## 1. Determinants of Private Sector Credit Growth

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### I. Introduction
- China’s nonfinancial sector domestic credit-to-GDP ratio:
  - Stable at around 135 percent before the Global Financial Crisis (GFC).
  - Increased to about 235 percent in 2016.
- Credit gap (deviation from historical trend) is about 25 percent of GDP, above the 10 percent BIS threshold for the maximum countercyclical buffer.
- Over the last five years, credit expansion efficiency deteriorated, indicating growing resource misallocation.
- Rapidly rising debt and deteriorating credit efficiency pose financial stability and disruptive-adjustment risks; international experience links rapid credit growth to financial crises and/or sharp growth slowdowns.
- China-specific mitigating factors: lack of reliance on foreign financing, low government debt, and state control — these can reduce near-term risks but may allow the boom to grow and last longer if unaddressed.
- Paper frames analysis to assess sustainability and policy options, building on literature on credit booms and busts.

### II. China’s Credit Developments
- Aggregate developments:
  - Domestic nominal credit to the nonfinancial sector more than doubled over the last five years (to end-2016).
  - Domestic nonfinancial sector credit-to-GDP ratio rose to about 235 percent of GDP as of end-2016.
- Credit intensity deterioration:
  - In 2007-08, about RMB 6½ trillion of new credit was needed to raise nominal GDP by about RMB 5 trillion per year.
  - In 2015-16, it took more than RMB 20 trillion in new credit for the same nominal GDP growth.
- Sectoral and ownership patterns:
  - Credit allocation shifted towards services; credit is now broadly evenly distributed between industrial and service sectors.
  - Due to deteriorating credit efficiency in industry, industrial sector contributed about one-fifth of GDP in 2016 while the more-efficient service sector contributed more than two thirds of GDP in 2016.
  - State enterprises account for less than a half of total stock of credit in industrial sectors as of end-2016 (down from about 60 percent pre-GFC), but still account for about half of new credit; their profitability is much lower and cumulative value added growth about one-half that of private enterprises.
- Geographic differences:
  - Five Northeast provinces with heavy mining exposure saw declines in contribution to national industrial output, while their relative shares of industrial-sector credit remained broadly constant — implying continued new credit flows despite falling productivity and worsening credit efficiency.

### III. Drivers of China’s Credit Growth
- Empirical model (2008Q4–2016Q4) included:
  - Growth rate of deposits (supply of loanable funds).
  - Lagged GDP growth (strength of the economy; lag to avoid reverse causality).
  - Growth forecast a year ahead (consensus forecast; demand outlook).
  - Change in benchmark deposit rate (monetary policy stance; lagged).
  - NPL ratio (banking system health).
- Key empirical findings:
  - All variables except NPL ratio have expected signs and are statistically significant when considered together.
  - Looser monetary policy is a key driver of rapid recent credit growth.
  - In 2015-16, slower overall activity and deposit growth implied credit growth should have slowed by about 3½ percentage points relative to 2013-14; actual credit growth slowed only by ¾ percentage points as monetary policy contributed to robust credit growth.
- PBC monetary policy instruments:
  1. Quantity-based: reserve requirements and M2 targets.
  2. Price-based: lending rates, deposit rates, open-market operations.
  3. Administrative window guidance.
- Reserve requirement changes played an important role in bank lending, though impact since 2010 has waned.
- Many corporate borrowers (including SOEs) are relatively interest-rate insensitive due to perceived implicit state guarantees and roles in overcapacity sectors; quantitative credit targets and window guidance remain important tools.

### IV. Role of Industrial Structure and Provincial Analysis
- Cross-provincial panel (2008–2016) regressed provincial bank loan growth deviations on deviations of deposit growth, GDP growth, NPL ratio, GDP per capita, FAI/GDP, and infrastructure FAI share in total FAI. Hainan and Tibet excluded as outliers.
- Results:
  - Higher deposit growth and GDP growth → higher credit growth.
  - Provinces relying more on FAI—especially infrastructure FAI—received more credit.
  - Example: Five mining-exposed provinces (Heilungjiang, Jilin, Liaoning, Shanxi, Inner Mongolia) had average bank loan growth about 3 percentage points higher than national growth in 2015 despite slower deposit growth and lower GDP growth; their FAI/GDP ratio was higher by about 5 percentage points and infrastructure FAI/total FAI ratio higher by about 2½ percentage points than other provinces.

