-side measures include demand-boosting policies (e.g., housing destocking supported by increased support for home purchases).

### Capacity buildup, targets, and implementation (steel and coal focus)
- Post-GFC stimulus channeled more than a third of the 4-trillion-yuan package to transport and power infrastructure, spurring heavy-industry capacity expansion.
- Government subsidies (cheap land and credit, discounted power, tax breaks) provided to about 30 percent of industrial output and contributed to excess capacity.
- Steel:
  - production increased from around 100 million metric tons in 1996 to 627 million tons in 2010;
  - global share rose from 13.5 percent to 44.3 percent (Greenpeace 2017).
- Coal:
  - grew by 3.9 percent per year in 2006-15 versus global growth rate of 2.5 percent;
  - China accounted for 47 percent of global coal production in 2015 with total production of 1.8 billion tons (BP 2017).
- Capacity utilization:
  - steel contracted from 79 percent in 2010 to less than 70 percent in 2015;
  - coal declined from more than 90 percent to 65 percent.
- By end-2015, coal and steel accounted for around 14 percent of corporate debt in China’s industrial sector.
- Debt payback at current cash flow generation:
  - coal would take 91 years;
  - ferrous metal smelting (including steel) would take 74 years;
  - industrial sector average is under 10 years (EIU 2017).
- State Council targets (announced post-SSSR, 2016):
  - Steel capacity reduction target: 45 million tons within the year; within “three to five years” reduce steel capacity by 100-150 million tons (about 8-12 percent of end-2015 capacity).
  - Coal capacity reduction target: 250 million tons within the year; within “three to five years” reduce coal capacity by 1 billion tons (about 20 percent of end-2015 capacity).
  - Targets are net reductions; new capacity requires deeper cuts to existing capacity.
  - Required reduction in annual working days for all coal producers from 330 to 267, temporarily cutting coal production capacity by an additional 16 percent.
- Capacity reduction also linked to environmental protection objectives after severe pollution episodes (e.g., January 2013 “Airpocalypse”); Hebei province accounted for more than a third of planned annual steel reduction in 2016 (Greenpeace 2017).

### Observed capacity reduction progress and industry outcomes (2016-2017)
- Steel capacity curtailed by 115 million tons in 2016-17, more than 75 percent of the 5-year (2016-20) target.
- China phased out production of 140 million tons of steel made from scrap metal:
  - Scrap steel exports rose to 2.2 million tons in 2017 versus around 1,000 tons in 2016.
- Coal capacity curtailed by more than 290 million tons in 2016 and another 150 million tons in 2017.
- Number of coal mines reduced from 10,800 in 2015 to about 7,000 at end-2017.
- Capacity utilization ratios and profitability improved markedly in both industries following cuts.
- Crude steel production: grew by 1.2 percent in 2016 and 5.7 percent in 2017, indicating production remained strong despite capacity cuts.

### Key findings on drivers of PPI reflation and the role of capacity cuts
- China's PPI inflation turned positive in September 2015 after 54 months in negative territory and remained robust thereafter.
- Aggregate demand recovery was likely a more important driver of broad-based PPI reflation than capacity cuts.
- Main quantitative findings:
  - Demand factors explain around 60 percent of the change in PPI in the coal and steel industries.
  - Capacity cuts account for 1/4 to one third of the change in PPI in the coal and steel industries.
  - For other upstream industries, demand factors more than explain the rise in PPI as follows:
    - chemical products: 225 percent
    - non-ferrous metal extraction: 121 percent
    - nonferrous processing: 123 percent
    - ferrous metal extraction: up to 92 percent
    - petroleum processing: up to 97 percent
    - petroleum extraction: up to 70 percent
  - Allowing for different impacts across industries: capacity reduction accounts for 42 percent of the change in PPI in the steel industry in 2016-17; capacity cuts do not have a statistically significant impact in the coal industry.
- Regression and decomposition takeaways:
  - Panel regression point estimate of capacity-reduction coefficient 훿 is 0.09 and statistically different from zero.
  - For steel and coal, capacity reduction represents about 34 percent and 25 percent, respectively, of the change in PPI in 2016-17; demand factors represent 63 percent and 59 percent respectively; regression residuals account for the remainder.
  - For the six upstream industries without capacity cuts, common demand factors account for 70 – 225 percent of the change in producer prices.
  - Difference-in-difference exercise indicates coal and steel industries experienced much higher PPI inflation (11 percent higher) once the policy was in place compared to non-intervened industries.

