## ch1onlineannex

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### Framework, sample, and data
- Objective: Propose a simple framework for policymakers to better target future policy support in the corporate sector (analysis in Chapter 1 of the April 2021 GFSR).
- Sample coverage:
  - Countries: Brazil, China, France, Germany, India, Italy, Japan, Mexico, Poland, Russia, Spain, Turkey, United Kingdom, and United States.
  - Firms: analysis at firm level; results aggregated by GICS level 2 sector and by firm size.
  - Size buckets:
    - large: >$500 million — bonds, equities, syndicated loans, bank loans
    - mid-sized: >$50 million and <$500 million — equities, bank loans, syndicated loans for larger firms
    - small: <$50 million — mainly bank loans, some equities for larger firms, credit cards for smaller firms
  - Sample size and composition:
    - The sample includes about 19,500 firms (text statement).
    - Online Annex Table 1.1.2 reports total 19,510 firms: total 19,510; large 8,442; mid-sized 6,219; small 4,849.
    - By region (Online Annex Table 1.1.2):
      - United States: 4,180 total; 1,999 large; 1,000 mid-sized; 1,181 small
      - Europe: 877 total; 484 large; 272 mid-sized; 121 small
      - Japan: 3,443 total; 1,290 large; 1,629 mid-sized; 524 small
      - China: 6,107 total; 3,879 large; 2,005 mid-sized; 223 small
      - other EMs: 4,903 total; 790 large; 1,313 mid-sized; 2,800 small
    - By ownership:
      - all public firms: 16,825 total; 6,382 large; 5,990 mid-sized; 4,453 small
      - all private firms: 2,685 total; 2,060 large; 229 mid-sized; 396 small
  - By sector: examples include Automobiles and Components: 559 total; Consumer Services: 3,528 total; Energy: 1,127 total; Food, Beverage and Tobacco: 2,244 total.
- Data source: S&P Capital IQ; latest balance sheets as of the third quarter of 2020 (2020Q3).

### Indicator design and thresholds
- Indicator categories:
  - Liquidity stress indicators: predict shortage of cash or liquid assets in near term.
  - Solvency stress indicators: signal possible erosion of equity position to zero in near term.
  - Viability indicators: gauge whether a firm will become profitable a few years from now (post-COVID recovery).
- Projection and modeling notes:
  - Firms’ EBIT projections based on analysts’ forecasts from IBES (S&P Capital IQ); for firms without forecasts, sectoral average projected changes applied to latest (2020Q3) EBIT to forecast EBIT in 2021.
  - Liquidity gaps in 2021 are assumed filled by raising debt; interest expense rises proportionally (effective interest rate constant).
  - Robustness checks include Altman Z scores and National University of Singapore/CRI probabilities of default (PDs).
- Selected indicators and thresholds (as reported):
  - Liquidity indicators:
    - projected cash < 0 — 2021 — IMF (2021)
    - interest coverage ratio (ICR) = EBIT / interest expense < 1 — latest (2020Q3) — IMF (2021)
    - liquidity buffer ratio = (cash + undrawn committed facilities + FCF) / 12m debt maturities < 1 — latest (2020Q3) — Fitch (2020)
    - current ratio = current assets / current liabilities < 5th percentile — latest (2020Q3) — IMF (2021)
  - Solvency indicators:
    - projected equity < 0 — 2021 — IMF (2021)
    - net debt/EBIT > CCC-rated firms — latest (2020Q3) — Fitch (2020), S&P (2013, 2018)
    - gross debt/EBIT > CCC-rated firms — latest (2020Q3) — Fitch (2020), Moody's (2020)
    - equity/assets 20% — latest (2020Q3)
  - Viability indicators (projected balance sheets and market valuations):
    - MT ICR and firm's age <1 for 3 years; exclude young firms — 2021-23
    - EBIT margin = EBIT / revenue < CCC-rated firms — 2023
    - gross debt/assets > 0.5 and increasing — 2021
    - price-to-book equity ratio < 1 — 2021
    - price-to-book equity ratio relative to firm's sector < 5th percentile — 2021
  - Robustness:
    - Altman Z scores (various formulas/thresholds) — latest (2020Q3) / 2021
    - Estimated probabilities of default > 95th percentile — 2021 — NUS model
- Composite rules:
  - Composite liquidity risk indicator high if at least three of four individual liquidity indicators are high.
  - Composite solvency risk indicator high if at least three of four individual solvency indicators are high.
  - Composite viability rules differ by data availability: for firms with no market price data, two out of three balance-sheet indicators required; for market-priced firms, both market-based indicators required; overall HVR deemed high if both balance sheets and market prices suggest high risk.