### V. Why Worry About China’s Credit Boom?
- Cross-country evidence:
  - 43 cases identified where credit-to-GDP rose by more than 30 percentage points over 5 years; only five ended without a major growth slowdown or financial crisis subsequently. After accounting for country-specific factors, those five provide little comfort.
  - All credit booms that began when ratios were above 100 percent—similar to China—ended badly.
- Two notable features of China’s credit series:
  1. Extended duration: since the most recent cycle end in 2003 no downturn observed; current expansion is 15 years and continuing—far longer than median international upswings.
  2. Limited corrections: average duration of credit downturns in China was 2 months; amplitudes ranged from about 1 percent to 9 percent—much smaller than typical international corrections.
- Growth implications (illustrative counterfactual):
  - If private nonfinancial sector credit-to-GDP had increased by only 10 percentage points instead of more than 45 percentage points, producing no private sector credit gap in 2016, and credit efficiency deteriorated less, then average real GDP growth for 2012–16 would have been 5.3 percent (versus the actual average reported in the source).
  - Growth-reducing effects of credit restraint could be partly offset by pro-rebalancing, on-budget fiscal stimulus, or productivity gains from structural reforms.

### Banking Sector Size, Complexity, and Vulnerabilities
- Banking system scale and expansion:
  - Banks’ balance sheets expanded by more than 50 percentage points of GDP over the last three years.
  - Total bank assets at 310 percent of GDP.
  - Sector size above the advanced economy average and nearly three times the emerging market average.
- Funding and structural complexity:
  - Rapid asset expansion financed via deposits, interbank markets, and wealth management products (WMPs), and through interlinked networks of entities.
  - Result: sharply rising size, complexity, and interconnectedness of exposures.
- Maturity mismatch:
  - Most banks are net borrowers in the interbank market with short maturities; bank assets have much longer maturities.
- WMP-specific risks:
  - WMP business invests more than half of its assets in fixed income markets.
  - Bond market losses at WMPs could stress bank balance sheets given perceived implicit guarantees by sponsoring banks.
- Liquidity and contagion risks:
  - Growing interlinkages imply a liquidity crunch could quickly spill over system-wide.
  - Disruptions could cascade down the intermediation ladder to other institutions and products.
- Recent indicators:
  - Net issuance of WMPs turned negative in Q2 2017.
  - Interbank repo usage declined somewhat, but banks remain vulnerable to funding shocks.

### China-specific Mitigating Factors and Limits
- External position:
  - Current account surplus and small external debt reduce external funding-crisis risk, but booms can still end badly without external reversals.
- Savings and deposit base:
  - Narrow loan-to-deposit ratio was 72 percent in 2016.
  - Total nonfinancial domestic credit-to-total deposit ratio was 118 percent.
  - Ratio of non-loan assets to total assets about 50 percent in 2016.
  - Short-term wholesale funding (non-deposit funding with maturity < 1 year) has risen.
- Corporate balance sheets and vulnerabilities:
  - Corporate asset values rose more than liabilities, lowering debt-to-asset leverage, but valuations are procyclical and could fall sharply.
  - Corporates’ liabilities are mostly financial; many assets are nonfinancial fixed assets (e.g., land) difficult to liquidate and valuation-sensitive.
  - Firm-level (WIND database; 5,428 firms) findings:
    - More leveraged firms have lower current ratios.
    - Lower interest coverage ratios associate with lower cash-to-current liabilities.
    - Non-financial firms’ exposure to financial assets is rising but remains low (about 3-4 percent of total assets on average).
  - Banks’ debt-at-risk (firms unable to generate sufficient earnings to cover debt interest) fell from about 15 percent of total loans at its 2015 peak to about 7 percent in 2017Q3, but remains well above official NPL and Special Mention Loan estimates.
- Fiscal space and limits:
  - Official general government debt was less than 40 percent of GDP as of 2016.
  - “Augmented” debt projected to rise to more than 90 percent of GDP over the medium term and on an unsustainable path.
  - Cross-country fiscal cost evidence of banking crises:
    - Average direct fiscal cost: 5-10 percent of GDP.
    - Indirect contingent-liability realization: about 6 percent of GDP (1990–2014).
    - Average gross fiscal cost of systemic banking crises: about 15 percent of GDP.
  - Fiscal backstops feasible but potentially costly, complex, and morally hazardous.
- Central bank and liquidity limits:
  - PBC can provide liquidity but may be constrained given system size, complexity, and entities outside liquidity frameworks.
  - Past stresses: bond market stress in December 2016; money market squeeze in March 2017.
  - Repeated PBC stabilization could raise moral hazard.
- Capital controls:
  - Recent tightening reduced capital outflows in 2017; controls remain effective but could trigger FX pressure if liquidity provisions lead to outflows. Stopping outflows could require large rate increases or draconian controls; controls lose effectiveness over time.
- Financial deepening and efficiency:
  - China’s leverage is significantly higher than peers; five years of rapid credit growth pushed debt ratios above levels typical for developed economies.
  - Financial deepening may have passed the point that maximizes growth benefits.
  - Investment and credit efficiency have fallen sharply; corporate profitability, leverage, and debt-servicing capacity deteriorated, especially among SOEs.
  - Estimated potential debt-at-risk remains high.

