## Executive Summary (wpiea2023016-print-pdf)

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---

### Context and motivation
- Governments used a wide variety of policy measures to mitigate the economic fallout from the COVID-19 pandemic, including credit guarantee programs, direct lending, equity purchases, and large-scale loan forbearance and payment moratoria programs.
- Assistance targeted firms of all sizes, especially SMEs, with the aim of averting bankruptcies and layoffs and supporting market and firms’ confidence.
- Authorized envelopes for principal amounts of credit support in advanced economies reached about 10 percent of GDP (with Italy and Germany close to 30 percent of GDP), comparable to traditional “above-the-line” fiscal measures that averaged about 11 percent of GDP.

### Scope and methodology
- Analysis estimates the upfront fiscal cost (the subsidy element) of the largest COVID-19 credit programs in: France, Germany, Italy, Spain, the United Kingdom, Japan, and the US.
- Cost estimates use a fair-value approach: the net present value of projected cash flows to and from the government over the life of underlying loans, approximately as of loan origination dates, with discount rates inferred from quoted or observed market rates and adjusted using fair value principles.
- Program-specific features (borrower characteristics, eligibility, guarantee coverage, interest rates, fees, loan maturities, lender passthrough rules, “skin-in-the-game” requirements, and servicing responsibilities) are incorporated and calibrated with contemporaneous market data on interest rates and credit spreads.
- Reported subsidy estimates are based on realized take-up rather than authorized program envelopes.

### Main quantitative results
- Total subsidies estimated: USD 1.1 trillion.
- Total subsidies excluding the US PPP: USD 330 billion.
- Estimated take-up across programs: USD 1.7 trillion.
- Average subsidy element (total subsidy divided by total take-up): 67 percent.
- Average subsidy element excluding the US PPP: 37 percent.

### Variation across programs and countries
- Subsidy elements varied widely across programs and countries as a function of:
  - Riskiness of target borrowers
  - Size of rate concessions
  - Loan maturity
  - Fees and other program features
- Cross-country variation in subsidy element ranged from 11 to 100 percent.
- Programs with more generous terms (highly subsidized interest rates, relaxed eligibility, long maturities) tended to have both higher take-up rates and higher subsidy elements; stricter programs had lower take-up and lower subsidies.
- Take-up rates are positively but only weakly correlated with the subsidy element.
- Some very large envelopes exhibited low take-up because of comparatively unattractive terms.

### Fiscal risk and interpretive points
- Many governments reported credit assistance “below the line,” producing little immediate budgetary impact and creating a perception that such programs are low-cost even when actual costs could be substantial.
- Fair-value estimates incorporate market-implied expectations about future loan losses and the potential macroeconomic benefits of credit support through contemporaneous market lending rates and credit spreads.
- Estimates are conservative relative to exposure at program inception because they are computed on realized take-up rather than on authorized envelopes.
- The total credit envelope in some countries exceeded 30 percent of GDP, which is a loose upper bound on potential loss exposure; had the recession been deeper and take-up higher, realized losses could have been significantly larger.
- For many loans that mature years in the future, performance could deteriorate if there is another recession.

### Policy implications
- Recognize and quantify fiscal impacts of credit interventions using comprehensive approaches such as fair value.
- Provide policymakers with timely and comparable measures of program cost to weigh credit support against traditional fiscal measures.
- Design program parameters (eligibility, pricing, maturity, fees, lender incentives) with explicit recognition of fiscal tradeoffs.
- Improve transparency and accountability around contingent liability exposures to avoid misperceptions that credit programs are “free.”
- Use fair-value measures to facilitate level comparisons across credit and non-credit assistance and to inform program design and management.

---

### Government Credit-Support Programs: Scope, Terms, and Take-up

### Coverage and data sources
- Coverage: France, Germany, Spain, Italy, the United Kingdom, Japan, and the U.S.; programs cover more than 90 percent of the credit support programs for firms introduced worldwide during the pandemic.
- Main information sources: official reports from ministries, central banks, or public financial institutions; IMF’s “Fiscal Monitor Database of Country Fiscal Measures in Response to the COVID-19 Pandemic”; analyst and media reports; discussions with IMF country teams and country authorities.
- Timing: many programs initiated almost coinciding with lockdown orders in March 2020; European Commission adopted the State Aid Temporary Framework on March 19.

### Authorized envelopes and realized take-up (selected figures)
- Aggregate credit envelope for the programs studied reached nearly 4 trillion USD in total.
- Country-level highlights:
  - United States: programs with the largest total envelope at over 1.4 trillion USD.
  - Germany: close to 900 billion USD of support available.
- Selected program envelope entries (preserved exactly as in source):
  - US Paycheck Protection Program (PPP): 799 Billion USD — Borrower Types: Small Enterprises
  - US Main Street Lending Program: 600 Billion USD — Borrower Types: SMEs
  - US Credit Support for Airlines and Critical Industries: 46 Billion USD — Borrower Types: Airlines and Critical Industries
  - Japan Safety Nets for Financing Guarantees No.4 and No. 5, Special Interest Program (実質無利子・無担保融資等): 53 Trillion Yen / 496 Billion USD — Borrower Types: SMEs
  - Germany KfW Instant Loans: 357 Billion euro / 407 Billion USD — Borrower Types: SMEs
  - Germany WSF*: 400 Billion euro / 457 Billion USD — Borrower Types: Large firms
  - UK Coronavirus Business Interruption Loan Scheme (CBILS): 330 Billion pound / 424 Billion USD — Borrower Types: SMEs
  - France PGE: 300 Billion euro / 342 Billion USD — Borrower Types: All firms affected by COVID-19
  - Italy Fondo Centrale di Garanzia PMI: >100 Billion euro — Borrower Types: Self-Employed, SMEs
  - Italy Public Guarantee for Debt Moratorium: No limit (155 Billion Euro maximum take-up in March 2020)
  - Italy SACE Garanzia Italia: 200 Billion Euro / 228 Billion USD — Borrower Types: Medium and large companies
  - Spain ICO loan guarantees: 140 Billion Euro / 160 Billion USD — Borrower Types: SMEs
- Realized take-up:
  - Total take-up by firms in the seven advanced economies reached over USD1.7 trillion.
  - Simple average take-up across countries is about 8 percent of 2020 GDP.
  - Take-up averaged 48 percent of the announced envelope (excluding Germany and Italy).
  - In most countries, take-up was less than half the announced envelope size.
- Unlimited envelopes: Germany and Italy announced guarantee schemes with no legal limits; analysis treats announced caps on loss absorption as the envelope amount where applicable.

### Eligibility, targeting, and program design features
- Firm-size tiering was common; firm size often determined eligibility and loan conditions.
- Some programs conditioned availability on a decline in revenues; exclusions applied to firms in financial difficulty before the pandemic.
- Guarantee coverage in assessed programs ranged from 70 percent to 100 percent.
- EU temporary framework: guarantee fees range from 25 basis points to 200 basis points and loan-size ceilings tied to wage bill or turnover limits.
- Five fully guaranteed programs among thirteen explored:
  - US Paycheck Protection Program
  - Germany KfW Instant Loan
  - UK Bounce Back Loan Scheme
  - Japan Safety Net Guarantee No. 4 and No. 5
  - Italy Fondo di Garanzia
- Operational model: firms apply to authorized private lenders; lenders assess and refer applications to public financial institutions; the state bears a contingent liability either directly or indirectly.

### Factors affecting take-up and cross-country differences
- Determinants of take-up: size of economic shock, attractiveness of program terms, bottlenecks in financial intermediaries, availability of non-credit relief measures, operational capacity.
- Country-specific notes:
  - Spain: greater recourse to guaranteed loans partly due to lower availability of alternative fiscal relief measures.
  - France: higher take-up reflected favorable pricing, especially during the first year.
  - Germany: limited use due to lower financing needs, less favorable lending terms, and supply-side bottlenecks.
  - Italy: initial operational bottlenecks led to limited take-up, which increased over time.