### Empirical approach and data structure
- PPI composition: 76 percent intermediate goods and 24 percent final consumer goods; upstream prices more volatile than downstream.
- Eight upstream industries studied (accounting for more than 80 percent of PPI variance since 2013): chemical products, coal, ferrous metal extraction, ferrous metal processing, nonferrous metal extraction, nonferrous metal processing, petroleum extraction, petroleum processing.
- Methodology:
  - Extracted common demand factors from a set of 40 downstream demand variables using factor analysis; top three common factors explain around 70 percent of variations.
  - Factor 1 comoves with inventory changes (general equipment, furniture, overall manufacturing product inventory).
  - Factor 2 comoves with manufacturing value added variables and real estate investment.
  - Factor 3 comoves with auto-inventory, price of asphalt, and electricity consumption.
  - Panel regression specification: ∆PPI_i,t = β X_t + δ D_i,t + ε_i,t, where ∆PPI_i,t is year-on-year PPI growth for industry i; X_t is vector of three common factors and constant; D_i,t is semi-annual reduction in capacity as percent of end-2015 capacity (non-zero only after 2015 for steel and coal).
  - Sample: monthly data January 2012 to November 2017.
  - Semi-annual capacity reduction used due to data availability; robustness checks with industry-time dummies yield broadly unchanged results.
  - Time-series analysis conducted separately for coal and steel to allow sector-specific demand and supply effects.

### Model specification and coal versus steel results
- Model (sector-specific): ∆푌௜,௧ = 훽௜ 푋௧ + 훿௜ 퐷௜,௧ + 휀௜,௧, where ∆푌௜,௧ is year-on-year PPI inflation for sector i (i={coal, steel}), 푋௧ is the vector of three principal downstream demand factors, 퐷௜,௧ measures semi-annual capacity cuts in percent of end-2015 capacity, and 훿௜ captures the effect of capacity reduction on prices in industry i.
- Sample windows:
  - Coal: monthly data from March 2012 to December 2017.
  - Steel: monthly data from August 2012 to December 2017.
- Sector regression highlights (Observations: 59; R-squared: coal 0.88, steel 0.87):
  - Coal regression coefficients (t-stats):
    - Factor 1: 0.02*** (t-stat: 4.5)
    - Factor 2: 0.02*** (t-stat: 6.6)
    - Factor 3: -0.06*** (t-stat: -15.8)
    - Capacity reduction: -0.13 (t-stat: -0.1)
    - Constant: -0.02 (t-stat: -1.1)
  - Steel regression coefficients (t-stats):
    - Factor 1: 0.01 (t-stat: 1.1)
    - Factor 2: 0.02*** (t-stat: 7.0)
    - Factor 3: -0.04*** (t-stat: -9.7)
    - Capacity reduction: 4.3** (t-stat: 2.7)
    - Constant: -0.04* (t-stat: -2.3)
- Panel / difference-in-difference excerpt (dependent variable: changes in PPI in the 8 upstream industries):
  - Table coefficients:
    - Factor 1: 0.01*** (t-stat: 7.04)
    - Factor 2: 0.02*** (t-stat: 16.51)
    - Factor 3: -0.04*** (t-stat: -21.02)
    - Capacity reduction: 3.55*** (t-stat: 5.07)
    - Constant: -0.03*** (t-stat: -6.55)
    - Observations: 472; R-squared: 0.69
  - Difference-in-difference mean PPI:
    - Before capacity cuts:
      - Control: -0.02, 0.01, -1.62, 0.11
      - Treated: -0.04, 0.01, -4.56, 0.00
      - Difference: -0.03, 0.01, -3.83, 0.00
    - After capacity cuts:
      - Control: -0.04, 0.01, -2.63, 0.01
      - Treated: 0.05, 0.02, 2.41, 0.02
      - Difference: 0.09, 0.02, 5.07, 0.00
    - Difference in difference: 0.11, 0.02, 6.12, 0.00