### Proactive Scenario and Quantitative Outcomes
- Assumptions:
  - Faster structural reform (notably SOE reform) and improved resource allocation efficiency permit credit growth to slow gradually while medium-term growth is supported by higher TFP.
- Outcomes:
  - Improved credit efficiency would raise GDP growth even with slower credit growth over the medium term.
  - Total domestic nonfinancial sector credit-to-GDP would stabilize at about 270 percent in 2022, about 20 percentage points lower than the baseline in the IMF staff report (2017).
- Historical/model projections referenced in source (charts):
  - Private credit growth: pro-active vs. baseline.
  - Real GDP growth: pro-active vs. baseline.
  - Total domestic nonfinancial sector debt (percent of GDP): pro-active vs. baseline.

### Policy Recommendations to Deflate the Credit Boom without Precipitating a Crisis
- Precondition:
  - Deemphasize high and hard GDP targets that drive excessive credit provision.
- Comprehensive strategy to increase credit efficiency and reduce least-productive credit demand; strengthen regulatory and supervisory frameworks; close regulatory arbitrage; rein in leverage; increase transparency of nonbank financial institutions and WMPs.
- Demand-side measures:
  - (i) Allow “zombies” to exit, reduce overcapacity, and reform SOEs by hardening budget constraints.
  - (ii) Open up SOE-dominated sectors, especially services.
  - (iii) Implement macro-prudential measures more effectively.
- Supply-side measures:
  - (i) Carefully eliminate implicit government guarantees.
  - (ii) Tighten macro-prudential measures.
  - (iii) Conduct focused asset quality reviews with loss recognition and capital backstops.
- Financial-sector measures:
  - (i) Strengthen supervision of liquidity risk management and collateral standards in wholesale funding markets.
  - (ii) Adopt a holistic approach to shadow banking supervision to contain shadow credit product proliferation.
  - (iii) Upgrade supervisory cooperation and coordination.
  - (iv) Develop resolution and crisis-management frameworks to address defaults/mini-crises of nonbank financial institutions without large liquidity showers.
- Bankruptcy framework:
  - Build a robust bankruptcy system enabling efficient loss-sharing between creditors and borrowers to mitigate macro impacts of deleveraging and allow private entities to restart.
- Macroprudential policy role:
  - Use macroprudential policies as the main tool to prevent financial instability, reserving monetary policy to focus on price stability.

### Conclusions
- China’s credit boom is among the largest and longest historically; comparable safe booms are scarce and not reassuring.
- Unaddressed, China’s debt overhang could threaten financial stability and growth; leverage expansion appears increasingly unsustainable.
- China-specific buffers may delay and mitigate a disruptive adjustment but are unlikely to eliminate it and may prolong the boom.
- Decisive policy action is required to break the negative feedback loop between slowing underlying growth, excessive credit provision, and worsening debt-service capacity. Current stable markets and robust growth provide a unique window to act.