---

### Rationale and Methodology for Fair-Value Subsidy Estimation

### Conceptual rationale for fair value
- Credit assistance involves legally binding commitments with highly uncertain future cash flows; measuring upfront multi-year costs requires an accrual basis (net present value of projected cash inflows and outflows).
- Fair value chooses discount rates with reference to market rates that are “risk-adjusted,” reflecting time value and compensation for priced risks.
- Using government borrowing rates for discounting understates cost because it treats taxpayer risk-absorption as costless.

### Subsidy element definition and valuation approach
- Subsidy element = present value of government costs per 100 of loan principal; an upfront cost rate (not an annual rate).
- Chosen valuation approach: discounted present value of promised cash flows discounted at the estimated interest rate a private lender would have charged for a similar loan without government backing (promised-rate discounting).
- Promised-rate discounting serves as a sufficient statistic for expected defaults, recoveries, and risk premiums, avoiding separate estimation of default rate (d), recovery rate (ρ), and expected return (R) when data are scarce.

### Illustrative numerical examples (preserved exactly)
- Direct loan example:
  - 2-year loan, borrower rate 3%, no payments in year 1, full repayment at end of year 2.
  - If private investor present value = EUR 92,663.11 for a EUR 100,000 face value, then:
    - Subsidy element = ((100,000 – 92,663.11) / 100,000) x 100 = 7.34
    - Equivalent upfront subsidy amount = EUR 7,340
  - If private lender rate C = 7%, present value = 100,000 x (1.03)2/(1.07)2 = EUR 92,663.11 producing subsidy element 7.34.
- 100% loan guarantee example:
  - Same borrower-facing terms; when 3% equals government borrowing rate and market discount for guaranteed cash flows, subsidy cost for a 100% guarantee equals that for a direct loan: borrower subsidy EUR 7,340; subsidy element 7.34.
- Lender subsidy incidence example:
  - Government borrowing rate = 2%, borrower rate = 3% with 100% guarantee:
    - Lender present value of safe cash inflow = 100,000 x (1.03)2/(1.02)2 = 101,970.4
    - Lender subsidy = 101,970.4 − 100,000 = EUR 1,970.4
- Partial guarantee example (government absorbs 80% of losses; C = 7%; government rate f = 3%):
  - Government subsidy = 5,961.04 (subsidy element = 5.96)
  - Borrower subsidy = 5,891.87
  - Lender subsidy = 69.2

### Administrative costs, fees, and discounting practice
- Administrative costs modeled as: an upfront fixed origination/collection cost plus a variable annual servicing cost, both scaled by loan principal.
- Fees and guarantee fees are risky (stop at default) and discounted at the assessed fair market borrowing rate.
- Payments from government to lenders that offset administrative costs become part of borrower subsidy if they reduce fees; payments in excess create a lender subsidy.
- For mandated pass-through programs, borrower rates solved to approximately cover banks’ funding, risk-bearing and administrative costs.

### Practical issues in inferring discount rates and cash flows
- Challenges: private-sector interest rates and terms are rarely disclosed; must rely on aggregated market data and judgmental mapping from borrower/program characteristics to observable market data.
- Objectives: produce unbiased estimates reflecting market information around program introduction and use consistent assumptions across similar programs.
- Avoid using temporary market-distress spikes (e.g., March spike) in base case rates.

---

### Market Rates, Parameter Choices, and Sensitivity Analysis

### Market rates and base-case treatment
- Noted market behavior: By mid-April, it dropped to 8%. It fell to 6% by the first week of June and stayed in the 5-6% range through the beginning of November.
- Highly elevated March rates were not used for base case calculations on the grounds that the spike may have reflected temporary market disruption.

### Standardized parameter choices (preserved exactly)
- Fair market lending spread assumptions:
  - SME programs with minimal screening and 100% guarantees: credit spread set to 15%.
  - Programs aimed at small to medium-sized firms with 80 to 90% guarantees and tighter eligibility: credit spread set to 10%.
  - Programs aimed at large firms (70% guaranteed) with highest scrutiny and possible collateral: fair credit spread set to 6%.
- Banks’ assumed own borrowing rates: 0.5 percentage points higher than the government rate.
- Lender administrative cost assumptions (based on ranges in Berg et. al. (2016)):
  - Fixed component set to 4% for programs directed at SMEs.
  - Fixed component set to 3% for those targeting medium-sized firms.
  - Fixed component set to 2% for large firms.
  - Annual variable cost set to 0.15% for all firms.
- Borrower rate treatment:
  - Some SME programs had borrower rates fixed by program rules; for most other programs banks could set borrower rates to recover costs, with program rules intended to pass guarantee value to borrowers.

### Loan maturity assumptions and effects
- Subsidies increase in maturity because annual interest savings last longer.
- Where ranges were permitted:
  - Assumed half of loans had the maximum allowable maturity and half had a shorter maturity matching average maturity of loans made to non-financial corporations for 2020Q2.
- Specific maturity sensitivity setups:
  - For nine programs with maximum maturity of 6 years: low-end assumes all loans mature in 3 years; high-end assumes all loans mature in 6 years.
  - For three programs with maximum maturity of 10 years: ranges vary (two use 4 years or 10 years; Japan Safety Net uses 6 years or 10 years).
  - UK BBLS: range taken as 5 years to 8 years.
  - Two Spanish programs: sensitivity uses 4 years to 7 years.
- Example: extending Spanish program maturity from 5 to 7 years increased the subsidy element by 5.4 percentage points.

### Sensitivity analysis results (preserved approach and example ranges)
- Fair-value lending spread sensitivity:
  - Base case spreads: 15 percent for SMEs, 10 percent for small and mid-sized firms, 6 percent for large firms.
  - Sensitivity ranges use credit spreads 2.5 percentage points higher and lower than the base case (e.g., SME programs recalculated with spreads of 12.5 percent and 17.5 percent).
  - Resulting change in subsidy element ranges from 4.5 to 7 percentage points across programs, with variation reflecting differences in loan maturity and guarantee share.
- For SME programs with government-fixed lending rates, shifting the assumed fixed administrative cost by 1 percent lowers lender subsidy by 1 percent and raises borrower subsidy by 1 percent; total subsidy is unchanged.
- Sensitivities change one parameter at a time rather than simultaneously.

---

### Subsidy Estimates: Patterns, Magnitudes, and Incidence

### Cross-program subsidy patterns
- Subsidy estimates reported for 13 government credit support programs, broken into 17 sub-programs.
- For fully guaranteed loans, subsidy rates range from 44 percent to 100 percent of principal, with the US Paycheck Protection Program (PPP) an outlier at the high end due to likely forgiveness of loans that didn’t default.
- Subsidy rates for partial guaranteed loans to SMEs and mid-caps ranged from 20 to 27 percent of principal.
- Programs targeting large firms had subsidy elements ranging from 10 to 12 percent of principal.

### Cross-country totals and GDP normalization
- US total subsidy cost is close to 800 billion dollars.
- Japan estimated cost is more than 150 billion USD.
- UK had the smallest cost at about 10 billion USD.
- When normalized by GDP:
  - The US and Japan provided subsidies in excess of 3 percent of GDP.
  - Other countries’ subsidies ranged from 0.3 (UK) to 2.4 percent (Italy) of GDP.

### Lender versus borrower subsidy incidence
- For most programs, rules required the value of guarantees to be passed through to borrowers; borrower rates were chosen so lender subsidies were close to zero.
- Significant lender subsidies arose in fully guaranteed programs with a mandated borrower rate.
  - Germany KfW Instant: lender subsidy element of 11 percent.
  - UK BBLS: lender subsidy element of 4 percent.
- A few programs had a small negative lender subsidy, possibly due to lender costs being lower than assumed or other compensating factors.

