## Policy Space Index: Short-Term Response to a Catastrophic Event (wpiea2022123-print-pdf)

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### Overview and purpose
- The Covid-19 crisis prompted assessment of what policy space a country has to respond to a ‘black swan’ event; governments and central banks deployed nearly US$ 12 trillion of fiscal policy support.
- Purpose: propose an effective policy space index to assess short-term policy space available to each country to respond to catastrophic events like Covid-19.
- Key illustrative finding: the index suggests at least 98 countries (about 8 percent of global GDP and 19 percent of population) have no or very limited policy space and may require emergency assistance.

### Working definition and scope
- Working definition: policy space is a country’s ability to finance measures needed to respond to shocks in the short run.
  - Includes financing from all available sources (existing or newly created instruments, own or borrowed resources).
  - Focuses on measures that do not undermine macroeconomic stability during the current year.
  - Measured directly in national currency and as percent of each country’s GDP.
- Short-term focus: confined to very short-term reactions to catastrophic events; excludes structural or longer-term measures to expand space.

### Policy Space Matrix and institutional modifiers
- Matrix framework: 5 institutional rows × 3 quantitative columns.
  - Quantitative components: fiscal stance, monetary stance, reserve stance.
  - Qualitative (institutional) components: currency status (RCC vs non-RCC), income group (AE, EM, LIC), access to capital markets (full, limited, none), debt distress risk (low, moderate, high, in distress), exchange rate regime (flexible, soft peg, hard peg).
- Nominal policy space = threshold − observed policy indicator (can be positive or negative).
- Effective policy space = nominal space adjusted for institutional environment; effective space is bounded below by zero.
  - Institutional adjustments: green (amplify), grey (neutral), yellow/red (restrict).
  - Example: flexible exchange rate amplifies monetary space; fixed peg restricts monetary space.

### Data and sample
- Data source: IMF World Economic Outlook (WEO) database published in October 2020.
- Sample: 178 countries covering 98 percent of global GDP and 97 percent of global population in 2020.
- Country grouping by WEO income classification: advanced economies (AE), emerging markets (EM), low-income countries (LIC). LICs defined as countries eligible to PRGT facilities.

### Fiscal space, borrowing space, and financing space
- Fiscal space here narrowly: sum of borrowing space and financing space (expressed in percent of GDP).
- Borrowing space = difference between public debt-to-GDP ratio and its sustainability threshold.
- Financing space = difference between gross financing needs (GFN) as percent of GDP and its sustainability threshold.
- Indicative debt sustainability benchmarks (percent of GDP):
  - AE: debt level benchmark 85; gross financing needs benchmark 20.
  - EM: debt level benchmark 70; gross financing needs benchmark 15.
- Borrowing and financing thresholds depend on income group (AE, EM, LIC) and access to capital markets (none, limited, full).
- LIC thresholds expressed in present value; AEs and EMs thresholds based on nominal debt stock.
- LIC debt-carrying capacity categories (DSF): strong, medium, weak; DSF uses 5-percent discount rate since 2013 to calculate PV of external debt.
- Fiscal assessment framework (IMF-style blocks):
  - Block 1: initial state of the economy.
  - Block 2: diagnosis of financing availability and debt burden.
  - Block 3: simulation of macro-fiscal impact of discretionary fiscal policies.
- IMF fiscal space classification post-2019: No fiscal space; Fiscal space at risk; Some fiscal space; Substantial fiscal space.

### Monetary space: operational definition and assumptions
- Monetary space defined as central bank’s ability to stimulate economy without compromising price stability; intersection of growth and inflation constraints.
- Operational assumptions:
  - Effective lower bound on policy interest rates: −1 percent.
  - Inflation must remain reasonably close to the target for monetary space to be available.
  - Conventional tools assumed exhausted before unconventional policies used.
- Two complementary definitions:
  - Rate-based: room to lower policy rates (adjusted for effective lower bound).
  - Inflation-based: headroom if headline inflation projected to remain below target or if target can be temporarily raised.
- Example operational choices:
  - Romer and Romer (2017) dummy: 1 if policy rate > 1.25 percent, 0 otherwise.
  - Shadow-rate approaches (Wu and Xia, 2016) can map term-structure to short rate.
- Monetary space also affected by output gap and neutral real rate differentials.

### Reserve space: operational thresholds and RCC adjustment
- Reserve space derived from reserve adequacy metrics: months of prospective imports, ratio of reserves to short-term debt, ratio of reserves to broad money (prudent upper end often set at 20 percent).
- Historical adequacy standards: 3 months of import coverage and 100 percent cover of short-term debt (Greenspan-Guidotti).
- Paper’s operational thresholds (selected):
  - AE: Adequate above 1 month of imports; no short-term debt cover required.
  - EM and LIC: Adequate if maintain 3 or more months of prospective imports; if constrained in capital markets, require 100 percent of short-term debt cover; if unconstrained, 50 percent short-term debt cover adequate.
- Reserve currency countries (RCCs): threshold effectively reduced to 0 reflecting ability to issue their own currency and lower precautionary reserve needs.