### Box 1. Measuring credit: How large is China’s nonfinancial sector credit?
- Measures:
  - Banks’ claims on the private nonfinancial sector: about 155 percent of GDP (2016).
  - Total Social Financing (TSF) stock: about 209 percent of GDP (end-2016); households account for about 44 percent of TSF.
  - Official general government debt: about 37 percent of GDP.
- Aggregate estimates:
  - Total domestic nonfinancial sector credit: about 235 percent of GDP.
  - Discrepancy with TSF + general government debt arises because LGFV debt explicitly recognized as local government debt (about 17 percent of GDP) is captured in both TSF and general government debt, and social capital portions (about 4 percent of GDP) are not captured in either.
- LGFV treatment and “augmented” debt:
  - Legal authorities’ definition: general government debt = 37 percent of GDP (includes only explicitly recognized former LGFV borrowings); remaining non-recognized LGFV debt classified as private (198 percent).
  - If non-recognized LGFV debt and social capital portions are treated as contingent government liabilities, “augmented” total public debt = 62 percent of GDP (as of 2016), with remaining private nonfinancial sector debt = 173 percent of GDP.
- Key statistics:
  - Banks’ claims on private nonfinancial sector: about 155 percent of GDP (2016).
  - TSF stock: about 209 percent of GDP (end-2016).
  - Households’ share of TSF: about 44 percent.
  - Official general government debt: about 37 percent of GDP.
  - Total domestic nonfinancial sector credit: about 235 percent of GDP.
  - LGFV debt explicitly recognized as local government debt: about 17 percent of GDP.
  - Social capital portion of government guided funds and special construction funds: about 4 percent of GDP.
  - Augmented total public debt (including contingent liabilities and non-recognized LGFV debt): 62 percent of GDP (as of 2016).
  - Remaining private nonfinancial sector debt under augmented assumption: 173 percent of GDP.

*IMF Working Paper (excerpt) — “1. Determinants of Private Sector Credit Growth” from wp1802.*

### 1. Determinants of Private Sector Credit Gr owth ............................................................... 18

### 1. Determinants of Private Sector Credit Growth

### I. Introduction
- China’s nonfinancial sector domestic credit-to-GDP ratio was stable at around 135 percent before the Global Financial Crisis (GFC) and increased to about 235 percent in 2016.
- The credit gap—the deviation of credit-to-GDP ratio from its historical trend—is about 25 percent of GDP, well above the 10 percent threshold for the maximum countercyclical buffer as suggested by the BIS.
- Over the last five years, the efficiency of credit expansion has deteriorated, pointing to growing resource misallocation.
- Rapidly rising debt and deteriorating credit efficiency raise concerns about financial stability and a disruptive adjustment. International experience suggests rapid credit growth is typically associated with a financial crisis and/or a sharp growth slowdown.
- China-specific mitigating factors noted include lack of reliance on foreign financing, low government debt, and state control; these may reduce near-term risks but could make the boom larger and last longer if risks are left unaddressed.
- The paper builds on literature on credit booms and busts (Schularick and Taylor, 2010; Geanakoplos, 2010; Borio, 2012; Friedman, 1986; Gourinchas and Obstfeld, 2012; Jorda et al., 2010; Chen and Svirydzenka, forthcoming) and frames the analysis to assess sustainability and policy options.

### II. China’s Credit Developments
- Credit surged since the GFC: domestic nominal credit to the nonfinancial sector more than doubled over the last five years (to end-2016).
- Domestic nonfinancial sector credit-to-GDP ratio rose to about 235 percent of GDP as of end-2016.
- Credit intensity deteriorated: 
  - In 2007-08, about RMB 6½ trillion of new credit was needed to raise nominal GDP by about RMB 5 trillion per year.
  - In 2015-16, it took more than RMB 20 trillion in new credit for the same nominal GDP growth.
- Sectoral and ownership patterns:
  - More credit has been allocated to service sectors in recent years; credit is now broadly evenly distributed between industrial and service sectors.
  - Due to continued deterioration in credit efficiency in the industrial sector, this credit contributed only about one-fifth of GDP in 2016, while the more-efficient service sector contributed more than two thirds of GDP in 2016.
  - State enterprises account for less than a half of total stock of credit in industrial sectors as of end-2016, down from about 60 percent before the GFC; however, these enterprises still account for about half of new credit while their profitability is much lower and cumulative value added growth has been only about a half of that of private enterprises.
- Geographic differences:
  - Five provinces in the Northeast with heavy exposure to mining sectors saw declines in contribution to national industrial output, while their relative shares of credit in industrial sectors remained broadly constant—implying new credit continued flowing despite falling productivity and hence deteriorating credit efficiency in those provinces.