### Cross-program observations and take-up relationships
- Fully guaranteed, “automatic” or “instant” programs featured limited lender screening, below-market borrower rates, and relatively generous compensation to lenders, contributing to higher subsidy rates.
- Guarantee schemes targeting smaller firms show higher subsidy elements.
- No clear relationship across schemes between take-up (relative to GDP) and subsidy element; relationships are noisy and influenced by maturity, guarantee share, fees, and other concessions.
- Examples:
  - KfW Instant Loans: relatively low take-up and relatively high subsidy element.
  - Spain’s ICO SME program: high take-up and moderate subsidy element.

---

### Spain: Program Details and Take-up

### Overview
- Two credit guarantee programs with a combined envelope of €140 billion (€100 billion initially) to support self-employed workers and businesses.
- Programs administered by Instituto de Crédito Oficial (ICO), Ministry of the Economic Affairs and Digital Transformation.
- As of December 31, 2021:
  - More than 1.1 million firms benefited.
  - 98 percent of beneficiaries are self-employed and SMEs.
  - Combined take-up totalled €13.5 billion.

### ICO Líneas Avales “Liquidez” COVID 19 (El Real Decreto-ley 8/2020 de marzo)
- Purpose: support businesses’ liquidity (working capital) needs.
- Guarantee shares by firm size:
  - SMEs: 80 percent guarantee.
  - Newly issued loans to large corporations: 70 percent guarantee.
- Guarantee maturity: initially 5 years and later extended to 8 years.
- Participation and take-up (as of December 31, 2021):
  - 619,118 participants.
  - Total take-up of €121.9 billion.
  - 90 percent of participating companies are micro-enterprises and self-employed.

### ICO Líneas Avales “Inversión y actividad” (El Real Decreto-ley 25/2020, de 3 de julio)
- Purpose: financing for current and capital expenses with new investments, expansion, adaptation or renewal of equipment, facilities and capacities.
- Loan conditions: same as for the liquidity program.
- Requirement: firms required to demonstrate an investment need.
- Participation and take-up (as of December 31, 2021):
  - 102,755 participants.
  - Take-up was €13.5 billion.
  - 90 percent of companies with formalized operations are micro-enterprises or self-employed individuals.

---

### Annex II — Further considerations in applying fair value principles

### Distinction between fair value and market value
- In well-functioning markets, fair values reference observed market prices; when comparable market data are unavailable, fair value permits interpolation and model-based approximations as a best estimate of prices or discount rates in a well-functioning market.
- Fair value allows adjustments to avoid inflating costs by distress premiums.

### Conceptual recap and discount-rate implications
- Goal: provide an upfront accrual estimate; when present value of government cash inflows falls short of outflows, the difference is government cost and participant subsidy.
- Default losses are often the largest source of risk.
- Taxpayers and other government stakeholders function as equity holders in risky government investments; government debt rates are not the correct rates to value risky government investments.

### Resisting cash-basis biases and the role of market prices
- Cash accounting can mislead when accruals and cash diverge; accruals recognize time value and risk-bearing costs.
- All discount rates come from market prices; choice concerns which market prices to use.
- Arguments against selective exclusion of liquidity premiums lack a robust practical procedure; fair value imposes consistent rules.

### Market incompleteness and valuing program benefits
- Market incompleteness complicates efficiency judgments and valuation of program benefits; valuing social benefits of pandemic-era credit assistance is outside the scope of this analysis.

### Practical considerations for implementation
- Accrual/fair-value estimates are more complex but can be standardized, centralized, or outsourced to reduce resource constraints and improve comparability and transparency.

---

### Conclusion (key takeaways)

- Total upfront fair-value subsidies estimated at USD 1.1 trillion (USD 330 billion excluding the US PPP).
- Average subsidy element reported as 67 percent (average subsidy element elsewhere in report stated as 34 percent divided by principal depending on aggregation; source reports both formulations in different sections).
- Subsidy element varies widely across programs driven by borrower risk, rate concessions, maturities, fees, and program features.
- Credit support often reported off-budget, understating fiscal costs and risks; fair-value accrual estimates improve transparency and fiscal management.
- At inception, credit envelopes in some countries reached as much as 30 percent of GDP; realized losses depend on future macroeconomic developments and loan performance.
- Debt moratoria and forbearance provided substantial support; their subsidy cost depends on structure and whether government payments to lenders are expected not to be fully recovered.

*Source: wpiea2023016-print-pdf*

### Executive Summary ......................................................................................................

### Executive Summary

### Context and motivation
- Governments used a wide variety of policy measures to mitigate the economic fallout from the COVID-19 pandemic, including credit guarantee programs, direct lending, equity purchases, and large-scale loan forbearance and payment moratoria programs.
- Assistance targeted firms of all sizes, especially SMEs, with the aim of averting bankruptcies and layoffs and supporting market and firms’ confidence.
- Authorized envelopes for principal amounts of credit support in advanced economies reached about 10 percent of GDP (with Italy and Germany close to 30 percent of GDP), comparable to traditional “above-the-line” fiscal measures that averaged about 11 percent of GDP.

### Scope and methodology
- The analysis estimates the upfront fiscal cost (the subsidy element) of the largest COVID-19 credit programs in: France, Germany, Italy, Spain, the United Kingdom, Japan, and the US.
- Cost estimates use a fair-value approach: the net present value of projected cash flows to and from the government over the life of underlying loans, approximately as of loan origination dates, with discount rates inferred from quoted or observed market rates and adjusted using fair value principles.
- Program-specific features (borrower characteristics, eligibility, guarantee coverage, interest rates, fees, loan maturities, lender passthrough rules, “skin-in-the-game” requirements, and servicing responsibilities) are incorporated and calibrated with contemporaneous market data on interest rates and credit spreads.
- Reported subsidy estimates are based on realized take-up rather than authorized program envelopes.

### Main quantitative results
- Total subsidies estimated: USD 1.1 trillion.
- Total subsidies excluding the US PPP: USD 330 billion.
- Estimated take-up across programs: USD 1.7 trillion (far smaller than program envelopes).
- Average subsidy element (total subsidy divided by total take-up): 67 percent.
- Average subsidy element excluding the US PPP: 37 percent.

### Variation across programs and countries
- Subsidy elements varied widely across programs and countries as a function of:
  - Riskiness of target borrowers
  - Size of rate concessions
  - Loan maturity
  - Fees and other program features
- Take-up and subsidy elements varied widely within countries and across countries.
- Cross-country variation in subsidy element ranged from 11 to 100 percent.
- Programs with more generous terms (highly subsidized interest rates, relaxed eligibility, long maturities) tended to have both higher take-up rates and higher subsidy elements; stricter programs had lower take-up and lower subsidies.
- Take-up rates are positively but only weakly correlated with the subsidy element.
- Some very large envelopes exhibited low take-up because of comparatively unattractive terms.

### Fiscal risk and interpretive points
- Many governments reported credit assistance “below the line,” producing little immediate budgetary impact and creating a perception that such programs are low-cost even when actual costs could be substantial.
- Fair-value estimates incorporate market-implied expectations about future loan losses and the potential macroeconomic benefits of credit support (e.g., reduced business disruptions and faster recoveries) through contemporaneous market lending rates and credit spreads.
- Estimates are conservative relative to exposure at program inception because they are computed on realized take-up rather than on authorized envelopes.
- The total credit envelope in some countries exceeded 30 percent of GDP, which is a loose upper bound on potential loss exposure; had the recession been deeper and take-up higher, realized losses could have been significantly larger.
- For many loans that mature years in the future, performance could deteriorate if there is another recession.

### Conclusions and implications for policy and fiscal management
- The significant costs of COVID-19 credit programs underscore the importance of:
  - Recognizing and quantifying the fiscal impacts of credit interventions using comprehensive approaches such as fair value.
  - Providing policymakers with timely and comparable measures of program cost to weigh credit support against traditional fiscal measures.
  - Designing program parameters (eligibility, pricing, maturity, fees, lender incentives) with explicit recognition of fiscal tradeoffs.
  - Improving transparency and accountability around contingent liability exposures to avoid misperceptions that credit programs are “free.”
- Fair-value measures facilitate level comparisons across credit and non-credit assistance and inform effective program design and management.

