## sdnea2022003 — Introduction and Key Findings

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### Executive summary and motivation
- Governments expanded roles as financiers of last resort during the COVID-19 crisis and the global financial crisis (GFC) using large-scale financial support measures (FSMs) such as credit guarantees, loan programs, and equity injections.
- Announced FSMs reached nearly $6 trillion globally, with the largest measures being guarantee programs in advanced economies.
- FSMs can be a large, rapid, and effective part of the policy response to extreme crises but bring elevated fiscal risks with long running implications for public finances.
- FSMs are most effective where governments have fiscal space or credibility to back them up and pre-existing FSM frameworks that allow rapid scaling up.
- FSMs increase the importance of transparent reporting because they can require complex institutional arrangements and are often undertaken outside traditional budget reporting.

### Scale, composition, and historical costs
- Cyclically adjusted primary deficit deteriorations:
  - Advanced economies: 3.9 percentage points during the GFC and 5.9 percentage points during COVID-19.
  - Emerging economies: 3.5 and 2.6 percentage points respectively.
- GFC interventions for the largest 37 economies:
  - $1.6 trillion of direct loan and equity interventions (4.5 percent of GDP) and $1.9 trillion of guarantees.
- Pandemic-era FSMs:
  - Announced FSMs reached nearly $6 trillion globally (6 percent of GDP).
  - In some advanced economies, announced FSMs reached as much as 30 percent of GDP.
- Examples of historical fiscal costs and balance-sheet effects:
  - US$700 billion TARP passed October 2008; maximum exposure reached about US$420 billion.
  - From 2008 to 2011, Irish financial sector support cost the Irish government 48.7 percent of GDP.
  - On average, realization of contingent liabilities due to the financial sector cost 10 percent of GDP.
  - Interventions during the GFC contributed to assets and liabilities increasing by 22 and 39 percent of GDP, respectively, and net worth falling by 17 percent of GDP.
  - During early pandemic stages, both assets and liabilities increased by more than 20 percent of GDP in 2020 alone.

### Macroeconomic effects and effectiveness of FSMs
- Crisis context and suitability:
  - FSMs are most appropriate where crises are expected to be temporary and measures are targeted to firms likely to remain solvent.
  - In normal recessions—when bankruptcies do not rise significantly—FSMs are less effective; standard fiscal and monetary policies should be relied upon.
- Empirical announcement effects during the pandemic:
  - FSM announcements can lead to larger improvements in contemporaneous and forward-looking economic indicators than traditional budget measures, conditional on governments having fiscal space.
  - Job ads increased by about 5 percent in the weeks following guarantee announcements.
  - Equity prices increased by 1–2 percent after guarantee scheme announcements.
  - Guarantee schemes were more effective in countries with fiscal cash or credibility; in countries with ample fiscal space contemporaneous indicators rose by more, whereas in countries with low fiscal space they appear to have a negative impact.
  - In advanced and emerging economies, FSM announcements tended to reduce sovereign spreads; in low-income countries sovereign spreads widened.
- Firm-level and model evidence on bankruptcies and output:
  - Corporate bankruptcies in 2020 in advanced economies declined by 14 percent, on average, from the previous year.
  - Existing estimates indicate that, absent policy measures, corporate bankruptcies during the pandemic would have increased by 5–20 percentage points.
  - In an exceptional recession that reduces output by 7.3 percent the first year, credit guarantees or loans can reduce the fall in output to 4.3 percent.
  - Model-simulated support program effects:
    - Support program reduces the bankruptcy rate by 3.6 percentage points (bringing it below its precrisis level).
    - Interest rate charged to firms is reduced by 4.3 percentage points in simulations.
    - FSMs reduce the depth of the recession by almost two-thirds where self-fulfilling pessimism is possible.
  - Two-way feedback: expectation of higher bankruptcies raises risk premiums, reducing credit supply and demand, which can generate a vicious cycle threatening macro and financial stability.

### Firm-level simulation (Portugal) — liquidity, solvency, zombification
- Portugal simulation (sample of 280,000 firms as of end-2019):
  - Without government support, share of firms in bankruptcy from illiquidity would have risen from 10 percent in 2019 to 36 percent in 2020.
  - With government support, the share fell to 9 percent in 2020.
- Policy inputs and assumptions in Portugal simulation:
  - Grants: 800 million euros for micro and small companies.
  - Wage subsidies: covering 70 percent of 2/3 of gross renumeration until a certain threshold.
  - Tax deferrals: VAT and corporate tax for firms with revenue below 10 million euros and activity closed or revenue fall above 20 percent.
  - Government credit lines with maximum loan principal amount not to exceed either twice the annual wage bill or 25 percent of total turnover in 2019.
  - Assumption: all available firms used the maximum amount allowed by each measure.
- Zombification risk and sectoral outcomes:
  - FSMs can lead to higher leverage and, if prolonged for insolvent firms, to zombification with resource misallocation and weaker productivity.
  - In contact-intensive sectors simulation: 5 percent of firms eventually go bankrupt because of the protracted crisis vs 0.8 percent for the other sector.
  - Evidence from Portugal: the share of zombie firms rises from 1 percent prior to the pandemic to 4 percent.

### Model calibration and scenario results (general equilibrium, typical advanced economy)
- Key calibration points:
  - Firms need financing to pay for about 60 percent of inputs.
  - Average total factor productivity calibrated to fall by 4 percent during the crisis.
  - Absent government support, crisis pushes 2.5 percent of firms into bankruptcy.
  - With three waves of shocks over 12 quarters (similar to the pandemic), absent government support:
    - Output falls by 7 percent in the first year.
    - Economy returns to pre-shock trend after five years.
    - Annual average loss of output is 6 percent during that period.
- Policy-comparison insights:
  - Guarantees reduce expected bankruptcies, lower risk premiums, and can prevent self-fulfilling waves of defaults.
  - Guarantees are less costly for the government than direct subsidized loans in simulations.