### III. Drivers of China’s Credit Growth
- Empirical approach:
  - A standard time-series model of private credit determinants was estimated using quarterly observations from 2008Q4 to 2016Q4. The specification included:
    - Growth rate of deposits (captures supply of loanable funds).
    - Lagged GDP growth (overall strength of the economy; lag used to avoid reverse causality).
    - Growth forecast a year ahead (proxied by consensus forecast; captures demand outlook).
    - Change in benchmark deposit rate (proxy for overall monetary policy stance; lagged to allow policy lag).
    - NPL ratio (captures banking system health).
- Key empirical findings:
  - All variables except NPL ratio have the expected signs and are statistically significant when considered together.
  - Looser monetary policy is a key driver of rapid credit growth in recent years.
  - In 2015-16, overall economic activity and growth outlook as well as deposit growth slowed compared to previous years, implying credit growth should have slowed by about 3½ percentage points compared to the 2013-14 period. However, actual credit growth slowed only by ¾ percentage points during this period as the monetary policy stance contributed to robust credit growth—confirming policy support was used to provide more credit and support the economy to achieve the high growth target.
- Monetary policy instruments in China:
  - The People’s Bank of China’s (PBC’s) policy tools fall broadly into three categories:
    1. Quantity-based instruments such as reserve requirements and M2 targets.
    2. Price-based instruments including lending rates, deposit rates and open-market operations.
    3. Administrative window guidance.
  - Changes in reserve requirement ratios played an important role in bank lending, though its impact since 2010 has waned (Fungáčová et al., 2016; Chen, forthcoming).
  - Many corporate borrowers, including SOEs, are relatively insensitive to changes in interest rates due to perceptions of implicit state guarantees and their role in manufacturing-based, overcapacity sectors with reduced credit efficiency. Use of quantitative credit targets and window guidance thus remain important monetary policy instruments.

### IV. Role of Industrial Structure and Provincial Analysis
- Cross-provincial panel regression (2008–2016) analyzed deviation of provincial bank loan growth from national growth rate with explanatory variables as deviations from national values: deposit growth, GDP growth, NPL ratio, GDP per capita, fixed asset investment (FAI)/GDP, infrastructure FAI share in total FAI. Hainan and Tibet excluded as outliers.
- Empirical results confirm industrial structure matters for excessive credit growth:
  - Higher deposit growth and GDP growth lead to higher credit growth (as expected).
  - Provinces relying more on FAI investment—especially infrastructure investment—received more credit.
  - Example: Five provinces with heavy exposure to mining sectors (Heilungjiang, Jilin, Liaoning, Shanxi, Inner Mongolia) had average bank loan growth about 3 percentage points higher than national growth rate in 2015 despite slower deposit growth and lower GDP growth. Their FAI/GDP ratio was higher by about 5 percentage points and infrastructure FAI/total FAI ratio was higher by about 2½ percentage points than other provinces—indicating more credit was provided than implied by overall demand and deposit growth.