*Source: Executive Summary, wpiea2023016-print-pdf.*

### 2. Government Credit-Support Programs for Firms during the COVID-19 Pandemic

### 2. Government Credit-Support Programs for Firms during the COVID-19 Pandemic

### Scope and data
- Coverage: programs in the five largest economies in Europe (France, Germany, Spain, Italy, and the United Kingdom), Japan, and the U.S.; these programs cover more than 90 percent of the credit support programs for firms that were introduced in the world during the pandemic.
- Main information sources: official reports from ministries, central banks, or public financial institutions; IMF’s “Fiscal Monitor Database of Country Fiscal Measures in Response to the COVID-19 Pandemic”; analyst and media reports; discussions with IMF country teams and country authorities.
- Timing: programs were introduced swiftly, with many initiations almost coinciding with the announcement of lockdown orders in March 2020. At the supranational level, the European Commission adopted the State Aid Temporary Framework on March 19.

### Authorized envelopes and program magnitudes
- Aggregate: the credit envelope for the programs studied reached nearly 4 trillion USD in total.
- Country-level highlights:
  - United States: programs with the largest total envelope at over 1.4 trillion USD.
  - Germany: close to 900 billion USD of support available.
- Selected program envelope entries (as presented in the source table):
  - US Paycheck Protection Program (PPP): 799 Billion USD (Envelope (LCD) 799 Billion USD) — Borrower Types: Small Enterprises
  - US Main Street Lending Program: 600 Billion USD — Borrower Types: SMEs
  - US Credit Support for Airlines and Critical Industries: 46 Billion USD — Borrower Types: Airlines and Critical Industries
  - Japan Safety Nets for Financing Guarantees No.4 and No. 5, Special Interest Program (実質無利子・無担保融資等): 53 Trillion Yen / 496 Billion USD — Borrower Types: SMEs
  - Germany KfW Instant Loans: 357 Billion euro / 407 Billion USD — Borrower Types: SMEs
  - Germany WSF*: 400 Billion euro / 457 Billion USD — Borrower Types: Large firms
  - UK Coronavirus Business Interruption Loan Scheme (CBILS): 330 Billion pound / 424 Billion USD — Borrower Types: SMEs
  - UK Bounce-Back Loan Scheme (BBL): included in UK listings — Borrower Types: SMEs
  - France PGE: 300 Billion euro / 342 Billion USD — Borrower Types: All firms affected by COVID-19
  - Italy Fondo Centrale di Garanzia PMI: >100 Billion euro — Borrower Types: Self-Employed, SMEs
  - Italy Public Guarantee for Debt Moratorium: No limit (155 Billion Euro maximum take-up in March 2020)
  - Italy SACE Garanzia Italia: 200 Billion Euro / 228 Billion USD — Borrower Types: Medium and large companies
  - Spain ICO loan guarantees: 140 Billion Euro / 160 Billion USD — Borrower Types: SMEs
- Unlimited envelopes: Germany and Italy announced guarantee schemes with no legal limits; for analysis the authors treat announced caps on loss absorption as the envelope amount where applicable.
- Realized take-up:
  - Total take-up by firms in the seven advanced economies reached over USD1.7 trillion.
  - Simple average take-up across countries is about 8 percent of 2020 GDP.
  - Take-up averaged 48 percent of the announced envelope (excluding Germany and Italy).
  - In most countries, take-up was less than half the announced envelope size.

### Eligibility and targeting
- Firm-size differentiation: several countries used tiering structures with differentiated offerings and rules for different firm size categories; firm size was often an important determinant of eligibility and loan conditions.
- Revenue shortfall condition: some programs conditioned availability on a decline in revenues during the pandemic; firms had to present evidence of revenue shortfalls compared to previous years.
- Exclusions: firms in financial difficulty before the pandemic were not eligible for loans (for instance, EU temporary framework).
- Targeting challenges:
  - Objective measures such as “revenue shortfall” during initial lockdown months were not reliable indicators of need.
  - Assessing firm viability was difficult under uncertainty.
  - Some smaller firms without existing bank relationships may have been discouraged by application requirements.
  - Uneven sectoral impact of COVID-19 complicated effective targeting.

### Guarantee program terms (common elements and variations)
- Typical elements of guarantee programs:
  - Program targets (which firm sizes are eligible).
  - Guarantee coverage: share of losses absorbed by the government in default.
  - Terms: interest-rate setting, loan maturities, eligible loan structures, guarantee fees or premiums, loan size limits.
- Range and examples:
  - Guarantee coverage in assessed programs ranged from 70 percent to 100 percent.
  - EU temporary framework: guarantee fees (premiums) range from 25 basis points to 200 basis points, increasing with duration and firm size.
  - EU ceiling on loan size for programs subject to the EU temporary framework: total amount should not exceed (i) double the annual wage bill of the beneficiary for 2019 or for the last year available; or (ii) 25 percent of the beneficiary’s total turnover in 2019 (exceptions allowed with justification and self-certification).
- UK CBILS example (program-level details preserved exactly):
  - Loan sizes: 50,000 GBP to 5 million GBP, with available amounts depending on firm characteristics.
  - Loan maturities: 3 months to 6 years.
  - Collateral: not required on most loans.
  - Guarantee coverage: 80 percent.
  - Annual guarantee fee charged to lenders: 75 basis points.
  - Government paid the first 12 months of interest and fees, up to a maximum of 800,000 GBP.
  - Loan pricing: at lender discretion, but lenders had to demonstrate that the net financial advantage of the guarantee was passed through to the borrower.
  - Authorized volume: 98,000 loans totaling 23.3 billion GBP.
- Full (100%) guarantee programs: out of thirteen loan guarantee programs explored, five offered a full 100% government guarantee:
  - US Paycheck Protection Program
  - Germany KfW Instant Loan
  - UK Bounce Back Loan Scheme
  - Japan Safety Net Guarantee No. 4 and No. 5
  - Italy Fondo di Garanzia
- Common features of full-guarantee schemes versus partial-guarantee programs:
  - (i) quicker disbursement and significantly less credit risk assessment;
  - (ii) longer maturity;
  - (iii) lower maximum loan amount.

### Public and private roles in program delivery
- Typical operational model: firms apply to authorized private lenders; the lender assesses and refers applications to a national public financial institution that administers the program.
- Financing and liability:
  - The state bears a contingent liability in all cases.
  - Liability is direct in some cases (e.g., Germany’s WSF program where the guarantee is directly issued by the Ministry of Finance).
  - Liability is indirect in other cases (e.g., German KfW must use its own funds first; similar for the Japan Financial Corporation).
- Trade-offs: for fully guaranteed loans, additional credit assessment by public financial institutions was discouraged to enable swift disbursement, increasing the risk of default or fraud.

### Factors affecting take-up and cross-country differences
- Determinants of take-up include:
  - Size of economic shock.
  - Attractiveness of program terms (pricing, maturity, guarantee share).
  - Bottlenecks in financial intermediaries’ ability to assess and process loans.
  - Availability of non-credit relief measures (e.g., debt moratoria, direct grants).
  - Operational capacity such as information technology used by banks to process online applications.
- Country-specific observations:
  - Spain: greater recourse to guaranteed loans partly attributed to lower availability of alternative fiscal relief measures.
  - France: higher take-up reflected favorable pricing offered to borrowers, especially during the first year of the loan.
  - Germany: relatively limited use reflected (i) lower financing needs due to less stringent lockdown and greater use of other measures (direct grants, tax deferrals, short-time working allowances); (ii) less favorable lending terms (higher rates, distribution and remuneration restrictions); (iii) supply-side bottlenecks related to risk assessment for large loans.
  - Italy: initial limited take-up due to operational bottlenecks and varying levels of bank IT; take-up continued to increase over time despite a debt moratorium scheme.
- Empirical note: Section 4 of the source examines quantitatively the relation between take-up, estimated subsidy value, and other loan characteristics.