### Fiscal costs, accounting, and reporting considerations
- Accounting treatment and contingent liabilities:
  - Loans and equity injections usually increase assets and liabilities; net worth may be unchanged short-term but can fall if assets are written down.
  - Under GFSM 2014, a loan or asset write-off impacts fiscal balances when written off.
  - Discrete government guarantees usually have no immediate upfront fiscal impact unless expected cost is budgeted; they create contingent liabilities.
  - For standardized guarantee schemes, provisions for expected losses impact fiscal balances and debt when the guarantee is issued (or reassessed).
  - Good practice: record FSMs in budgetary documents and appropriations even when they do not initially impact revenues and expenditures.
- Measured subsidy and fair-value approaches:
  - Fair value / grant-equivalent approach computes net present value of cash flows to and from government over life of loan/guarantee.
  - An average of 40 percent of loan principals were subsidized by advanced economies’ governments during the pandemic (or 25 percent if excluding the US Paycheck Protection Program).
  - Subsidy element determinants: discount rate, maturity, interest rate, grace periods, share guaranteed, guarantee fees.
- Historical fiscal-impact comparisons:
  - If the government provides subsidies or subsidized loans instead of guarantees, the fiscal cost is 4.8 percentage points of GDP higher in the first year of the program.
  - The fiscal cost is 3.5 percentage points of GDP higher, on average, during the five years following the shock when using subsidies or subsidized loans instead of guarantees.
  - In model comparisons, a direct loan program with about 70 percent coverage would increase overall fiscal deficits by 5.0 percent the first year and by more than 7 percent in the second year while the impact of guarantees on the deficit is almost zero (under the model’s assumptions).

### Institutional arrangements, wind-down, and transparency
- Implementation channels and opacity:
  - Private lenders sometimes led liquidity channeling with guarantee mitigation (examples: Pakistan, Romania, Serbia, South Africa).
  - Special purpose vehicles (SPVs) created in some cases to acquire credit extended by lenders; SPVs often created by central banks but with losses backstopped by the government (examples: India, Korea, United Kingdom, United States).
  - In most cases, government bore the risk; the channel and size of this risk was sometimes opaque and uncertain.
- Wind-down timing and ongoing monitoring:
  - Some loan and guarantee schemes began winding down in early to mid-2021 (United Kingdom, United States); others continued into 2022 (France’s PGE).
  - Governments should monitor and manage FSMs with updated estimates of fiscal costs, regular updates of asset valuations and risk assessments, and forward-looking assessments (statistical models or scenario analysis).
  - Clear allocation of risk management responsibilities and development of analytical capacity in ministries of finance and implementing agencies is important.
  - Transparent reporting examples: UK detailed reporting on Bounce Back Loan Scheme losses and fraud; US Pandemic Response Accountability Committee under the CARES Act.
  - Publishing fiscal risk statements as part of or alongside budgets and following international accounting/reporting standards is recommended.

### Policy recommendations and checklist (design, approval, mitigation, exit)
- Overarching guidance:
  - Set a high bar for deploying FSMs given large and uncertain costs and fiscal implications.
  - Build fiscal buffers in normal times (bring debt down to prudent levels, invest in quality assets, reduce unnecessary fiscal risks).
  - Develop standing policymaking frameworks to ensure FSMs are well targeted, timely, cost effective, and keep fiscal risks within manageable levels.
  - Strengthen fiscal risk reporting, monitoring, and management; quantify and report fiscal costs and risks for as long as measures remain in place.
- Design and approval checklist (selected items):
  - Decide whether government intervention is needed and whether other macroeconomic levers can provide needed support.
  - Choose the most cost-effective instrument and consider alternatives.
  - Assess targeting, administrative capacity, and implementation feasibility.
  - Estimate costs and maximum exposure under worst-case scenarios; assess affordability without breaching fiscal credibility or rules.
  - Adopt risk mitigation measures and set a clear exit strategy and communication plan; designate responsibilities for administration, monitoring, and reporting.
- Design features and risk mitigation:
  - Key decision criteria: nature of shock, relative merits of FSMs (guarantees vs debt vs equity), speed, duration, and risk exposure.
  - Risk mitigation tools: timing, coverage limits, eligibility criteria, maturity limits, loan size caps, partial guarantees, collateral requirements, risk-based guarantee charges, prohibiting risky borrowers.
  - Exit strategy: structured phasing out, swift restructuring provisions for nonviable firms, and clearly communicated exit criteria based on publicly verifiable milestones.

### Annex and model technical highlights
- Annex 1 (Portugal) methodological notes:
  - Cash flows follow a Brownian motion with drift (true mean 휇̅, volatility 휎).
  - Pre-, during-, and post-COVID parameter estimation and policy inputs specified; simulations assume maximum take-up per firm.
- Annex 2 (general equilibrium model) calibration and assumptions:
  - Average total productivity during the exceptional crisis is 4 percent lower than during standard times.
  - Autocorrelation of μ and its standard deviation are 0.7 and 0.015, respectively.
  - Support for idiosyncratic productivity shock: [0.895, 1.105].
  - Fixed costs are about 6 percent of average firms’ output.
  - Firms need financing for 0.581 of their inputs; κ = 0.7; share of labor in GDP (α_l) corresponds to 0.66.
  - Calibration produces high private debt close to 200 percent of GDP; AEs’ average private debt about 174 percent of GDP (period 2000–2019), EMs 60 percent, LICs 16 percent.
- Main model insights:
  - Guarantees during heightened uncertainty have important effects on output, investment, and bankruptcy rates similar to direct loans when guarantees are credible and properly administered.
  - Guarantees are less costly to the government than direct loans under the model’s assumptions.