### Main results on liquidity, solvency, and viability
- Timing:
  - Liquidity and solvency assessed for 2020 and 2021.
  - Viability assessed using market prices and projected balance sheets over 2021–23.
- Liquidity stress:
  - Liquidity stress is elevated at small firms.
  - Among liquidity indicators, the ICR yields the highest share of debt with high risk; the cash indicator suggests a lower share of debt with high risk.
- Solvency stress:
  - Some solvency indicators based on leverage suggest elevated risk even at large firms.
  - Equity indicator points to a lower share of debt with high risk (existing equity as buffer).
  - Large firms: leverage elevated but equity position generally strong.
- Sector differentiation:
  - Considerable differentiation across sectors driven by sectoral nature of the COVID shock and pre-existing vulnerabilities.
  - Common vulnerable sectors across advanced and emerging market economies: energy, real estate, and retail.
  - Differences: biotech firms in advanced economies often growth firms with weak earnings and low equity but full market access; in emerging markets, telecommunication services are highly vulnerable.
- Viability findings:
  - Viability assessed via balance-sheet projections and market valuation-based indicators.
  - Composite viability indicator for firms with market price data requires both individual market indicators to signal high risk, which may understate share of firms with high viability risk by design.
  - Firms classified as non-viable by the framework would not receive government support under the framework; policymakers advised to err on the side of caution to minimize economic scarring and externalities.
- Notable cross-region observation:
  - Results based on balance sheet indicators for small firms in emerging market economies appear generally better than those for small firms in advanced economies; price-to-book comparison: shares of debt of small firms with the price-to-book ratio below 1 are 45 percent and 62 percent in advanced economies and emerging markets, respectively.

### Robustness and sensitivity analysis
- Altman Z scores:
  - Computed using formulas for listed firms, private firms, and firms in emerging markets.
  - Qualitatively confirm that small firms are riskier than large firms on average.
  - Suggest default risks are suppressed in emerging markets, contrasting with some simple metrics like ICRs.
- Estimated probabilities of default (NUS/CRI):
  - Based on balance sheets and market prices, including liquidity, solvency, and market-based viability risks.
  - PDs point to modest risks in advanced economies and elevated risks in emerging markets.
  - PDs do not suggest that small firms in advanced economies are weak—contrasting with the wide set of indicators used here.
- Forecast-earnings uncertainty:
  - Using upside (mean forecast plus two standard deviations) and downside (mean forecast minus two standard deviations) earnings assumptions affects shares of debt at firms with projected negative equity.
  - The shares of debt at firms with negative equity change by up to 2 percentage points of debt of all firms.
  - Reported chart range values include values from -2.5% to 2.0% (axis labels shown: -2.5%, -2.0%, -1.5%, -1.0%, -0.5%, 0.0%, 0.5%, 1.0%, 1.5%, 2.0%).
- Composite construction uncertainty:
  - Varying the number of individual indicators required for the composite solvency indicator (two, three, or four) materially changes the share of debt with high solvency risk.
  - When only two indicators are required, the share of debt with high solvency risk increases notably because two leverage indicators suggest highest stress.
  - Using three indicators presented as a balanced approach given offsetting effect of very low interest rates.
  - Reported aggregate ranges for share of debt with high solvency risk span 0% to 100% on the chart axes for different firm sizes and country groups.

### Policy-use guidance
- Policymakers can calibrate support using sensitivity results when fiscal space is constrained:
  - If fiscal resources are limited to support viable firms, policymakers could use pessimistic earnings assumptions (mean minus two standard deviations) and a more punitive composite viability risk indicator (fewer indicators required to signal stress) to minimize the number of firms classified as viable and thereby minimize the amount of required support.
- Operational recommendation:
  - Firms classified as non-viable by the framework would not receive government support; policymakers are advised to err on the side of caution to limit scarring and externalities (such as wider job losses).