### Transition to subsidy-cost estimation
- Next analytical steps (as introduced in the source):
  - Review of theoretical and practical rationales for evaluating fiscal cost of government credit programs on a fair-value basis and contrast with other government budgeting and reporting practices.
  - Introduction of the framework used to produce fair value estimates with simplified examples.
  - Explanation of principles governing choice of model parameters and reporting of baseline parameter values standardized across programs.
- Further details and a more extensive discussion of applicability and issues are provided in Annex 2 of the source.

*Source: IMF Working Paper — 2. Government Credit-Support Programs for Firms during the COVID-19 Pandemic*

### 3.1 Rationale for Fair-Value Cost Estimation for Credit Assistance

### 3.1 Rationale for Fair-Value Cost Estimation for Credit Assistance

### Key conceptual rationale
- Credit assistance (loan guarantees, direct loans, loan participations) involves legally binding commitments that often extend over years or decades and entail highly uncertain future cash flows, unlike most conventional fiscal policies with shorter commitment periods and less uncertainty.
- To measure upfront, multi-year costs comprehensively when resources are committed requires an accrual rather than a cash basis of accounting; accruals are calculated as net present values of projected cash inflows and outflows over the commitment period.
- The critical distinguishing decision for accruals is the conceptual basis for selecting discount rates. Under a fair value approach, discount rates are chosen with reference to market rates and therefore are “risk-adjusted,” reflecting time value and compensation for priced risks (market risk, interest rate, prepayment, liquidity risk).
- Fair value principle: discount rates should be selected on the basis of the priced risks associated with the cash flows of the loan or guarantee, independently of how the investment is financed.
- Using government borrowing rates for discounting (common in some governments’ accrual implementations) understates the cost of credit support because it treats taxpayer risk-absorption as costless and ignores that risky investments cannot be funded entirely with safe borrowing.

### Fiscal-signaling and risk-incorporation arguments
- Fair value cost estimates make salient the longer-run fiscal effects of credit support, including the concentration of large losses during severe economic downturns when fiscal capacity is strained.
- A fair value approach implicitly prices higher the future losses that occur during downturns, thereby incorporating the fiscal risk that average outcomes understate.

### Current budgetary reporting practices and limitations
- Off-budget (below-the-line) reporting: common cross-country practice; delays recognition of costs and risks relative to other fiscal measures and can understate costs, creating an incentive to rely on credit support excessively.
- Cash-basis accounting: reports cash flows when realized; for guarantees, delays recognition until defaults are realized and thus often fails to inform upfront policy decisions; for direct loans, treating principal outlay as cost can overstate losses when repayment is likely.
- Accrual accounting: aims to measure lifetime cost at commitment; rare internationally though the U.S. applies accruals for most credit programs (with noted shortcomings such as use of Treasury rates for discounting and separate accounting for administrative costs that cause underreporting).

### Debate and counterarguments
- Common objections to risk-adjusted discount rates include relevance of non-cash costs and market rates for governments, incomplete markets, and practical implementation challenges (discussed in Annex 2).
- The authors judge that stronger arguments favor fair value; the paper reports cost estimates on a fair value basis.

---

### Methodology for calculating the “subsidy element” (upfront subsidy per $100 principal)
- Definition: subsidy element = present value of government costs per 100 of loan principal; an upfront cost rate (not an annual rate). It is the grant-equivalent cost of credit support.
- Interpretations:
  - For borrowers: the upfront payment a competitive private lender would charge the government to offer credit to the borrower on identical terms without government support.
  - For guaranteed lenders: the excess of lender receipts (fees and loan payments) over normal lender costs (administration, funding, risk).
- Chosen valuation approach: discounted present value of promised cash flows discounted at the estimated interest rate a private lender would have charged for a similar loan without government backing (a quoted or promised rate). Advantages: robustness to limited data, common usage, familiarity to policymakers, simplicity and transparency.
- Promised-rate discounting (C) serves as a sufficient statistic for combined effect of expected defaults, recoveries, and risk premiums—avoids separately estimating default rate (d), recovery rate (ρ), and expected return (R) when data are scarce.

### Illustrative direct loan example (numerical)
- Program terms: 2-year loan, annual interest rate charged to borrowers fixed at 3%, no payments in year 1, full repayment of principal and interest at end of year 2.
- Promised cash repayment on a EUR 100,000 loan: 100,000 x (1+.03)2 at end of two years.
- If inferred present value paid by private investor for that promise = EUR 92,663.11, then:
  - Subsidy element = ((100,000 – 92,663.11) / 100,000) x 100 = 7.34
  - Equivalent upfront subsidy amount = EUR 7,340
- Alternative derivation using market lending rate: if private lender rate C = 7%, then present value = 100,000 x (1.03)2/(1.07)2 = EUR 92,663.11 producing the same subsidy element of 7.34.

### Illustrative 100% loan guarantee example
- Same borrower-facing terms (3% on 2-year loans) but lenders are guaranteed in full by government.
- Table of cash flows (t = 0, t = 2) summarized in text; government cash flow at t = 2 is pmt2 − 100,000 x (1.03)2.
- When 3% equals government borrowing rate and market discount for guaranteed cash flows:
  - Lender NPV = -100,000 + [100,000 x (1.03)2/(1.03)2] = 0 (lender breaks even)
  - Government NPV = 100,000 x (1.03)2/(1.07)2 – 100,000(1.03)2/(1.03)2 = -7,340
  - Conclusion: subsidy cost for a 100% guarantee equals that for a direct loan (borrower subsidy EUR 7,340; subsidy element 7.34) when lending rate equals government borrowing rate.

### Lender subsidy incidence (numerical)
- If government borrowing rate = 2% and borrower rate = 3% with 100% guarantee:
  - Lender present value of safe cash inflow = 100,000 x (1.03)2/(1.02)2 = 101,970.4
  - Lender subsidy = 101,970.4 − 100,000 = EUR 1,970.4 (i.e., government provides EUR 1,970.4 to lender)
- Higher mandated borrower rates increase potential lender subsidy absent pass-through rules.

### Partial guarantees and allocation of subsidies (numerical)
- Example: government absorbs 80% of losses, lenders bear 20%; government borrowing rate = 3%, market risky loan rate = 7%; loans due in two years.
- Borrower rate set to weighted average .2 x C + .8 x f where C = 7% and f = government rate (3%); since administrative costs abstracted, f = 3%.
- Government cash flows equivalent to a direct risky loan of EUR 80,000 funded by a zero-coupon bond with face value 80,000 x (1 + .8 x .03 + .2 x .07)2.
- Government present value calculation yielded:
  - 80,000 x (1 + .8 x .03 + .2 x .07)2/(1.07)2 − 80,000 x (1 + .8 x .03 + .2 x .07)2/(1.03)2 = 75,286.51 – 81,247.54 = -5,961.04
  - Government subsidy = 5,961.04 (subsidy element = 5.96)
- Borrower present value and subsidy:
  - Borrower PV = 100,000 x (1 + .8 x .03 + .2 x .07)2 = 94,108.13
  - Borrower subsidy = 100,000 − 94,108.13 = 5,891.87
- Lender subsidy:
  - .8 x 100,000 x (1 + .8 x .03 + .2 x .07)2/(1.03)2 + .2 x 100,000 x (1 + .8 x .03 + .2 x .07)2/(1.07)2 − 100,000 = 100,069.2 − 100,000 = 69.2
  - Small lender subsidy in this design, reflecting objective of passing value to borrowers.

### Administrative costs and fees
- Administrative costs are modeled as two components scaled by loan principal: an upfront fixed origination/collection cost and a variable annual servicing cost.
- Fees and guarantee fees: future fee payments are risky (stop at default) and are discounted at the assessed fair market borrowing rate because their risk is closely related to that of the underlying loan.
- Payments from government to lenders that offset administrative costs become part of the borrower subsidy if they reduce fees borne by borrowers; payments in excess of normal costs create a lender subsidy element.
- When programs allow lenders to recover normal administrative costs, the assumed borrowing rate is adjusted to allow for cost recovery; in practice one can solve for a borrower rate that sets lender subsidy to zero.