*Source: sdnea2022003 — STAFF DISCUSSION NOTE: THE STATE AS FINANCIER OF LAST RESORT (IMF).*

### Introduction------------------------------------------------------------------------------------------------------------

### Introduction

### Executive Summary
- Governments expanded their roles as financiers of last resort during the COVID-19 crisis and the global financial crisis (GFC), using large-scale financial support measures (FSMs) such as credit guarantees, loan programs, and equity injections.
- This Staff Discussion Note analyzes the macroeconomic benefits and fiscal costs of FSMs and sketches a framework for managing their legacy and improving design for future crises.
- Announced FSMs reached nearly $6 trillion globally, with the largest measures being guarantee programs in advanced economies.
- FSMs can be a large, rapid, and effective part of the policy response to extreme crises but bring elevated fiscal risks with long running implications for public finances.
- FSMs are most effective where governments have fiscal space or credibility to back them up and pre-existing FSM frameworks that allow rapid scaling up.
- FSMs increase the importance of transparent reporting because they can require complex institutional arrangements and are often undertaken outside traditional budget reporting.

### Introduction and motivation
- In ordinary recessions, fiscal stabilization often relies on automatic stabilizers or discretionary tax/expenditure measures; in major crises, policymakers expanded fiscal interventions to support people and firms.
- A prominent avenue—primarily aimed at firms—was large-scale use of FSMs (credit guarantees, loan programs, equity injections), raising novel policy issues around macroeconomic implications and modalities to manage sizable fiscal risks outside the standard budgetary process.
- The Note is complementary to IMF studies on fiscal policy during the pandemic, traditional fiscal measures, and monetary and financial sector policy responses.

### Scale and composition of fiscal interventions
- Traditional “above the line” fiscal measures were widely used in both crises; FSMs are often “below the line” (affecting assets/liabilities or contingent liabilities rather than revenues/expenditures initially).
- Specific numeric magnitudes reported:
  - The cyclically adjusted primary deficit in advanced economies deteriorated by 3.9 percentage points during the GFC and by 5.9 percentage points during COVID-19.
  - For emerging economies the deteriorations were 3.5 and 2.6 percentage points respectively.
  - During the GFC, public sector interventions for the largest 37 economies included $1.6 trillion of direct loan and equity interventions (4.5 percent of GDP) and $1.9 trillion of guarantees.
  - During the COVID-19 pandemic, announced FSMs reached nearly $6 trillion globally (6 percent of GDP).
  - In some advanced economies, the announced size of FSMs reached as much as 30 percent of GDP.
- FSMs were among the earliest and largest policy responses, often rolled out before traditional budget measures, supporting firms while households were cushioned through wage support and cash transfers.

### Macroeconomic effects and effectiveness of FSMs
- FSMs can reduce bankruptcies significantly by increasing firms’ liquidity, reducing risk premiums, and boosting confidence; evidence comes from firm-level microsimulations and a general equilibrium model.
- FSMs increase corporate debt, and many firms permanently affected by the crisis are still likely to go bankrupt.
- FSMs are most appropriate where crises are expected to be temporary and where measures are targeted to firms likely to remain solvent.
- In normal recessions—when bankruptcies do not rise significantly—FSMs are less effective and standard fiscal and monetary policies should be relied upon.
- An empirical analysis of announcement effects during the pandemic finds that FSM announcements can lead to larger improvements in contemporaneous and forward-looking economic indicators than traditional budget measures, conditional on governments having fiscal space.
- FSMs reduce the risk of a vicious cycle between bankruptcies, high risk premia, pessimism of households and firms, and contraction of activity, as shown in a general equilibrium model.

### Fiscal risks, transparency, and institutional arrangements
- FSMs shift risks from private to public sector balance sheets with long-lasting public finance implications.
- Many FSMs are undertaken outside traditional budget and fiscal reporting, reducing transparency and raising governance issues.
- The bar for intervention should be set high; fiscal costs and risks of FSMs should be quantified and reported for as long as they remain in place.
- Developing policy frameworks in advance will:
  - Allow rapid, well-targeted rollout in future crises.
  - Help determine which measures to use in different circumstances.
  - Assist in setting risk mitigation measures at the design stage when governments can best control fiscal risks.

### Scope of Note (chapters summarized)
- Chapter 2 documents the scale of FSMs during the GFC and the pandemic, noting FSMs were largest in exceptional crises and in advanced economies, used less in emerging economies, and largely absent in low-income countries.
- Chapter 3 explores benefits and fiscal costs of FSMs, using firm-level microsimulations (Portugal example) and macroeconomic modeling to analyze liquidity, solvency, and confidence channels; empirical analysis of FSM announcements supports simulation findings.

*Source: sdnea2022003 - Introduction (IMF Staff Discussion Note).*

### Chapter 4 presents recommendations for governments to consider when managing  the legacies of  the

### sdnea2022003 - Chapter 4 presents recommendations for governments to consider when managing  the legacies of  the

### Fiscal policy response during crises
- Governments implement larger FSMs during exceptional crises; available data suggest an increase in government interventions during deep downturns.
- During the GFC:
  - Some countries increased their loans and equity purchases by more than 4 percent of GDP.
  - Contingent liabilities increased above average, sometimes by more than 10 percent of GDP.
- During the pandemic:
  - Governments scaled up FSMs swiftly in March–April 2020.
  - In several advanced economies, the face value of guarantees made available exceeded 30 percent of GDP.
  - Equity injections were smaller and targeted (example cited: France’s loan of €3 billion to Air France/KLM).
  - FSMs were more promptly deployed where pre-existing FSM frameworks allowed rapid scaling up (example: Germany, United Kingdom).
- The energy price shock in 2022 led to additional FSMs in Europe, with some announced measures worth almost 5 percent of GDP (as of end of September 2022) in the form of guarantees, credit lines, and equity injections.
- Large FSM envelopes are often justified to boost confidence; take-up by the private sector often ended up lower than envelopes made available:
  - Example: US$700 billion TARP passed in October 2008; maximum exposure reached about US$420 billion.
- Historical costs and balance sheet effects:
  - From 2008 to 2011, financial sector support cost the Irish government 48.7 percent of GDP.
  - On average, realization of contingent liabilities due to the financial sector cost 10 percent of GDP, about four times more than support to the nonfinancial sector.
  - Interventions during the GFC contributed to public sector balance sheet changes: assets and liabilities increasing by 22 and 39 percent of GDP, respectively, and net worth falling by 17 percent of GDP.
  - During the early stages of the pandemic, both assets and liabilities increased by more than 20 percent of GDP in 2020 alone.
- Institutional arrangements:
  - Private lenders sometimes led channeling liquidity support, with credit risk mitigated by guarantee schemes (examples: Pakistan, Romania, Serbia, South Africa).
  - Special purpose vehicles (SPVs) were created in some cases to acquire credit extended by lenders; SPVs often created by central banks but with losses backstopped by the government (examples: India, Korea, United Kingdom, United States).
  - In most cases, the government bore the risk; the channel and size of this risk was sometimes opaque and uncertain.