*Source: ch1onlineannex - 1.   Share of Debt at Firms with Poor Viability (PDF chapter/section).*

### References

### ch1onlineannex - References

### References (selected)
- Adrian, Tobias, Richard K. Crump and Emanuel Moench. 2013. “Pricing the Term Structure with Linear Regressions.” Journal of Financial Economics, Vol. 110, No. 1, pp. 110–138.  
- Albagli, Elias, Luis Ceballos, Sebastian Claro, and Damian Romero. 2019. “Channels of US Monetary Policy Spillovers to International Bond Markets.” Journal of Financial Economics, Vol. 134, pp. 447–473.  
- Bowman, David, Juan M. Londono, and Horacio Sapriza. 2016. “U.S. Unconventional Monetary Policy and Transmission to Emerging Market Economies.” Journal of International Money and Finance, Vol. 55, pp. 27–59.  
- Driscoll, John C., and Aart C. Kraay. 1998. “Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data”. Review of Economics and Statistics, Vol. 80, pp. 549–560.  
- Mönch, Emanuel. 2019. “The Term Structures of Global Yields.” Bank for International Settlements, Vol. 102, pp. 3–15.  
- Rich, Robert W., J.E. Raemond, J. S. Butler. 1992. “The Relationship Between Forecast Dispersion and Forecast Uncertainty: Evidence from a Survey Data – ARCH model.” Journal of Applied Econometrics, Vol. 7, pp. 131–148.  
- Wright, Jonathan H. 2011. “Term Premia and Inflation Uncertainty: Empirical Evidence from an International Panel Dataset.” The American Economic Review, Vol. 101, No. 4, pp. 1514–1534.

### Corporate sector analysis — framework and scope
- Objective: Propose a simple framework for policymakers to better target future policy support in the corporate sector (analysis in Chapter 1 of the April 2021 GFSR).
- Sample coverage:
  - Countries: Brazil, China, France, Germany, India, Italy, Japan, Mexico, Poland, Russia, Spain, Turkey, United Kingdom, and United States.
  - Firms: analysis at firm level; results aggregated by GICS level 2 sector and by firm size.
  - Size buckets (classification by firm size):
    - large: >$500 million — bonds, equities, syndicated loans, bank loans
    - mid-sized: >$50 million and <$500 million — equities, bank loans, syndicated loans for larger firms
    - small: <$50 million — mainly bank loans, some equities for larger firms, credit cards for smaller firms
  - Sample size and composition:
    - The sample includes about 19,500 firms (text statement).
    - Online Annex Table 1.1.2 reports total 19,510 firms: total 19,510; large 8,442; mid-sized 6,219; small 4,849.
    - By region (Online Annex Table 1.1.2):
      - United States: 4,180 total; 1,999 large; 1,000 mid-sized; 1,181 small
      - Europe: 877 total; 484 large; 272 mid-sized; 121 small
      - Japan: 3,443 total; 1,290 large; 1,629 mid-sized; 524 small
      - China: 6,107 total; 3,879 large; 2,005 mid-sized; 223 small
      - other EMs: 4,903 total; 790 large; 1,313 mid-sized; 2,800 small
    - By ownership:
      - all public firms: 16,825 total; 6,382 large; 5,990 mid-sized; 4,453 small
      - all private firms: 2,685 total; 2,060 large; 229 mid-sized; 396 small
    - By sector: (examples from Online Annex Table 1.1.2)
      - Automobiles and Components: 559 total; 280 large; 213 mid-sized; 66 small
      - Consumer Services: 3,528 total; 1,795 large; 1,077 mid-sized; 656 small
      - Energy: 1,127 total; 344 large; 404 mid-sized; 379 small
      - Food, Beverage and Tobacco: 2,244 total; 900 large; 779 mid-sized; 565 small
      - total row: 19,510 total; 8,442 large; 6,219 mid-sized; 4,849 small
- Data source: S&P Capital IQ; latest balance sheets as of the third quarter of 2020 (2020Q3) used to incorporate COVID shock and policy effects.