### Practical issues in inferring discount rates and cash flows
- Challenges: government-supported loans often differ from private offerings (longer maturities, riskier borrowers); private-sector interest rates and terms are rarely disclosed.
- Analysts must rely on aggregated market data (bond credit spreads, credit card rates, average bank lending rates) and judgmental mapping from borrower/program characteristics to observable market data.
- Objectives in selecting rates:
  - Produce unbiased estimates reflecting market information around program introduction.
  - Use consistent assumptions across similar programs unless objective reasons exist to distinguish.
- Avoid using reference rates elevated due to market distress; example: U.S. high yield bond spread hovered around 4% in late February and spiked to a peak of 11% on March [text truncated in source].

*Source: 3.1 Rationale for Fair-Value Cost Estimation for Credit Assistance, wpiea2023016-print-pdf*

### 23. By mid-April, it dropped to 8%. It fell to 6% by the first week of June and stayed in the 5-6% range

### 23. By mid-April, it dropped to 8%. It fell to 6% by the first week of June and stayed in the 5-6% range

### Market rates, March spike, and treatment in base case
- By mid-April, it dropped to 8%. It fell to 6% by the first week of June and stayed in the 5-6% range through the beginning of November.
- Credit markets in other countries also experienced temporary rate spikes.
- The highly elevated March rates were not used for any base case calculations on the grounds that the spike may have reflected a temporary period of market disruption rather than a sharp increase in expected defaults.

### Administrative costs, fees, and implications for subsidy measurement
- Administrative costs and fees have a significant effect on subsidy estimates and add complexity to analysis.
- Quoted interest rates in some programs are set to cover administrative costs; in others, fees or other charges substitute for higher rates.
- Discount rates inferred from secondary market price data (e.g., from bond market prices) do not include associated administrative costs, whereas loan rates may be set to cover non-interest expenses; adjustments are made for those differences.
- Fees vary across programs and may include a combination of upfront and periodic fees.
- Fee payments flow in multiple directions: from the government to lenders, from lenders to the government, and from borrowers to lenders.
- Accurately capturing the size and flow of these payments is important for assessing both borrower and lender subsidies.
- The same or similar assumptions were used across similar programs unless there was an objective reason to distinguish, to avoid adding noise into comparisons of drivers of costs.

### Standardized parameters for imputing cash flows and discount rates
- Parameters are set to standardized values in the absence of program- or country-specific data.
- Several interest rates are needed: a base government rate, the fair market lending rate, the lending rate charged to borrowers (borrower rate), and the bank’s cost of borrowed funds.
- The term structure of government bonds provides the observable base rate for each country.
- Fair market lending rate assumptions:
  - SME programs with minimal screening and with 100% guarantees: credit spread set to 15%.
  - Programs aimed at small to medium-sized firms with 80 to 90% guarantees and tighter eligibility: credit spread set to 10%.
  - Programs aimed at large firms (70% guaranteed) with highest scrutiny and possible collateral: fair credit spread set to 6%.
- Reference points cited include unsecured credit card spreads and ICE BofA US high yield index and ICE BofA US BB US High Yield Index Option-Adjusted Spread (noting the BB spread index ranged between 4% and 6% for most of the lending period).
- Borrower rate treatment:
  - Some SME programs had borrower rates fixed by program rules.
  - For most other programs, banks could set borrower rates to recover costs, with the value of the guarantee supposed to be passed through to borrowers; some programs capped rates.
  - For mandated pass-through programs, borrower rates were solved to approximately cover banks’ funding, risk-bearing and administrative costs.
  - Assumed banks’ own borrowing rates are 0.5 percentage points higher than the government rate.
- Lender administrative cost assumptions (based on ranges in Berg et. al. (2016)):
  - Fixed component set to 4% for programs directed at SMEs.
  - Fixed component set to 3% for those targeting medium-sized firms.
  - Fixed component set to 2% for large firms.
  - Annual variable cost set to 0.15% for all firms.

### Loan maturities: assumptions and effects on subsidies
- Distribution of loan maturities significantly affects subsidy estimates; subsidies increase in maturity because annual interest savings last longer.
- Some programs mandated specific maturities (e.g., 5 years); others allowed a range (e.g., 3 months to 7 years).
- Where a range was permitted, the assumption was:
  - Half of the loans had the maximum allowable maturity, and half had a shorter maturity.
  - The shorter maturity was chosen to match the average maturity of loans made to non-financial corporations for 2020Q2.
- Realized maturities were often unavailable or may not represent expected outcomes at program inception; program expansions and eased repayment terms over time further complicate realized-outcome proxies.

### Subsidy estimates for pandemic credit programs: patterns and magnitudes
- Subsidy estimates reported for 13 government credit support programs, broken into 17 sub-programs.
- For fully guaranteed loans, subsidy rates range from 44 percent to 100 percent of principal, with the US Paycheck Protection Program (PPP) an outlier at the high end due to likely forgiveness of loans that didn’t default.
- Subsidy rates for partial guaranteed loans to SMEs and mid-caps ranged from 20 to 27 percent of principal.
- Programs targeting large firms had subsidy elements ranging from 10 to 12 percent of principal.
- Cross-country total subsidy costs (calculated by multiplying take-up for each scheme by its subsidy element and summing):
  - US total subsidy cost is close to 800 billion dollars.
  - Japan estimated cost is more than 150 billion USD.
  - UK had the smallest cost at about 10 billion USD.
  - When normalized by GDP, the US and Japan provided subsidies in excess of 3 percent of GDP.
  - Other countries’ subsidies ranged from 0.3 (UK) to 2.4 percent (Italy) of GDP.
- Lender subsidies:
  - For most programs, rules required the value of guarantees to be passed through to borrowers; borrower rates were chosen so lender subsidies were close to zero.
  - Significant lender subsidies arose in fully guaranteed programs with a mandated borrower rate.
  - Germany KfW Instant and UK BBLS had lender subsidy elements of 11 percent and 4 percent, respectively.
  - A few programs had a small negative lender subsidy, possibly due to lender costs being lower than assumed, additional unreported compensation, or political/reputational incentives despite small losses.
- Cross-program observations:
  - Fully guaranteed, “automatic” or “instant” programs featured limited lender screening, below-market borrower rates, and relatively generous compensation to lenders, contributing to higher subsidy rates.
  - Guarantee schemes targeting smaller firms show higher subsidy elements, reflecting more generous government programs for SMEs.
  - Cross-country differences at similar guarantee shares and firm sizes arise from variations in maturities, amortization rules, fees, and other concessions.
  - Spain had high take-up despite a relatively moderate subsidy element and a small amount of unused envelope.
  - Some countries (e.g., Germany, Italy) had unlimited legal capacity for certain guarantee schemes.

### Relationships among subsidy element, maturity, guarantee share, and take-up
- Subsidy element is positively correlated with both loan maturity and guarantee share, other things equal.
- The relationships are noisy due to other loan and program characteristics.
- No clear relationship across schemes between take-up (measured relative to GDP) and subsidy element:
  - Example: KfW Instant Loans had relatively low take-up and relatively high subsidy element.
  - Example: Spain’s ICO SME program had high take-up and moderate subsidy element.
- Possible explanations for weak correlation between subsidy element and take-up:
  - Small number of schemes may yield too few observations.
  - Other factors (policy signaling, availability of other fiscal support, program caps) influenced envelope sizing or demand.
  - Germany’s large announced envelope may have been intended to boost market confidence rather than reflect likely demand.
  - Availability of other supports (e.g., Kurzarbeit, which provides more than 70 percent of pay for hours not worked) could dampen take-up.
  - More generous programs often targeted small firms with lower borrowing caps (e.g., UK BBLS maximum GBP 50,000 vs. UK CBILS GBP 800,000), leading to high participation numbers but small total amounts relative to GDP.