### Effectiveness of financial support measures (FSMs)
- FSMs during the pandemic prevented bankruptcies and helped preserve productive capacity:
  - Corporate bankruptcies in 2020 in advanced economies declined by 14 percent, on average, from the previous year.
  - Existing estimates indicate that, absent policy measures, corporate bankruptcies during the pandemic would have increased by 5–20 percentage points, owing in equal part to liquidity shortfalls and insolvencies.
- Take-up dynamics and determinants:
  - Take-up expanded quickly but ended up much lower than commitments in many cases (Figures referenced: 6 and 7).
  - Factors affecting take-up include size of shock, monetary support, program terms attractiveness, and bottlenecks in financial intermediaries.
  - Country examples: Germany (low take-up due to less stringent lockdowns, grants, Kurzarbeit, pricing of loans and dividend/managerial remuneration restrictions); Spain and Italy (higher take-up due to greater liquidity needs and broader accessibility).
- Legacy and wind-down:
  - Some loan and guarantee schemes began winding down in early to mid-2021 (United Kingdom, United States) while others continued into 2022 (France’s prêts garantis par l'État – PGE).

### Liquidity and solvency effects of financial support measures
- Firm-level simulation for Portugal (sample of 280,000 firms as of end-2019):
  - Three scenarios compared: Pre-COVID; COVID with no fiscal measures; COVID with the measures introduced by the government in 2020.
  - In absence of government support programs, the share of firms in bankruptcy from illiquidity would have risen from 10 percent in 2019 to 36 percent in 2020.
  - With government support, the share actually fell to 9 percent in 2020.
  - Measures such as tax deferrals and wage subsidies lowered operating costs directly; debt moratoria and credit guarantee programs relaxed financial constraints.
  - Surplus liquidity (difference between cash and deposits and minimum to avoid bankruptcy) increased under support measures.
- Leverage and zombification risks:
  - FSMs can lead to higher leverage as extra borrowing (often guaranteed) builds cash buffers but adds to debt.
  - Prolonged support to otherwise insolvent firms ("zombification") may lead to resource misallocation, lower investment, and weaker productivity growth.
  - In simulations for contact-intensive sectors:
    - 5 percent of firms in the contact-intensive sector eventually go bankrupt because of the protracted crisis in this sector, compared to 0.8 percent for the other sector.
  - Evidence from Portugal: the share of zombie firms rises from 1 percent prior to the pandemic to 4 percent (IMF (2022b) cited in text).

### Macroeconomic channels and effects
- Two-way feedback between bankruptcies and credit markets can create a vicious cycle:
  - Expectation of higher bankruptcies raises risk premiums, reducing credit supply.
  - Anticipated lower demand reduces firms' demand for inputs and credit.
  - The interaction can threaten macroeconomic and financial stability and lead to a deflationary spiral; volatility increases because shocks are not fully understood.
- Macroeconomic model calibration and results (typical advanced economy; Annex 2):
  - Firms need financing to pay for about 60 percent of inputs.
  - Average total factor productivity is calibrated to fall by 4 percent during the crisis.
  - Absent government support, the crisis pushes 2.5 percent of firms into bankruptcy (magnitude similar to increase observed during the GFC in the United States).
  - With three waves of shocks over a period of 12 quarters (similar to the pandemic), absent government support:
    - Output falls by 7 percent in the first year of the shock.
    - The economy returns to pre-shock trend after five years.
    - Annual average loss of output is 6 percent during that period.
- Role of FSMs in the model:
  - FSMs reduce expected bankruptcies, lower risk premiums, and can prevent a self-fulfilling wave of defaults by improving prospective viability for firms, including those not directly receiving support.

### Policy implications and recommendations (as presented)
- Strengthen fiscal risk reporting, monitoring, and management because many FSMs are undertaken outside traditional budget and fiscal reporting; objective is greater transparency on potential costs and benefits and to avoid surprises that impinge on fiscal space.
- Set up policy frameworks for designing FSMs and deciding on their use in advance of future crises.
- Build requisite fiscal buffers in normal times and ensure appropriate frameworks are in place before being called on to intervene on a large scale.
- Use FSMs in crises likely to be temporary and target measures to firms likely to remain solvent to reduce zombification risk; however, when support to zombie firms represents a small share of total support, delaying programs to improve targeting may not be ideal.