### Indicators: types, selection, and thresholds
- Indicator categories (framework considers three types, similarly to Tressel and Ding (2021)):
  - Liquidity stress indicators: predict shortage of cash or liquid assets in near term.
  - Solvency stress indicators: signal possible erosion of equity position to zero in near term.
  - Viability indicators: gauge whether a firm will become profitable a few years from now (post-COVID recovery).
- Projection and modeling notes:
  - Firms’ EBIT projections are based on analysts’ forecasts from IBES (S&P Capital IQ). For firms without forecasts, sectoral average projected changes applied to latest (2020Q3) EBIT to forecast EBIT in 2021.
  - Liquidity stress is assumed to translate into solvency stress: firms with projected liquidity gaps in 2021 are assumed to fill gaps by raising debt; interest expense rises proportionally to increase in debt (assuming effective interest rate remains constant).
  - Robustness checks: (1) Altman Z scores (Altman (2000)), and (2) National University of Singapore/CRI probabilities of default (PDs).
- Selected indicators and thresholds (Online Annex Table 1.1.3; thresholds and times of assessment reported exactly as in source):
  - Liquidity indicators:
    - 1 projected cash < 0 — 2021 — IMF (2021)
    - 2 interest coverage ratio (ICR) = EBIT / interest expense < 1 — latest (2020Q3) — IMF (2021)
    - 3 liquidity buffer ratio = (cash + undrawn committed facilities + FCF) / 12m debt maturities < 1 — latest (2020Q3) — Fitch (2020)
    - 4 current ratio = current assets / current liabilities < 5th percentile — latest (2020Q3) — IMF (2021)
  - Solvency indicators:
    - 5 projected equity < 0 — 2021 — IMF (2021)
    - 6 net debt/EBIT > CCC-rated firms — latest (2020Q3) — Fitch (2020), S&P (2013, 2018)
    - 7 gross debt/EBIT > CCC-rated firms — latest (2020Q3) — Fitch (2020), Moody's (2020)
    - 8 equity/assets20% — latest (2020Q3)
  - Viability indicators (projected balance sheets):
    - 9 ICR and firm's age <1 for 3 years; exclude young firms — 2021-23 — Adalet McGowan et al (2017)
    - 10 EBIT margin = EBIT / revenue < CCC-rated firms — 2023 — Fitch (2020), Moody's (2020)
    - 11 gross debt/assets > 0.5 and increasing — 2021 — Fukuda and Nakamura (2011)
  - Viability indicators (market valuations):
    - 12 price-to-book equity ratio < 1 — 2021
    - 13 price-to-book equity ratio relative to firm's sector < 5th percentile — 2021 — Banerjee and Hofmann (2018)
  - Robustness checks:
    - 14 Altman Z scores for public firms in advanced economies — Altman thresholds — latest (2020Q3) / 2021 — Altman (2000)
    - 15 Altman Z" scores for private firms in advanced economies and all firms in emerging mar — Altman thresholds — latest (2020Q3) / 2021 — Altman (2000)
    - 16 Estimated probabilities of default > 95th percentile — 2021 — NUS model