### Policy implications, welfare considerations, and recommendations
- Aggregate view:
  - Capacity reduction contributed importantly to improved prices and profitability in steel and coal, but broader PPI reflation was largely driven by aggregate demand recovery (stimulus measures boosting infrastructure spending, real estate activity, and credit).
  - Government plans to expand capacity cuts to more industries including cement, electrolyzed aluminum, flat glass and coal-fired power plants.
  - Capacity cuts were implemented alongside environmental objectives and broader SSSR measures; some supply-side measures were accompanied by demand-supporting policies (e.g., housing support aimed at destocking).
- Welfare considerations:
  - Potential welfare-improving mechanisms:
    - Capacity reduction can offset market failures that prevent exit of loss-making producers (e.g., local government support to maintain employment, investment, tax revenues).
    - Facilitates exit of insolvent (and low-capacity) firms and may reduce negative environmental externalities (air pollution).
  - Caveats and risks:
    - Higher prices alone are not sufficient to conclude capacity reduction is welfare-improving.
    - Persistent loss-making may reflect optimal firm behavior under irreversible upfront investments.
    - Capacity reduction could exacerbate resource misallocation if it disproportionately affects private firms.
    - Rising coal and steel prices increase costs for downstream industries (electricity generation, real estate, manufacturing) and could represent pure transfers rather than net welfare gains.
    - Strong pickup in aggregate demand during the period was important to mitigate adverse effects on downstream industries; absent demand strength, higher upstream prices would have hurt input-using industries.
- Recommendation:
  - A fuller cost-benefit analysis is desirable before expanding capacity cuts to other industries.
  - Supporting prices is a necessary condition for capacity cuts to deliver overall positive welfare impacts, but not a sufficient condition; capacity cuts can produce transfers both within intervened industries and between intervened and non-intervened industries.

*Source: IMF working paper content (wp18216).*

### 1. The Chinese economy is undergoing a transition with policymakers putting increasing

### 1. The Chinese economy is undergoing a transition with policymakers putting increasing emphasis on the quality rather than the quantity of growth

### Overview of the transition and Supply-Side Structural Reforms (SSSR)
- Years of rapid growth driven by investment and credit expansion led to structural imbalances: a large debt overhang, financial system risks, proliferation of “zombie firms”, rapid buildup of residential housing inventories, and excess capacity in certain industrial sectors.
- Annual growth declined to "6-7 percent" as China entered a "new normal."
- In December 2015 the government announced Supply-Side Structural Reforms (SSSR) to guide the economy to a new normal; SSSR was elevated as a main national strategy at the 19th Party Congress in October 2017.
- Five areas of focus under SSSR: capacity reduction, housing inventory destocking, corporate deleveraging, reducing corporate costs, and improving “weak links” (industrial upgrading, infrastructure investment, etc.). Known in Chinese as the “three cuts, one reduction, and one improvement.”
- SSSR departs from traditional demand-side stimulus and aims to improve growth quality by addressing structural imbalances; some supply-side measures include demand-boosting policies (e.g., housing destocking supported by increased support for home purchases).

### Capacity buildup, targets, and implementation (steel and coal focus)
- Post-GFC stimulus channeled more than a third of the 4-trillion-yuan package to transport and power infrastructure, spurring heavy-industry capacity expansion.
- Government subsidies (cheap land and credit, discounted power, tax breaks) provided to about 30 percent of industrial output and contributed to excess capacity.
- Steel: production increased from around 100 million metric tons in 1996 to 627 million tons in 2010; global share rose from 13.5 percent to 44.3 percent (Greenpeace 2017).
- Coal: grew by 3.9 percent per year in 2006-15 versus global growth rate of 2.5 percent; China accounted for 47 percent of global coal production in 2015 with total production of 1.8 billion tons (BP 2017).
- Capacity utilization trends: steel contracted from 79 percent in 2010 to less than 70 percent in 2015; coal declined from more than 90 percent to 65 percent.
- By end-2015, coal and steel accounted for around 14 percent of corporate debt in China’s industrial sector.
- Debt payback at current cash flow generation: coal would take 91 years; ferrous metal smelting (including steel) would take 74 years; industrial sector average is under 10 years (EIU 2017).
- State Council targets (announced post-SSSR, 2016):
  - Steel capacity reduction target: 45 million tons within the year; within “three to five years” reduce steel capacity by 100-150 million tons (about 8-12 percent of end-2015 capacity).
  - Coal capacity reduction target: 250 million tons within the year; within “three to five years” reduce coal capacity by 1 billion tons (about 20 percent of end-2015 capacity).
  - Targets are net reductions; new capacity requires deeper cuts to existing capacity.
  - Required reduction in annual working days for all coal producers from 330 to 267, temporarily cutting coal production capacity by an additional 16 percent.
- Capacity reduction also linked to environmental protection objectives after severe pollution episodes (e.g., January 2013 “Airpocalypse”); Hebei province accounted for more than a third of planned annual steel reduction in 2016 (Greenpeace 2017).

### Observed capacity reduction progress and industry outcomes (2016-2017)
- Steel capacity curtailed by 115 million tons in 2016-17, more than 75 percent of the 5-year (2016-20) target.
- China phased out production of 140 million tons of steel made from scrap metal:
  - Scrap steel exports rose to 2.2 million tons in 2017 versus around 1,000 tons in 2016.
- Coal capacity curtailed by more than 290 million tons in 2016 and another 150 million tons in 2017.
- Number of coal mines reduced from 10,800 in 2015 to about 7,000 at end-2017.
- Capacity utilization ratios and profitability improved markedly in both industries following cuts.
- Crude steel production: grew by 1.2 percent in 2016 and 5.7 percent in 2017, indicating production remained strong despite capacity cuts.