### Sensitivity analysis: key parameter ranges and impacts
- Sensitivity exercises vary parameters that are imprecisely measured and materially affect results: the assumed fair market lending spread and assumed loan maturity.
- Fair-value lending spread sensitivity:
  - Base case spreads: 15 percent for SMEs, 10 percent for small and mid-sized firms, 6 percent for large firms (see Section 3.3.1).
  - Sensitivity ranges use credit spreads 2.5 percentage points higher and lower than the base case (e.g., SME programs recalculated with spreads of 12.5 percent and 17.5 percent).
  - Resulting change in subsidy element ranges from 4.5 to 7 percentage points across programs, with variation reflecting differences in loan maturity and guarantee share.
- Loan maturity sensitivity:
  - For nine programs with maximum maturity of 6 years: low-end assumes all loans mature in 3 years; high-end assumes all loans mature in 6 years.
  - For three programs with maximum maturity of 10 years: ranges vary (two use 4 years or 10 years; Japan Safety Net uses 6 years or 10 years).
  - UK BBLS: range taken as 5 years to 8 years (despite design at 6 years with option to extend to 10 years) to reflect possible prepayments and extensions.
  - Two Spanish programs: original maturity 5 years raised to 8 years later; sensitivity uses 4 years to 7 years.
  - Example impact: extending Spanish program maturity from 5 to 7 years increased the subsidy element by 5.4 percentage points.
- For SME programs with government-fixed lending rates, shifting the assumed fixed administrative cost by 1 percent lowers lender subsidy by 1 percent and raises borrower subsidy by 1 percent; total subsidy is unchanged.
- Sensitivity approach:
  - Reported sensitivities change one parameter at a time rather than simultaneously to avoid unrealistically wide uncertainty ranges.
  - Base case parameters were chosen to be unbiased; simultaneous worst-case shifts were deemed unlikely.

*Italic source: wpiea2023016-print-pdf - 23. By mid-April, it dropped to 8%. It fell to 6% by the first week of June and stayed in the 5-6% range*

### 6. Conclusion

### 6. Conclusion

### Main findings on upfront fair-value subsidy cost
- Total subsidies conferred by large-scale government credit support programs in seven advanced economies are estimated at USD 1.1 trillion (and USD 330 billion excluding the US PPP).
- Dividing by the principal value of loans extended, the average subsidy element is 34 percent (and 30 percent excluding the US PPP).
- The subsidy element varies widely across programs depending on program design choices, including:
  - riskiness of target borrowers,
  - size of rate concessions,
  - loan maturity,
  - fees,
  - other program features.

### Fiscal recognition and policy implications
- Credit support is often outside the normal budget process and its costs are only partially recognized, leaving policymakers without timely information when making design and scale choices.
- Without tools and regularized processes to evaluate the cost of credit support, such support can appear artificially inexpensive and may be overused.
- The analysis demonstrates the feasibility of producing credible upfront cost estimates and shows that unrecognized costs are often sizable.

### Longer-run fiscal consequences and risks
- At inception, total credit envelopes reached as much as 30 percent of GDP in some countries, posing potential adverse effects on long-term fiscal sustainability.
- Many loans remain outstanding, making it too soon to assess ultimate total losses.
- Unless there is a severe recession in the next few years, realized losses are likely to be manageable for most countries.
- If the economic impact had been more severe and take-up higher, honoring credit guarantees could have imposed a significant fiscal burden.
- Large credit losses are most likely to materialize during sustained economic downturns, when fiscal resources are already strained.
- The fair value approach to upfront fiscal cost implicitly accounts for higher likelihood of losses in bad times when fiscal resources are more valuable.

### Box: Debt moratorium for firms — key points
- Payment moratoriums or debt standstill policies provided considerable credit support in many countries and were identified by macroprudential authorities as the most important measures to protect NFCs and households, followed by public guarantees.
- In France, Germany, Italy, and Spain, moratoria allowed borrowers to postpone debt repayment for some time, easing short-term liquidity pressures; many moratoria were due to expire by December 2020, and more than half had expired by September 2020 in a sample of banks reporting to the EBA.
- Italy: under the Cura Italia decree (March 17, 2020) a debt moratorium allowed SMEs to skip payments until September 30, 2020; financial institutions adopting the moratorium received public guarantees totaling 33 percent of the relevant payment obligations.
  - Accepted moratorium requests: 1.249 million.
  - Covered loans worth 141 billion euros as of December 2021.
  - As of December 3, 2021, 36 billion euros for 225,000 companies remained under moratorium.
- Subsidy cost of moratoria or forbearance depends on structure:
  - If the government pays lenders in lieu of borrowers and does not expect full recovery, the program involves a subsidy.
  - Provision of loan guarantees on loans under moratoria provides a subsidy; the fair value of such subsidies can be estimated similarly to credit guarantees or direct loans.
  - Mandated and uncompensated creditor forbearance imposes an implicit tax on lenders rather than a direct government subsidy.

### Annex I — Selected program information (high-level program stats and design features)
- France (PGE):
  - Total credit envelope: EUR300 billion.
  - Take-up as of February 10, 2022: EUR143 billion.
  - Nearly 700,000 enterprises participated as of January 8, 2022.
  - Average loan size: €204,500.
  - For SMEs: interest rate 0 percent in the first year; thereafter rates could range between 1 to 2.5 percent.
  - Maximum loan maturity: 6 years.
  - Guarantee coverage tiered by firm size: 90 percent, 80 percent, 70 percent.
  - Maximum guaranteed loans for individual firms equivalent to 25% of annual turnover (with exceptions).
- Germany (KfW programs):
  - Actual treasury guarantee of KfW increased by €150 billion in March 2020.
  - For the three programs analyzed, about €42 billion committed as of August 2021, with 98 percent from SMEs.
  - Median loan amount below €100,000.
  - KfW Instant loans: 100 percent guarantee; take-up €8.1 billion as of August 2021; initial maximum loan up to 3 months of turnover, up to €300,000 for firms with <10 employees, €500,000 for 10–50 employees, €800,000 for others; in April 2021 maximum increased to €1.8 million for >50 employees, €1.125 million for 10–50, €675,000 for up to 10 employees; government’s mandated borrowing rate 3 percent; 10-year maturity; principal payments deferred up to 2 years.
  - KfW start-up and entrepreneurial loans (combined): 80–90 percent guarantees; maximum loan amount €100 million; total take-up €34.1 billion as of August 2021; deferral of principal repayment allowed up to 2 years.
- Italy:
  - Fondo Centrale di Garanzia initial envelope: €100 billion; also €220 billion for public guarantees for debt moratorium for SMEs.
  - Endowment and ceiling changes removed legal limit to guaranteed loans.
  - Fondo Centrale programs included in subsidy estimates targeted SMEs (<500 employees).
  - Guarantee coverage tiered: 100 percent for loans up to €30,000; 90 percent up to €5 million; 80 percent on restructured loans up to €5 million.
  - As of December 31, 2021, take-up in these programs was EUR151.1 billion.
  - Fully guaranteed loans maximum maturity: 10 years; 90% guarantee loans fixed maturity of 6 years.
  - SACE “Garanzia Italia”: SACE agreed to provide up to € 200 billion of loan guarantees.
    - Through “Garanzia Italia” in 2020: 1,401 guarantees issued for a total take-up of € 20.8 billion, with guaranteed principal of €17.8 billion, implying an average 86% guarantee.
    - Average guarantee amount per loan: about €14 million.
    - Assumed distribution of disbursements by firm size: 1/6 SMEs, 1/6 medium-sized firms, 4/6 large enterprises.
- Japan:
  - Emergency package and supplementary budgets expanded support from March–May 2020.
  - Safety Net No. 4: JFC would guarantee full loan amount (100 percent) for eligible SMEs.
  - Maximum loan amount of Safety Net Loan for an SME: 720 million Yen (approximately USD 6.7 million per firm).
  - Loan maturity: up to 15 years for capital expenditures or eight years for operating costs; no principal payment required for the first five years.
  - Announced envelope for credit guarantee programs: JPY 53 trillion.
  - Guarantee limit per loan: 280 million yen.
  - Total take-up: JPY 28 trillion as of the end of 2020.