### Results — key findings and mechanics
- Timing of assessments:
  - Liquidity and solvency assessed for 2020 and 2021 (per Annex Table 1.1.3).
  - Viability assessed using market prices and projected balance sheets over the next three years (2021–23).
- Main findings:
  - Liquidity stress:
    - Liquidity stress is elevated at small firms (text reference to Annex Figure 1.1.2, panel 1).
    - Among liquidity indicators, the interest coverage ratio (ICR) yields the highest share of debt with high risk, while the cash indicator suggests a lower share of debt with high risk.
  - Solvency stress:
    - Some solvency indicators—based on leverage—suggest elevated risk even at large firms (Annex Figure 1.1.2, panel 2).
    - Equity indicator points to a lower share of debt with high risk (existing equity as buffer).
    - Large firms: leverage elevated but equity position generally strong.
  - Composite indicator mechanics:
    - Composite liquidity risk indicator is deemed high if at least three out of the four individual liquidity indicators are assessed as high.
    - Composite solvency risk indicator is deemed high if at least three out of the four individual solvency indicators are assessed as high.
  - Sector differentiation:
    - Considerable differentiation across sectors due to sectoral nature of COVID shock and pre-existing vulnerabilities (Figure 1.1.3).
    - Common vulnerable sectors across advanced and emerging market economies: energy, real estate, and retail.
    - Differences: biotech firms in advanced economies often growth firms with weak earnings and low equity but full market access; in emerging markets, telecommunication services are highly vulnerable (likely due to large presence of call centers).
  - Viability assessment:
    - Viability assessed using both balance-sheet projections and market valuation-based indicators (Figure 1.1.4).
    - Composite viability risk indicator for firms with no market price data (balance sheets only) requires two out of three individual indicators to signal high risk.
    - Composite market valuations-based viability risk indicator for firms with market price data requires both individual indicators to signal high risk.
    - Composite viability risk indicator for firms with market price data is deemed high if both balance sheets and market prices suggest high risk — this requirement may understate share of firms with high viability risk by design.
    - Firms that are classified as non-viable by the framework would not receive government support under the framework; policymakers are advised to err on the side of caution to minimize economic scarring and externalities (such as wider job losses).
- Additional notes:
  - Results based on balance sheet indicators for small firms in emerging market economies appear generally better than those for small firms in advanced economies. Explanation given: greater market access for weak small firms in advanced economies. Example numeric comparison in text: shares of debt of small firms with the price-to-book ratio below 1 are 45 percent and 62 percent in advanced economies and emerging markets, respectively.

### Figures and tables referenced (topics)
- Online Annex Figure 1.1.1: EM Term Premium Sensitivity to Inflation Measures and the US Term Premium — panel shows estimated coefficients of emerging markets term premium to 1 percentage point shock in the U.S. term premium, inflation survey dispersion and 12-month inflation expectations; coefficient estimates reported with 90% confidence interval with robust standard errors (Driscoll-Kraay, 1998). Data sources noted: Bloomberg Finance L.P.; National Settlement; Clearing and Valuation Agencies; and IMF staff calculations.
- Online Annex Table 1.1.1: Classification of Non-Financial Corporates by Sector and Firm Size — sectors classified based on level 2 GICS; firm size buckets shown with typical sources of funding.
- Online Annex Table 1.1.2: Sample of Non-Financial Corporates — detailed regional, ownership, and sectoral counts (total 19,510).
- Online Annex Table 1.1.3: List of Liquidity, Solvency, and Viability Indicators — indicators, thresholds, time of assessment, and references (includes items 1–16 as listed above).
- Online Annex Figure 1.1.2: Individual Indicators of Liquidity and Solvency Risks by Firm Size — panels show share of debt at firms with elevated liquidity stress indicators and elevated solvency stress indicators (in percent of total debt at firms of respective sizes; averages across sectors). Notes on variable definitions and thresholds included in figure note.
- Online Annex Figure 1.1.3: Composite Solvency Indicators by Sector — panels show share of debt at mid-sized firms in advanced economies and emerging market economies with elevated solvency stress (in percent of total debt at all firms in these sectors).
- Online Annex Figure 1.1.4: Individual Indicators of Viability by Firm Size — referenced for viability findings.

*ch1onlineannex - References*

### 1.   Share of Debt at Firms with Poor Viability

### 1. Share of Debt at Firms with Poor Viability

### Key findings on firm viability and debt distribution
- Small firms have the largest share of non-viable entities relative to firm size.
- In emerging markets, small firms have stronger balance sheets based on liquidity, solvency, and balance sheet-based viability than small firms in advanced economies, but have considerably lower market valuations based on price-to-book ratios.
- The framework uses multiple indicators including:
  - EBIT = earnings before interest and taxes
  - MT ICR = medium-term interest coverage ratio (EBIT divided by interest expense) or ICR in each of the years 2021-23 for mature firms (with age greater than 5 years)
  - low EBIT margin = EBIT divided by revenues below 4 percent
  - high GD = gross debt divided by assets above 0.5 and growing
  - P/B = price-to-book
  - BS VR = balance sheet-based viability composite indicator
  - MV VR = market valuation-based viability composite indicator
  - HVR = composite viability indicator
- The composite viability indicator (HVR) and individual components are averaged across sectors and reported as shares of total debt at firms of respective sizes.