*Source: STAFF DISCUSSION NOTES THE STATE AS FINANCIER OF LAST RESORT, INTERNATIONAL MONETARY FUND (Chapter 4 excerpt).*

### 1. Effect on level of Macroeconomic Variables

### 1. Effect on level of Macroeconomic Variables

### Effect on levels of output, bankruptcies, employment, and investment
- In an exceptional recession that reduces output by 7.3 percent the first year, credit guarantees, or loans can reduce the fall in output to 4.3 percent.
- The bankruptcy rate increases from about 1 percent to 3.5 percent in the absence of support.
- A support program can reduce the bankruptcy rate to 0.15 percentage points below precrisis.
- Five years after the start of the crisis, firms’ capital remains below precrisis levels in the absence of government guarantees.
- FSMs reduce the depth of the recession by almost two-thirds where self-fulfilling pessimism is possible.
- By lowering the bankruptcy rate and reducing firms’ borrowing costs, FSMs reduce incentives to downsize production, supporting demand for productive inputs and lessening negative effects on employment and investment.
- Stronger workers’ and capital owners’ incomes allow households to spend more, improving outlooks and further supporting firms’ viability, investment, and consumer spending.

### Effect on volatility
- Volatility is reduced by 40 percent in the exceptional recession scenario with FSMs; during standard contractions, the effect of these programs on volatility (of output, employment) is small or even marginally positive.
- Automatic stabilizers can reduce macroeconomic volatility by 20–50 percent.

### Model-simulated macro-financial impacts of FSMs
- The support program reduces the bankruptcy rate by 3.6 percentage points (bringing it below its precrisis level) in model simulations.
- The interest rate charged to firms is reduced by 4.3 percentage points in the model simulations.
- FSMs break the vicious cycle between borrowing costs and pessimistic expectations, reducing bankruptcy rates and risk premiums.
- Guarantees, loans, or transfers can all be effective, but guarantees entail lower fiscal costs in simulation comparisons.

### Fiscal costs and instrument comparisons
- If the government provides subsidies or subsidized loans instead of guarantees, the fiscal cost is 4.8 percent points of GDP higher in the first year of the program.
- The fiscal cost is 3.5 percentage points of GDP higher, on average, during the five years following the shock when using subsidies or subsidized loans instead of guarantees.
- Guarantees only have explicit fiscal costs when they fail to prevent a significant rise in bankruptcies; guarantees that involve sizable fiscal risks may lead to potentially unexpected costs if issued liberally and conditions deteriorate more than expected.
- Loan programs and equity injections expand the public sector’s balance sheet by increasing both assets and liabilities; guarantees may have no immediate impact but increase contingent fiscal risk if called or loans are written down.
- It is important to record increased risk exposure and to ensure proper reporting and accounting for FSMs to manage fiscal implications and avoid distortion of policymaking.

### Empirical estimates and announcement effects during the pandemic
- Empirical analysis of FSM announcements during the pandemic finds guarantees and loan schemes boosted contemporaneous and forward-looking macroeconomic indicators.
- The announcement of guarantee programs had the most significant impact on short-term activity; job ads increased by about 5 percent in the weeks following announcements.
- Equity prices increased by 1–2 percent after guarantee scheme announcements.
- Announcement effects: above the line measures had little-to-no short-term effects in the constrained lockdown environment; below the line measures and credit guarantees produced stronger effects.
- Guarantee schemes were more effective in countries with either cash or credibility in terms of macroeconomic impact and effect on sovereign interest rates; in countries with ample fiscal space, contemporaneous indicators rose by more, whereas in countries with low fiscal space they appear to have a negative impact.
- In advanced and emerging economies, announcement of FSMs tended to reduce sovereign spreads; in low-income countries sovereign spreads widened, perhaps reflecting investor concerns about ability to manage greater fiscal risks.

### Mechanisms, risks, and contextual considerations
- FSMs improve economywide confidence and financing conditions and help limit banking losses, improving broader financial stability.
- Guarantees can prevent chain reactions of non-payments, loss of demand, and lack of inputs, and may be more helpful than generalized interest rate cuts in restoring credit flow to firms hit by the shock.
- During standard recessions, FSMs are less effective because uncertainty is less pronounced and firms’ buffers may be sufficient to prevent a rise in bankruptcies; risk premiums remain stable and FSMs are less appropriate.
- The ultimate fiscal impact of FSMs depends on implementation modalities (direct government, central banks, state-owned enterprises) and on adequate monitoring, analysis, and reporting frameworks.

*Source: IMF staff calculations.*

### Box 1. Accounting  for Credit Support Measures  and Quasi-Fiscal  Activities

### Box 1. Accounting for Credit Support Measures and Quasi-Fiscal Activities

### Accounting treatment of FSMs (financial support measures)
- Loans and equity injections are funded through increased borrowing or asset drawdowns; if the government receives an asset of similar value in return (the loan or equity), government net worth is typically unchanged in the short-term.
- Over the longer term, new assets can expose the government to risk of losses (for example, loan default or asset write off reduces asset value and net worth).
- Under GFSM 2014 principles, a loan or asset write-off impacts fiscal balances at the time it is written off.
- Good practice: record FSMs in budgetary documents and appropriations even when they do not initially impact revenues and expenditures.

### Contingent liabilities and guarantees
- Discrete government guarantees usually have no immediate upfront fiscal impact unless the expected cost is budgeted; they create contingent liabilities and expose the government to future calls on guarantees.
- A call on a guarantee increases gross public debt as the guaranteed debt is assumed by the government.
- Under GFSM 2014, expected losses on standardized guaranteed schemes are treated differently to individual guarantees: provisions for expected losses on the guaranteed portfolio impact both fiscal balances and debt at the time the standardized guarantee is issued (or in subsequent reassessments), as an estimated average of the annual portfolio loss.
- If loans are provided that are not expected to be repaid, or guarantees are highly likely to be called, good practice requires recording these interventions as expenditure at the time the loan or guarantee is issued, immediately reducing the fiscal balance and net worth.