### V. Why Worry About China’s Credit Boom?
- Cross-country evidence:
  - Identified 43 cases where credit-to-GDP rose by more than 30 percentage points over a 5-year period; among these, only five ended without a major growth slowdown or a financial crisis in the immediate aftermath. Once country-specific factors are considered, these five provide little comfort.
  - All credit booms that began when ratios were above 100 percent—similar to China—ended badly.
- Two remarkable features of China’s credit series (from cycle-dating algorithm on BIS credit series, deflated by GDP deflator, with interpolated monthly data):
  1. Extended duration: Since the end of the most recent cycle in 2003, no downturn has been observed. The current expansion is 15 years and continuing—far surpassing median durations in other studies (median upswings 7 to 12 quarters in OECD; about 3 years in broader samples).
  2. Limited corrections: Downturns in China have been short and limited. Average duration of credit downturns in China was 2 months; amplitudes ranged from about 1 percent to 9 percent—much smaller than typical international corrections which feature sharp declines often exceeding the size of preceding credit buildups.
- Growth implications of excessive credit:
  - The paper provides an illustrative counterfactual: if the nonfinancial private sector credit-to-GDP ratio had increased by only 10 percentage points over the recent period (instead of more than 45 percentage points), leading to no private sector credit gap in 2016, and if credit efficiency evolved in line with slower credit growth (i.e., deteriorated less), then average real GDP growth for 2012–16 would have been 5.3 percent rather than the actual average (actual average is not fully quoted in the supplied excerpt).
  - The analysis notes that growth-reducing effects of credit restraint could be partly offset by pro-rebalancing, on-budget fiscal stimulus, or productivity gains from decisive structural reforms.

*Italicized source attribution: IMF Working Paper (excerpt) — “1. Determinants of Private Sector Credit Growth” from wp1802.*

### 7.3  percent.

### wp1802 - 7.3  percent.

### Key findings on sustainable growth and credit boom
- The second approach using estimated fiscal and credit multipliers based on provincial panel data during 2003–15 (Chen et al. 2017) finds that 2012-16 average real GDP growth would have been 5.9 percent.
- Both approaches examined in the source find that sustainable growth was likely much lower than actual growth over the last five years, highlighting the need to arrest the negative feedback loop between slower underlying growth, excessive credit provision, and worsening debt service capacity.

### Banking sector size, complexity, and vulnerabilities
- Chinese banks’ balance sheets have expanded by more than 50 percentage points of GDP over the last three years.
- Total bank assets are at 310 percent of GDP.
- China’s banking sector size is above the advanced economy average and nearly three times the emerging market average.
- Rapid asset expansion financed by complex structures: beyond deposit funding to interbank markets and wealth management products (WMPs), and via interlinked networks of entities.
- Resulting risks: sharply rising size, complexity, and interconnectedness of exposures.
- Maturity mismatch:
  - Most banks remain net borrowers in the interbank market, with maturities still hovering near the short end.
  - Bank assets tend to have much longer maturities by comparison.
- WMP-specific risk:
  - The WMP business invests more than half of its assets in the fixed income markets.
  - Bond market losses suffered by WMPs could lead to bank balance sheet stress given the widespread perception of implicit guarantees of banks’ sponsorship of WMPs.
- Liquidity and contagion risk:
  - Growing interlinkages suggest a liquidity crunch could quickly spill over to the broader financial system.
  - Disruption to financing flows could cascade down the intermediation ladder, reaching other financial products and institutions including the broader banking sector.
- Recent developments:
  - Net issuance of WMPs turned negative in the second quarter of 2017.
  - Total usage of the interbank repo market has declined somewhat, but banks remain vulnerable to funding shocks.