### Robustness checks
- Altman Z scores:
  - Computed using formulas for listed firms, private firms, and firms in emerging markets.
  - Qualitatively confirm that small firms are riskier than large firms on average.
  - Suggest that default risks are suppressed in emerging markets, which contrasts with some simple corporate stress metrics, such as ICRs, in emerging markets.
- Estimated probabilities of default (PDs) from the University of Singapore:
  - Based on firms’ balance sheets and market prices, including liquidity, solvency, and market-based viability risks.
  - PDs point to modest risks in advanced economies and elevated risks in emerging markets.
  - PDs do not suggest that small firms in advanced economies are weak—contrasting with the findings in this analysis based on a wide range of indicators.
- Graphical findings summarized (as reported in the source):
  - Share of Debt at All Firms with Low Altman Z Score: comparisons by firm size and by advanced vs emerging market economies, distinguishing market-based and balance-sheet Z scores.
  - Share of Debt at All Firms with High Estimated Probabilities of Default: higher PDs concentrated in mid-sized and small firms in emerging markets and in COVID-sensitive sectors.

### Sensitivity analysis (uncertainty addressed and impacts)
- Two sources of uncertainty analyzed:
  1. Uncertainty in forecasts of earnings:
     - Using upside (mean forecast plus two standard deviations) and downside (mean forecast minus two standard deviations) assumptions of earnings affects the share of debt at firms with projected negative equity.
     - The shares of debt at firms with negative equity change by up to 2 percentage points of debt of all firms.
     - Reported chart range values include values from -2.5% to 2.0% (axis labels shown: -2.5%, -2.0%, -1.5%, -1.0%, -0.5%, 0.0%, 0.5%, 1.0%, 1.5%, 2.0%) across firm sizes and country groups.
  2. Construction of composite indicators:
     - Varying the number of individual indicators required to signal high risk for the composite solvency indicator (requiring two, three, or four indicators) materially changes the share of debt with high solvency risk.
     - When only two indicators are required, the share of debt with high solvency risk increases notably because two leverage indicators suggest the highest stress level among individual indicators.
     - Using three indicators is presented as a balanced approach because high leverage may be partly offset by very low interest rates.
     - Reported aggregate ranges for share of debt with high solvency risk span 0% to 100% on the chart axes for different firm sizes and country groups; legend entries include HSR (2 factors), HSR (3 factors), HSR (4 factors).

### Policy-use guidance from sensitivity analysis
- Policymakers can use the sensitivity analysis to calibrate policy support when fiscal space is constrained:
  - If fiscal resources are limited to support viable firms, policymakers could use pessimistic earnings assumptions (mean minus two standard deviations) and a more punitive composite viability risk indicator (fewer indicators required to signal stress) to minimize the number of firms classified as viable and thereby minimize the amount of required support.

### Notes on indicators, data sources, and interpretation
- Sources: S&P Capital IQ; National University of Singapore/CRI database; and IMF staff calculations.
- Notes on indicator thresholds and interpretation:
  - low EBIT margin defined as EBIT divided by revenues below 4 percent.
  - high GD defined as gross debt divided by assets above 0.5 and growing.
  - HVR = composite viability indicator; MV VR = market valuation-based viability composite indicator; BS VR = balance sheet-based viability composite indicator.
- Altman Z scores combine liquidity, solvency, and market-based viability (for advanced economies) risks and were used as a robustness check.
- The PDs and Altman Z scores sometimes yield differing assessments, particularly between advanced economies and emerging markets, highlighting sensitivity to methodology and inputs.

*Source: ch1onlineannex - 1.   Share of Debt at Firms with Poor Viability (PDF chapter/section).*

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_Source: https://www.imf.org/-/media/files/publications/gfsr/2021/april/english/ch1onlineannex.pdf_