### Quasi-fiscal activities
- Credit support measures implemented by public corporations on behalf of government are often referred to as quasi-fiscal activities (examples: loan and guarantee programs managed by central banks, government-directed expansion of credit at public banks, deferral of billing at state-owned utilities).
- If it is clear the government instructed the public corporation to carry out interventions for public policy purposes, budgeting and reporting should reflect the substance, with interventions considered as undertaken by government and recorded on the general government balance sheet.
- Otherwise, transactions affect the balance sheet of the public corporation similar to below-the-line and contingent liability measures for general government; from a public-sector balance sheet perspective, the effect is the same.
- FSMs result in additional risks assumed across the public sector; monitoring and managing implications is crucial for reducing uncertainty and ensuring fiscal sustainability.

### Strengthening policymaking frameworks and approval processes
- FSMs warrant similar scrutiny and budget procedures as traditional fiscal support measures to avoid policy distortion and undue favoring of FSMs.
- Ideally, all FSMs should be assessed, costed, and included in budget plans using similar procedures as traditional measures; during crises, streamlined ex ante processes and high-level cost estimation may be necessary, increasing the importance of transparent ex post disclosure and scrutiny.
- Quantifying fiscal costs and risks at the time of decision helps clarify tradeoffs even under heightened uncertainty; present scenarios and maximum exposure assessments to indicate worst-case costs.
- For public loans and guarantees, credit risk evaluation techniques used in financial analysis can be applied.
- Empirical illustrations and references in the text:
  - Subsidy element estimated to be about 40 percent on average for the largest guarantee programs in advanced economies.
  - Lucas (2016) estimating that in 2010, the multiplier of US federal credit programs was about 5.
  - The United States Federal Credit Reform Act of 1990 requires inclusion of the present value of expected costs of credit schemes in agency budgets in the year the intervention is made.
- Use of the IMF Fiscal Risk Toolkit is recommended for estimating expected costs and conducting scenario analysis.

### Design considerations for interventions
- Key decision criteria:
  - Nature of shock: FSMs are most appropriate for temporary shocks where private actors cannot absorb large shocks and where liquidity/credit markets may freeze.
  - Relative merits of alternative FSMs: guarantees and debt measures are more effective for liquidity challenges; guarantees when financial sector can lend, debt when direct state intervention is necessary; equity injections for systemically important firms facing solvency challenges.
  - Shape of the measure: speed, duration, and risk exposure should be timely, targeted, and temporary; trade-offs exist between targeting, administrative burden, and speed.
  - Institutional considerations: interventions should reflect institutional strengths; implementing agencies need to provide guidance on terms, eligibility, and manage implementation; legal frameworks and coordination mechanisms may be required.
- Exit strategy: structured phasing out, swift restructuring provisions for nonviable firms, and clearly communicated exit criteria based on publicly verifiable milestones.

### Risk mitigation and management
- Risk mitigation should be considered at the design stage when government control is greatest.
- For loan and guarantee schemes, limit exposure through timing, coverage, eligibility, maturity limits, loan size caps, prohibiting risky borrowers, partial guarantees, collateral requirements, and risk-based guarantee charges.
- Governments should assess trade-offs between mitigating and bearing risks early, based on cost/benefit assessments and capacity to accommodate residual risk.

### Monitoring, reporting, and managing legacy costs
- FSMs expand government balance sheets and can have long-term implications that may take years to unwind; following the GFC, government balance sheets remained considerably larger.
- Examples of wind-down timing cited:
  - Some guarantee and loan schemes were wound up in early to mid-2021 (United Kingdom, United States).
  - Other schemes were continued and eventually wound up in 2022 (France’s PGE).
  - Some continued with tightened conditions (Italy).
- Governments should monitor and manage FSMs on an ongoing basis with updated estimates of fiscal costs as events unfold; regular updates of asset valuations and risk assessments support decisions on budget provisions or buffers.
- Forward-looking assessments (statistical models or scenario analysis) help gauge risk evolution and inform fiscal forecasts.
- Clarity over risk management responsibilities, development of new analytical capacities in ministries of finance, and information channels with implementing agencies are important.
- Transparent reporting on fiscal risks and their realization enhances confidence and fiscal credibility (examples cited: UK detailed reporting on Bounce Back Loan Scheme losses and fraud; US Pandemic Response Accountability Committee under the CARES Act).
- Publishing fiscal risk statements as part of or alongside budgets and following international accounting/reporting standards helps include these measures in government balance sheets and fiscal documents; public sector financial reporting assists tracking interventions delivered outside general government.

### Box 2 — Concise checklist for policy approval of crisis interventions (selected items)
- Deciding whether to intervene:
  - Is there a need for government intervention?
  - Can other macroeconomic policy levers provide the needed support?
  - What form of policy instrument is likely to be most effective?
- Deciding on specific interventions:
  - Is this measure the most cost-effective way of providing needed support?
  - Have alternative options been considered?
  - Is the measure targeted appropriately and what are administrative implications?
  - Do we have capacity to administer and implement these measures?
- Determining whether costs can be accommodated:
  - What are costs of the measure? How will this impact the deficit, financing requirements and debt?
  - What are the maximum costs under the worst-case scenario? What are the likely costs?
  - Can the costs be accommodated without undermining fiscal credibility or breaching fiscal rules?
- Risk mitigation and management:
  - Have risks been identified and appropriate mitigation measures adopted?
  - Is there a clear exit strategy and communication plan?
  - Who is responsible for administering, monitoring, and reporting on the scheme?

*Source: Box 1. Accounting for Credit Support Measures and Quasi-Fiscal Activities (sdnea2022003).*

### Conclusion

### Conclusion

### Summary of interventions and macroeconomic role of FSMs
- During the pandemic and GFC, governments acted as financiers of last resort to stabilize economies, transferring risk from the private sector to the public.
- Financial support measures (FSMs), which go beyond traditional budgetary support, were especially large and effective in reducing bankruptcies and attenuating the depth of the recession.
- FSMs preserve productive capacity by improving firms’ liquidity and solvency, and more generally boosting confidence and macroeconomic performance.
- FSMs supported firms’ liquidity and led bankruptcy rates to fall in many countries during the pandemic; they also facilitated a decline in risk premiums, a loosening of financing conditions, and an improvement in the long-term viability of firms.
- Both real-time and forward-looking measures of economic activity improved after FSMs were announced, particularly where governments had fiscal space.