### China-specific mitigating factors and limits
- Strong external position:
  - Current account surplus and small external debt reduce the possibility of a typical external funding crisis, but history shows booms can still end badly without large external funding reversals.
- High domestic savings and deposit base:
  - Narrowly-defined loan-to-deposit ratio was 72 percent in 2016.
  - Total nonfinancial domestic credit-to-total deposit ratio was 118 percent.
  - Ratio of non-loan assets to total assets is about 50 percent in 2016, higher than the median in the cross-country sample.
  - Short-term wholesale funding (non-deposit funding with maturity < 1 year) has risen.
- Strong asset side of corporate balance sheets but rising vulnerabilities:
  - Corporate asset values have increased more than liabilities, lowering leverage as measured by debt-to-asset ratio, but valuations are procyclical and could fall sharply if the boom ends.
  - Corporates’ liabilities are mostly financial while a significant portion of assets are nonfinancial fixed assets (e.g., land), which may not be easily liquidated and are subject to sharp valuation changes.
  - Firm-level data (WIND database; 5,428 firms) indicate:
    - More leveraged firms tend to have lower current ratios (liquidity).
    - Lower debt servicing capacity (interest coverage ratio) is associated with lower cash buffers (cash-to-current liabilities).
    - Non-financial firms’ exposure to financial assets is rising though still at very low level (about 3-4 percent of total assets on average), implying increased vulnerability to market shocks.
  - Banks’ debt at risk (companies unable to generate sufficient earnings to cover debt interest payments) declined from about 15 percent of total loans at its peak in 2015 to about 7 percent in 2017Q3, but remains substantially higher than official estimates of NPLs and Special Mention Loans.
- Ample fiscal space but limits and costs:
  - Official general government debt was less than 40 percent of GDP as of 2016.
  - “Augmented” debt is projected to rise to more than 90 percent of GDP over the medium term, with debt on an unsustainable path.
  - Cross-country evidence on fiscal costs of banking crises:
    - Average direct fiscal cost estimated to be 5-10 percent of GDP.
    - Indirect fiscal cost from contingent liabilities realization about 6 percent of GDP during 1990-2014.
    - Average gross fiscal cost of systemic banking crises about 15 percent of GDP.
  - Fiscal backstops are feasible but could be costly, complex, and may deepen moral hazard.
- Liquidity provision and central bank limits:
  - The PBC can provide liquidity, but quick action may not be sufficient given the size, complexity, and interconnectedness of the system.
  - Many institutions and assets are outside the PBC’s liquidity framework; stress in small banks or non-banks may be hard to contain.
  - Past events: bond market stress in December 2016 and money market squeeze in March 2017 illustrate risks from informal markets with limited transparency.
  - If the PBC stabilizes markets repeatedly, moral hazard may increase.
- Capital controls and closed capital account:
  - Recent tightening of controls reduced capital outflows in 2017 and controls remain effective.
  - Liquidity provision could trigger capital outflows and FX pressure; stopping outflows could require large rate increases or draconian capital controls, both with growth costs.
  - Capital controls tend to lose effectiveness over time.
- Financial deepening and efficiency:
  - Leverage ratio in China is significantly higher than in countries with similar development.
  - Over the five years of rapid credit growth, debt ratios exceeded levels typical for developed economies.
  - Sahay et al. (2015) find China’s financial deepening has exceeded the turning point that maximizes positive effects on growth.
  - Efficiency of investment and credit has fallen sharply; financial performance of corporates—profitability, leverage, debt servicing capacity—has deteriorated, especially for SOEs.
  - Estimated potential debt-at-risk remains high.

### Proactive scenario and quantitative outcomes
- Illustrative proactive scenario assumptions:
  - Faster progress on structural reform (especially SOE reform) and improved overall efficiency in resource allocation allow credit growth to slow gradually while supporting medium-term growth via higher TFP growth.
- Quantitative scenario outcomes:
  - Improved credit efficiency would help raise GDP growth even with slower credit growth over the medium term.
  - This would stabilize the ratio of total domestic nonfinancial sector credit-to-GDP at about 270 percent in 2022, about 20 percentage points lower than in the baseline presented in the latest IMF staff report (2017).
- Historical and model-based projections (charts referenced in source):
  - Private credit growth: pro-active vs. baseline (series shown in source charts).
  - Real GDP growth: pro-active vs. baseline (series shown in source charts).
  - Total domestic nonfinancial sector debt (percent of GDP): pro-active vs. baseline (series shown in source charts).

### Policy recommendations to deflate the credit boom without precipitating a crisis
- Precondition:
  - Deemphasize high and hard GDP targets and the attendant excessive credit necessary to achieve these targets.
- Comprehensive strategy needed to increase credit efficiency by reducing demand for the least productive uses; financial reforms to bolster regulatory and supervisory frameworks, close regulatory arbitrage loopholes, rein in leverage, and increase transparency of nonbank financial institutions and WMPs.
- Demand-side measures:
  - (i) Let zombies exit, continue to reduce overcapacity, and reform SOEs by hardening budget constraints.
  - (ii) Open up SOE-dominated sectors, especially services sectors.
  - (iii) Implement macro-prudential measures more effectively.
- Supply-side measures:
  - (i) Eliminate implicit government guarantees carefully.
  - (ii) Tighten macro-prudential measures.
  - (iii) Conduct focused asset quality reviews with corresponding loss recognition and capital backstop.
- Financial-sector measures:
  - (i) Strong supervisory focus on the adequacy of liquidity risk management and collateral standards in wholesale funding markets.
  - (ii) A holistic approach to shadow banking supervision to contain proliferation of shadow credit products.
  - (iii) Upgrade supervisory cooperation and coordination.
  - (iv) Develop resolution and crisis management frameworks to address defaults/mini-crises of nonbank financial institutions without large liquidity showers.
- Bankruptcy framework:
  - A robust bankruptcy system enabling efficient loss-sharing agreements between creditors and borrowers can mitigate negative macro impacts from deleveraging and allow private entities to restart.
- Macroprudential policy emphasis:
  - Macroprudential policies should be the main instrument in preventing financial instability, rather than monetary policy, because they can target imbalances closer to their source with less output loss and allow monetary policy to focus on price stability.