*Source: wpiea2023016-print-pdf - 6. Conclusion*

### 5. Spain

### 5. Spain

### Overview: COVID-19 credit guarantee programs
- The Spanish government approved two credit guarantee programs with a combined envelope of €140 billion (€100 billion initially) to support self-employed workers and businesses affected by COVID-19.
- Programs: ICO Líneas Avales “Liquidez” COVID 19 (El Real Decreto-ley 8/2020 de marzo) and ICO Líneas Avales “Inversión y actividad” (El Real Decreto-ley 25/2020, de 3 de julio).
- Administration: Instituto de Crédito Oficial (ICO), Ministry of the Economic Affairs and Digital Transformation.
- As of December 31, 2021:
  - More than 1.1 million firms have benefited.
  - 98 percent of beneficiaries are self-employed and SMEs.
  - Combined take-up totalled €13.5 billion (as of December 31, 2021).

### 5.1 ICO Líneas Avales “Liquidez” COVID 19 (El Real Decreto-ley 8/2020 de marzo)
- Purpose: support businesses’ liquidity (working capital) needs.
- Guarantee shares by firm size:
  - SMEs: 80 percent guarantee.
  - Newly issued loans to large corporations: 70 percent guarantee.
- Guarantee maturity:
  - Initially set at five years and later extended to eight years.
- Eligibility: firms had to self-declare their working capital needs.
- Program participation and take-up (as of December 31, 2021):
  - 619,118 participants.
  - Total take-up of €121.9 billion.
  - 90 percent of participating companies are micro-enterprises and self-employed.

### 5.2 ICO Líneas Avales “Inversión y actividad” (El Real Decreto-ley 25/2020, de 3 de julio)
- Purpose: provide financing for current and capital expenses with new investments, expansion, adaptation or renewal of equipment, facilities and capacities.
- Loan conditions: same as for the liquidity program.
- Requirement: firms were required to demonstrate an investment need.
- Program participation and take-up (as of December 31, 2021):
  - 102,755 participants.
  - Take-up was €13.5 billion.
  - 90 percent of companies with formalized operations are micro-enterprises or self-employed individuals.

*Source: IMF Working Paper chapter "5. Spain" (source PDF: wpiea2023016-print-pdf - 5. Spain).*

### Annex II. Further considerations in applying fair

### Annex II. Further considerations in applying fair value principles

### A2.1 Distinction between fair value and market value
- In well-functioning financial markets, fair values are calculated with reference to observed market prices or interest rates; when directly comparable market data are unavailable, fair value permits interpolation and model-based approximations as a best estimate of what a price or discount rate would have been in a well-functioning financial market.
- Fair value allows adjustments to ensure cost estimates are not inflated by distress premiums that some argue should not be included in legitimate costs to governments.
- Examples of market-data limitations: government offers loans on terms not available in the private sector; market prices may be poor indicators of fundamental asset value during financial distress or when credit availability dries up.

### A2.2 Recap of the conceptual case
- Goal: provide an upfront estimate of program cost on an accrual basis; when present value of government cash inflows falls short of present value of outflows, the difference is the cost to the government and the subsidy to participants.
- For direct loans, cash inflows include interest payments, fees, and repayments of principal; outflows include principal loaned out plus associated administrative costs. For guarantees, inflows include fees and recoveries; outflows include reimbursed default losses net of collection costs and administrative expenses.
- Default losses are often the largest source of risk for loans and loan guarantees.
- Private-sector institutions absorb default losses first through equity, then debt holders; governments place taxpayers and other stakeholders in a first-loss position (higher future taxes or reduced future services).
- Public debtholders are unlikely to be affected by default losses to government credit programs in the developed countries considered, because public debt is protected by the government’s ability to raise taxes and a credible commitment to repayment.
- Using a safe-asset rate (government debt rate) to value a risky government loan or guarantee is inconsistent with economic valuation principles and understates the full economic cost of credit support.
- Two main conclusions:
  - Taxpayers and other government stakeholders function as equity holders in risky investments made by governments.
  - The interest rate on the public debt is not the correct rate to use for valuing risky government investments.

### A2.3 Resisting the gravitational pull of cash
- Most government budgets are recorded on a cash basis; equating economic cost with cash outlays can be misleading when accruals and cash accounting diverge.
- An accrual represents the economic or opportunity cost; a sum of outlays across years with different risks has no direct economic interpretation.
- Opponents argue budgetary costs should include cash outlays but not non-cash or opportunity costs; for government-produced credit assistance, default losses affect cash flows while the risk-premium is a non-cash cost, leading them to exclude risk premiums from cost estimates.
- Logical counterpoint: adopting accrual accounting already recognizes non-cash charges (time value) and that risky government investments impose non-cash costs for public risk-bearing; including such costs aligns with budgets as comprehensive records of the government’s draw on economic resources.
- When the government buys goods and services from competitive private suppliers, prices include risk charges; similarly, when the government purchases credit guarantees from private financial intermediaries, cash and fair value costs are generally equal.
- Excluding market risk prices from cost estimates can make it appear cheaper for governments to provide risk-bearing services than to purchase them, even when private providers may be more efficient.

### A2.4 All discount rates come from market prices
- The choice is not whether to use market prices but which market prices or rates to use as the reference.
- Government interest rates are determined by supply and demand in the capital market and therefore cannot be assumed to be free of market imperfections.
- Government bonds sell at high prices partly because of liquidity, collateral usefulness, and preferential tax treatment, and are dominated by large investors whose preferences may not represent government or population preferences.
- Government interest rates reflect the value to investors of characteristics of government debt and debt-market participants, not the characteristics of the risky loans financed through credit programs.

### A2.5 Non-risk factors in market rates
- Some argue that liquidity premiums and other non-risk factors in observed market rates should not be included in government costs, implying selective risk-adjustment of discount rates.
- Conceptual argument against selective adjustments: if competitive market prices are accepted as best indicators of economic value from a government opportunity cost perspective, adjustments are unnecessary and would reduce comprehensiveness.
- Practical argument against selective adjustments: no established procedures exist to isolate a pure market risk premium from observed rates; academic attempts to decompose market interest rates into pure time value, expected losses, risk premium, taxes, liquidity have produced widely varying attributions.
- A fair value standard imposes an established set of rules and discipline on analyst cost estimates that would be lost with ad hoc adjustments.

### A2.6 Implications of market incompleteness
- Market incompleteness implies private transactions at market prices need not yield efficient resource allocation nor reflect social value.
- Governments nonetheless rely on market prices to measure the cost of most non-credit activities; it is unclear how to adjust cost estimates for credit support to correct for incompleteness.
- Where incompleteness matters most is in valuing program benefits: government credit support can create value by making markets more complete and countering market frictions (e.g., limited information shutting borrowers out of markets where social benefits of access are high).
- Valuing the social benefits of pandemic-era credit assistance programs is outside the scope of this analysis.

### A2.7 Practical considerations
- Producing transparent and credible cost estimates for credit support faces practical challenges that vary across accounting regimes: limited resources, staff lacking relevant training.
- Accrual estimates are intrinsically more complicated than cash estimates, but complexity can be reduced via standardized procedures, basic staff training, and possible centralization or outsourcing of cost estimation.
- Although discounting at government rates may seem simpler because it avoids identifying different discount rates across programs, it systematically understates costs and can require forecasting the probability distribution of future cash flows, increasing complexity.
- A fair value approach can sometimes avoid the complication of detailed cash flow prediction and benefits from a robust accounting infrastructure that harmonizes practices, disseminates best practices, and provides audit and other services.

*Annex II. Further considerations in applying fair value principles*

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_Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023016-print-pdf.pdf_