### Risks, limitations, and appropriate bar for intervention
- FSMs are less transparent, their costs are large and uncertain, and their benefits are not as well established as traditional measures.
- FSMs increase fiscal risks, expanding public sector balance sheets and creating contingent liabilities that can impact public finances for years.
- FSMs can distort private sector decision-making over the longer term and can entrench expectations of bailouts, leading to riskier behavior.
- FSMs are at their most effective during crises, when they can short circuit a doom loop of low confidence and tight financing, and thus preserve productive capacity.
- FSMs are less effective during standard economic slowdowns, when traditional fiscal and monetary actions remain the first port of call for policymakers.
- Support in the current juncture of high energy prices should be limited to systemic companies required for macroeconomic stability and tailored to the duration of the shock.

### Policy recommendations
- Governments should set a high bar for deploying FSMs given their large and uncertain costs and fiscal implications.
- During normal times, governments should seek to build fiscal space so FSMs can be deployed effectively in crises.
  - Bringing debt down to prudent levels, investing in quality assets, and reducing unnecessary fiscal risks when conditions allow increases scope to act when needed.
  - Regular fiscal stress tests, combined with probabilistic forecasting methods, help integrate fiscal risks in fiscal frameworks (IMF 2016).
- Governments should build institutional capacity to design, deploy, and manage FSMs so they are ready to be rolled out quickly and effectively.
  - Develop a standing policymaking framework to ensure FSMs are well targeted, timely, cost effective, and keep fiscal risks within manageable levels.
  - Where FSMs have been deployed, ongoing monitoring mechanisms are needed to assess the value of interventions and provide early warning should fiscal risks crystallize.

### Box 3 — Fair Value Approach: measuring true costs of FSMs
- The fair value approach produces cost estimates for credit support programs that are “grant-equivalent,” computing the net present value of cash flows on an accrual basis to and from the government over the life of the loan or guarantees, as of a specified point in time.
- Cash flows are discounted at rates that include risk premiums to account for the market value of risky cash flows.
- The subsidy element reports the lifetime cost of the programs and is measured as the difference between actual disbursement amount and the present value of all expected future cash flows associated with the program, discounted at market rates.
- An average of 40 percent of loan principals were subsidized by advanced economies’ governments during the pandemic (or 25 percent if excluding the US Paycheck Protection Program that is largely subsidized).
  - The analysis covers the seven countries that made the most extensive use of credit guarantee programs during the pandemic (Hong and Lucas, forthcoming).
- Programs with a longer maturity or higher guarantee rates tend to have larger subsidy components, whereas higher fees or interest rates reduce it.
- As guarantees were often more generous for small enterprises, the subsidy components and associated fiscal risks are higher in those programs.

### Annex 1 — Corporate support programs during COVID-19 in Portugal: model and simulation
- Model highlights interconnectedness between illiquidity and insolvency when cash flows are uncertain; cash flows follow a Brownian motion with drift (true mean 휇̅, volatility 휎).
- Firms can default due to:
  - liquidity default if cash reserves fall below a threshold (threshold higher for higher-value firms), or
  - insolvency if the updated expectation of the firm’s value falls below debt obligations.
- Simulations compare scenarios with and without government support, using firm-level corporate balance sheets from ORBIS (data up to 2018).
- Simulations are conducted for three periods: (1) pre COVID-19, (2) during the COVID-19 pandemic, and (3) post COVID-19.
- Key assumptions and policy inputs (Portugal):
  - Grants: 800 million euros for micro and small companies.
  - Wage subsidies: covering 70 percent of 2/3 of gross renumeration until a certain threshold.
  - Tax deferrals: VAT and corporate tax for firms with revenue below 10 million euros and whose activity has been closed or had a revenue fall above 20 percent.
  - Loan and debt deferrals.
  - Government credit lines available to affected sectors (tourism and restaurant) with the maximum loan principal amount not to exceed either 1) twice the annual wage bill or 2) 25 percent of the total turnover in 2019.
  - Assumption in simulations: all available firms used the maximum amount allowed by each measure.
- Pre-, during-, and post-COVID parameters (approach):
  - Pre-COVID firm-specific 휇′s and 휎′s estimated by maximum likelihood on EBIT.
  - COVID-period parameters inferred from realized output for each sector in March 2020 using industrial production series.
  - Post-COVID recovery paths calibrated with a half-life calculation to recover duration to reach pre-COVID output levels.