### Key findings on drivers of PPI reflation and the role of capacity cuts
- China's PPI inflation turned positive in September 2015 after 54 months in negative territory and remained robust thereafter.
- While capacity reduction helped prop up prices in steel and coal, the paper finds aggregate demand recovery was likely a more important driver of broad-based PPI reflation.
- Main quantitative findings:
  - Demand factors explain around 60 percent of the change in PPI in the coal and steel industries.
  - Capacity cuts account for 1/4 to one third of the change in PPI in the coal and steel industries.
  - For other upstream industries, demand factors more than explain the rise in PPI as follows:
    - chemical products: 225 percent
    - non-ferrous metal extraction: 121 percent
    - nonferrous processing: 123 percent
    - ferrous metal extraction: up to 92 percent
    - petroleum processing: up to 97 percent
    - petroleum extraction: up to 70 percent
  - Allowing for different impacts across industries: capacity reduction accounts for 42 percent of the change in PPI in the steel industry in 2016-17; capacity cuts do not have a statistically significant impact in the coal industry.
- Regression and decomposition takeaways:
  - Panel regression point estimate of capacity-reduction coefficient 훿 is 0.09 and statistically different from zero.
  - For steel and coal, capacity reduction represents about 34 percent and 25 percent, respectively, of the change in PPI in 2016-17; demand factors represent 63 percent and 59 percent respectively; regression residuals account for the remainder.
  - For the six upstream industries without capacity cuts, common demand factors account for 70 – 225 percent of the change in producer prices (common demand factors more than sufficient).
  - Difference-in-difference exercise indicates coal and steel industries experienced much higher PPI inflation (11 percent higher) once the policy was in place compared to non-intervened industries.

### Empirical approach and data structure
- PPI composition: 76 percent intermediate goods and 24 percent final consumer goods; upstream prices more volatile than downstream.
- Eight upstream industries studied (accounting for more than 80 percent of PPI variance since 2013): chemical products, coal, ferrous metal extraction, ferrous metal processing, nonferrous metal extraction, nonferrous metal processing, petroleum extraction, petroleum processing.
- Methodology:
  - Extracted common demand factors from a set of 40 downstream demand variables using factor analysis; top three common factors explain around 70 percent of variations.
  - Factor 1 comoves with inventory changes (general equipment, furniture, overall manufacturing product inventory).
  - Factor 2 comoves with manufacturing value added variables and real estate investment.
  - Factor 3 comoves with auto-inventory, price of asphalt, and electricity consumption.
  - Panel regression specification: ∆PPI_i,t = β X_t + δ D_i,t + ε_i,t, where ∆PPI_i,t is year-on-year PPI growth for industry i; X_t is vector of three common factors and constant; D_i,t is semi-annual reduction in capacity as percent of end-2015 capacity (non-zero only after 2015 for steel and coal).
  - Sample: monthly data January 2012 to November 2017.
  - Semi-annual capacity reduction used due to data availability; robustness checks with industry-time dummies yield broadly unchanged results.
  - Time-series analysis conducted separately for coal and steel to allow sector-specific demand and supply effects.

### Policy implications and extensions noted
- Capacity reduction contributed importantly to improved prices and profitability in steel and coal, but broader PPI reflation was largely driven by aggregate demand recovery (stimulus measures boosting infrastructure spending, real estate activity, and credit).
- Encouraged by progress, government plans to expand capacity cuts to more industries including cement, electrolyzed aluminum, flat glass and coal-fired power plants.
- Capacity cuts were implemented alongside environmental objectives and broader SSSR measures; some supply-side measures were accompanied by demand-supporting policies (e.g., housing support aimed at destocking).

*Source: IMF working paper content (wp18216).*

### 24. We allow coal and steel to have heterogeneous elasticities to the same three common

### wp18216 - 24. We allow coal and steel to have heterogeneous elasticities to the same three common

### Model specification and data
- Model: ∆푌௜,௧ = 훽௜ 푋௧ + 훿௜ 퐷௜,௧ + 휀௜,௧, where:
  - ∆푌௜,௧ is year-on-year PPI inflation for sector i, i={coal, steel}.
  - 푋௧ is the vector of the three principal downstream demand factors identified previously.
  - 퐷௜,௧ measures semi-annual capacity cuts in percent of end-2015 capacity.
  - 훿௜ captures the effect of capacity reduction on prices in industry i and is estimated separately for each industry.
- Sample:
  - Coal: monthly data from March 2012 to December 2017.
  - Steel: monthly data from August 2012 to December 2017.