### Conclusions
- China’s credit boom is one of the largest and longest in history; historical precedents of “safe” booms of similar magnitude and speed are scarce and not reassuring.
- If left unaddressed, China’s debt overhang could pose risks to financial stability and growth; debt deterioration suggests leverage expansion is increasingly unsustainable.
- China-specific buffers can help delay and mitigate a disruptive adjustment but will likely not eliminate the eventual adjustment and may make the boom larger and last longer.
- Decisive policy action is needed to arrest the negative feedback loop between slowing growth, excessive credit provision, and worsening debt service capacity. The current period of stable financial markets and robust growth offers a unique opportunity for tackling these issues.

*Source: wp1802 - 7.3  percent.*

### Box 1. M e asuring cre dit: How large  is  China’s  nonfinancial  s e ctor cre dit?

### Box 1. Measuring credit: How large is China’s nonfinancial sector credit?

### Measures of credit
- The narrowest measure is banks’ claims on the private nonfinancial sector, which stood at about 155 percent of GDP as of 2016.
- Total social financing (TSF) statistics capture conventional bank loan channels plus financing through off-balance items of financial institutions—trust loans, entrusted loans, and undiscounted bankers’ acceptances—and corporate bond issuance.
  - As of end-2016, TSF stock was about 209 percent of GDP, of which households accounted for about 44 percent.
- Official general government debt is about 37 percent of GDP.

### Estimates of aggregate nonfinancial sector credit
- Total domestic nonfinancial sector credit is estimated to be about 235 percent of GDP, smaller than the sum of TSF and general government debt.
  - The discrepancy arises because credit to local government financing vehicles (LGFVs) that was explicitly recognized as local government debt (about 17 percent of GDP) is captured both in TSF and general government debt statistics.
  - The social capital portion of government guided funds and special construction funds (about 4 percent of GDP) is not captured in either TSF or general government debt.

### Treatment and classification of LGFV debt
- The treatment of LGFV debt that has not been explicitly recognized as government debt straddles the border line between public and private debt.
  - From a legal perspective, the authorities’ definition of general government debt (37 percent of GDP), which includes only former LGFV borrowings that were explicitly recognized as LG debt, is public debt and the remaining is private debt (198 percent).
  - Assuming that the non-recognized LGFV debt resulting from public policy and the social capital portion of government guided funds and special construction funds are contingent government liabilities, “augmented” debt is then total public debt (62 percent of GDP as of 2016) and the remaining nonfinancial sector debt is private debt (173 percent).

### Key statistics and table highlights
- Banks’ claims on the private nonfinancial sector: about 155 percent of GDP (2016).
- TSF stock: about 209 percent of GDP (end-2016).
  - Households’ share of TSF: about 44 percent.
- Official general government debt: about 37 percent of GDP.
- Total domestic nonfinancial sector credit: about 235 percent of GDP.
- LGFV debt explicitly recognized as local government debt: about 17 percent of GDP.
- Social capital portion of government guided funds and special construction funds: about 4 percent of GDP.
- Augmented total public debt (including contingent liabilities and non-recognized LGFV debt): 62 percent of GDP (as of 2016).
- Remaining private nonfinancial sector debt under the augmented assumption: 173 percent of GDP.

*Source: IMF staff estimates as presented in Box 1, “Measuring credit: How large is China’s nonfinancial sector credit?”*

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_Source: https://www.imf.org/-/media/files/publications/wp/2018/wp1802.pdf_