### Annex 2 — General equilibrium model of government support to the corporate sector
- Open-economy model: households consume tradable good c, supply labor l for wage w, hold financial assets b with return r*, and physical capital k rented at rate r_k; government collects taxes, buys goods and services, gives transfers T, and borrows internationally.
- Firms produce tradable good y using labor, capital, and intermediate goods m with productivity A subject to aggregate shock Z and firm-specific shock φ_i: y_i = φ_i Z A k^αk l^αl m^(1−αk−αl).
- Aggregate productivity shock Z = μ z. μ follows AR(1): μ = μ̅ + ρ_z μ_{t−1} + ε, ε ~ N(0, σ_ε).
  - Two regimes: μ = μ_H = 1 in normal times, μ = μ_L < 1 during disaster periods.
  - Transition governed by Markovian stochastic process with initial matrix א_0(μ′|μ); disastrous shock is unprecedented and initially assigned almost 0 probability, but μ_L is realized in d=0.
- Agents update transition probabilities p^{א} based on observed transitions with frequencies n_{ij}.
- Firms borrow J_F from banks to finance fraction κ of production costs; may also borrow J_G from government at rate r_{G,F}. Fixed costs F must be covered; firms fail when idiosyncratic productivity φ_i ≤ φ*.
- Government guarantees (fraction η of loans) reduce banks’ required lending rate: 1 + r_F = (1 + r*)/(1 − ω (1 − η)), where ω is bankruptcy probability.
- Calibration highlights and exact numeric assumptions:
  - Average total productivity during the exceptional crisis is 4 percent lower than during standard times.
  - Autocorrelation of μ and its standard deviation (same across regimes) are 0.7 and 0.015, respectively.
  - Support for the idiosyncratic productivity shock: [0.895, 1.105].
  - Fix costs are about 6 percent of average firms’ output.
  - Intermediate goods as share of gross output about 50 percent.
  - Calibration assumes three waves of the shock observed over 10 quarters, with the first wave ending after 3 quarters.
  - Firms assumed to need financing for 0.581 of their inputs.
  - Value of κ is 0.7; share of labor in GDP (α_l) corresponds to 0.66.
  - Priors at time 0 for p^{0א}_{HH} and p^{0א}_{LL} are 0.97 and 0.5 respectively.
  - Calibration produces a high level of private debt as share of GDP (close to 200 percent).
  - AEs’ average private debt is about 174 percent of GDP (period 2000–2019), while averages for EMs and LICs are 60 percent and 16 percent, respectively.
- Main model insights and results:
  - Guarantees during periods of heightened uncertainty have important effects on macroeconomic aggregates and have effects on output, investment, and bankruptcy rates that are similar to the effect of direct loans (under the assumption that guarantees are fully credible and properly administered).
  - Guarantees are less costly for the government than direct loans; time series of excess overall fiscal deficits differ between guarantees and loans with guarantees imposing lower fiscal costs.

*Source: IMF staff.*

### Annex Figure 2.2. Loans  vs Credit Guarantees: Macroeconomic Impact and Fiscal Deficit

### Annex Figure 2.2. Loans  vs Credit Guarantees: Macroeconomic Impact and Fiscal Deficit

### Effect on Output, Investment and Bankruptcy Rate during an Exceptional Recession
- In an exceptional recession corporate support in the form of guarantees is as impactful as support in the form of direct subsidized loans (panel 1).  
- During exceptional contractions characterized by heightened uncertainty, high levels of guarantees or direct loans can be more effective and overall, less costly in terms of GDP than lower levels of support, as these larger policy responses help to achieve a significantly higher level of GDP (Annex Figure 2.3).

### Effect on the Overall Fiscal Deficit during an Exceptional Recession
- Support through direct loans (if the program has a coverage of about 70 percent of total loans) would increase overall fiscal deficits by 5.0 percent the first year and by more than 7 percent in the second year while the impact of guarantees in the deficit is almost zero (panel 2).

### Comparative findings from Annex Figure 2.3 (Different Levels of Corporate Support)
- A corporate support program with 30 percent coverage reduces the first-year output contraction from 7.3 percent without support to 5.4 percent.
- If the program has a 70 percent coverage, the output contraction is 4.3 percent.
- The additional effect on output is not proportional:
  - increasing a corporate support program with 30 coverage reduces the output contraction by 2 percentage points,
  - increasing support to cover 70 percent of credit/loans only reduces the output contraction by an additional 1 percent.
- The fiscal impact of the programs does not necessarily increase monotonically:
  - a program of corporate support with 30 percent coverage increases the fiscal deficit by more than 6 percent,
  - a program with 70 percent coverage increases the deficit by 5 percent.

### Subsidy Element in Guarantee Schemes — measurement and components
- Definition: The lifetime cost of a new loan or loan guarantee is referred to as the “subsidy element,” measured by discounting all expected future cash flows associated with the loan or loan guarantee (amounts disbursed, principal repaid, interest received, fees charged, net losses from defaults) to a present value at the date the loan is disbursed.
- Determinants of the present value: the discount rate, the maturity of the loan or guarantee, the interest rate charged for the loan, the grace period or pre-amortization, the share of government guarantees in the case of corporate default, and guarantee fees.
- Subsidy element to firms (normalized for a disbursement to 100):
  - Subsidy Element for Firms = Actual Disbursement – 
    ∑ 퐶퐶퐹퐹_t / (1+푟푟_퐹퐹퐹퐹)^t  (t=1 to M)
  - where M refers to the average maturity of loans and 퐶퐶퐹퐹_t refers to the sum of principal payment, interest payments and guarantee fees. 푟푟_퐹퐹퐹퐹 is the fair-value discount rate.
  - For fully guaranteed loan schemes, the interest rate applied to the interest payments is taken from the government administered interest rate. For partial guaranteed loan schemes, the interest rate is the weighted average between the government administered interest rate applied to fully guaranteed loans for the portion that is guaranteed and the fair-value rate for the portion that is not guaranteed.
- Subsidy element to banks:
  - Subsidy Element to Banks = 
    ∑ (퐶퐶퐹퐹_t,non-guar − 퐠퐠퐠퐠푔푔푔푔   푛푛푡푡_푔푔푔푔푔푔) / (1+푟푟_퐹퐹퐹퐹)^t  (t=1 to M)
    + ∑ 퐶퐶퐹퐹_t,guar   푛푛푡푡_푔푔푔푔푔푔 / (1+푟푟_푔푔푟푟)^t  (t=1 to M)
    − c
  - where 퐶퐶퐹퐹_t,guar and 퐶퐶퐹퐹_t,non-guar refer to the share of cash flows guaranteed and non-guaranteed, respectively. C refers to the bank’s cost of lending.
- Footnote example on interest-rate weighting:
  - For example, in a government guarantee scheme with 80% coverage, the interest rate applied to interest payments is calculated as a weighted average of (1) the mandated government rate used for fully guaranteed loans and (2) a fair-value rate, with the former receiving 80% weight and the latter 20% weight.

*Source: IMF staff calculations.*

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_Source: https://www.imf.org/-/media/files/publications/sdn/2022/english/sdnea2022003.pdf_