### Sector regression results — coal versus steel
- Steel industry:
  - Demand factors explain 58 percent of steel price movements.
  - Capacity cuts account for 42 percent of steel price movements, larger than in the panel regression.
- Coal industry:
  - Demand factors account for the full shift in PPI inflation in the sector; capacity reduction is insignificant.

- Industry-specific regression estimates (dependent variable: industry-specific PPI changes; Observations: 59, R-squared: coal 0.88, steel 0.87):
  - Coal:
    - Factor 1: 0.02*** (t-stat: 4.5)
    - Factor 2: 0.02*** (t-stat: 6.6)
    - Factor 3: -0.06*** (t-stat: -15.8)
    - Capacity reduction: -0.13 (t-stat: -0.1)
    - Constant: -0.02 (t-stat: -1.1)
  - Steel:
    - Factor 1: 0.01 (t-stat: 1.1)
    - Factor 2: 0.02*** (t-stat: 7.0)
    - Factor 3: -0.04*** (t-stat: -9.7)
    - Capacity reduction: 4.3** (t-stat: 2.7)
    - Constant: -0.04* (t-stat: -2.3)

- Panel / difference-in-difference highlights (dependent variable: changes in PPI in the 8 upstream industries):
  - Table 1 / Table 2 coefficients excerpt:
    - Factor 1: 0.01*** (t-stat: 7.04)
    - Factor 2: 0.02*** (t-stat: 16.51)
    - Factor 3: -0.04*** (t-stat: -21.02)
    - Capacity reduction: 3.55*** (t-stat: 5.07)
    - Constant: -0.03*** (t-stat: -6.55)
    - Observations: 472; R-squared: 0.69
  - Difference-in-difference mean PPI (Mean PPI, Std. Error, t, P>|t|):
    - Before capacity cuts:
      - Control: -0.02, 0.01, -1.62, 0.11
      - Treated: -0.04, 0.01, -4.56, 0.00
      - Difference: -0.03, 0.01, -3.83, 0.00
    - After capacity cuts:
      - Control: -0.04, 0.01, -2.63, 0.01
      - Treated: 0.05, 0.02, 2.41, 0.02
      - Difference: 0.09, 0.02, 5.07, 0.00
    - Difference in difference: 0.11, 0.02, 6.12, 0.00

### Conclusions on drivers of PPI reflation and policy context
- Main findings:
  - Strengthening of downstream demand played a major role in the broad-based PPI reflation in China since mid-2015.
  - For coal and steel, capacity cuts also contributed to shoring up prices.
- Downstream demand drivers likely reflect 2015-16 policies that boosted infrastructure investment and housing sector activity, including:
  - Destocking in the residential housing market and resurgence in housing prices and sales (notably in smaller cities) until tighter housing market policies were introduced.
  - Large increase in public sector infrastructure investment in 2016.
  - Tax cuts supporting demand in downstream industries (example policies: tax cut on vehicle purchases and reduction in business taxes).
- Conclusion: The full package of stimulus policies implemented in 2015-16 appears to have been important for the observed PPI reflation.

### Welfare considerations regarding capacity reduction policy
- Potential welfare-improving mechanisms:
  - Capacity reduction can offset market failures that prevent exit of loss-making producers (e.g., local government support to maintain employment, investment, tax revenues).
  - Facilitates exit of insolvent (and low-capacity) firms and may reduce negative environmental externalities (air pollution) associated with sub-optimally excessive production.
- Caveats and risks:
  - Higher prices alone are not sufficient to conclude capacity reduction is welfare-improving.
  - Persistent loss-making may reflect optimal firm behavior under irreversible upfront investments (Dixit and Pindyck, 1994).
  - Capacity reduction could exacerbate resource misallocation if it disproportionately affects private firms.
  - Rising coal and steel prices increase costs for downstream industries (electricity generation, real estate, manufacturing) and could represent pure transfers rather than net welfare gains.
  - Strong pickup in aggregate demand during the period was important to mitigate adverse effects on downstream industries; absent demand strength, higher upstream prices would have hurt input-using industries.
- Recommendation:
  - A fuller cost-benefit analysis is desirable before expanding capacity cuts to other industries.
  - Supporting prices is a necessary condition for capacity cuts to deliver overall positive welfare impacts, but not a sufficient condition; capacity cuts can produce transfers both within intervened industries and between intervened and non-intervened industries.

*Source: IMF Working Paper excerpt (wp18216).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2018/wp18216.pdf_