### Institutional components: RCC status, income group, capital market access, debt distress, exchange regime
- RCC operational definition: United States and countries in the Euro area (issuers of US dollar and Euro).
- RCC premium estimates (GLS RE model):
  - RCCs have held on average 27.8 percent of GDP more in public debt than non-RCCs (α_d = +27.8).
  - Gross financing needs premium ≈ 14 percent of GDP.
  - RE estimator is used for consistency (POLS suggests +29 for debt).
- Access to capital markets proxied by Moody’s long-term foreign-currency ratings:
  - Full access (investment grade) = 2.
  - Limited access (speculative grade) = 1.
  - No access (near default) = 0.
  - If no rating, assume 0.
- Debt distress (LICs only using DSF ratings): low, moderate, high, in debt distress. For LICs in high risk or in distress, fiscal space set to zero in effective calculation.
- Exchange rate regime classification: hard pegs, soft pegs, floating regimes (AREAER classification). Pegged regimes reduce monetary and reserve usability.

### Effective Policy Space: construction and formulas
- Nominal components expressed in percent of GDP: fiscal space, monetary space, reserve space.
- Effective component = nominal component × probability factor × institutional adjustments; bounded below at zero.
- Step 1 — Effective Fiscal Space (EFS):
  - Probabilistic approach: assume normal distribution with mean equal to median levels (since 2007) and σ such that threshold τ = μ + 2σ.
  - Formulas:
    - B_j = [ τ_d(I_j; κ_j) − D_j + RCC_j × α_d ] × Φ[ μ_d(I_j; κ_j); σ_d(I_j; κ_j) ]
    - F_j = [ τ_f(I_j; κ_j) − GFN_j + RCC_j × α_f ] × Φ[ μ_f(I_j; κ_j); σ_f(I_j; κ_j) ]
    - EFS_j = (B_j + F_j) × (1 − 1{dist})
    - where 1{dist} = 1 if country j is in high risk of or in debt distress (LICs), 0 otherwise.
- Step 2 — Effective Monetary Space (EMS):
  - Probabilistic rate approach: policy rate ~ Normal(mean, SD).
    - For AEs: mean = 1.25, SD = 1.125 (allows effective lower bound −1).
    - For non-AEs: mean = 1.25, SD = 0.625 (0 is 2σ away).
  - Inflation threshold parametrization:
    - μ = 0.75 × τ^π_j; σ = 0.25 × τ^π_j; π_j^max = 1.25 × τ^π_j.
  - Central bank credibility proxied by inverse average deviation from target over last 10 years.
  - Expected inflation room E[Δπ_j] and mapping to policy rate and GDP via elasticities from panel autoregressive models.
  - Elasticity estimates (sum of lagged coefficients):
    - CPI elasticity (ψ_cpi): −0.178*** (standard error 0.012)
    - GDP elasticity (ψ_gdp): −0.132*** (standard error 0.041)
  - EMS formula:
    - EMS_j = [ E[Δπ_j] / ψ_cpi × ψ_gdp ] × (1 − 1{hard})
    - where 1{hard} = 1 if pegged exchange rate, 0 otherwise.
- Step 3 — Effective Reserve Space (ERS):
  - Probabilistic thresholds with μ equal to median reserves (months of imports) in last 15 years and τ = μ + 2σ.
  - ERS formula (simplified presentation):
    - ERS_j = Max{ ( R_j − (...) − (1 − RCC_j) × τ_j^R (I_j) ) − R̅ × 1{soft} ; 0 } × Φ[ μ_R(I_j) ; σ_R(I_j) ] × (1 − 1{hard})
    - If country has a hard peg, ERS_j = 0.
    - For EM and LICs with limited access, require 100% coverage of short-term debt.
    - R̅ (median reserves for soft-peg countries) assumed 4.1 percent of GDP in 2020.
- Step 4 — Total Effective Policy Space:
  - Effective Policy Space_j = EFS_j + EMS_j + ERS_j
  - All components in percent of GDP and additive; bounded below by zero.

### Country classification by effective policy space (Covid-19 application)
- Sample: 178 countries.
- Five groups by decreasing available policy space:
  - Group 1 — RCC Type 1: Reserve currency countries with unrestricted policy space.
    - Number of countries: 12
    - All advanced economies
    - Representing over one third of global GDP
    - Representing almost 8 percent of world’s population
  - Group 2 — RCC Type 2: Reserve currency countries with somewhat restricted policy space.
    - Number of countries: 8
    - All advanced economies
    - Producing about 3 percent of world GDP
  - Group 3 — Non-RCC with substantial policy space (threshold: total policy space > 5 percent of GDP).
    - Number of countries: 60
    - Accounting for about 69 percent of global population
    - Producing roughly half of global GDP
    - Example: an African EM with effective policy space 9.7 percent of GDP = monetary 1.2 + reserves 8.5 + zero fiscal space
  - Group 4 — Non-RCC with limited and/or conditional policy space.
    - Number of countries: 59
    - Accounting for 13 percent of global population
    - Producing 6.3 percent of global GDP
    - Includes sub-groups with conditional policy space that could be mobilized via international organizations/donors
  - Group 5 — Non-RCC with no policy space.
    - Number of countries: 39
    - Representing 1.4 percent of global GDP
    - Representing 5.4 percent of global population
    - These countries have effective policy space equal to zero and require external assistance

### Aggregate impacts observed for Covid-19 (2019–20)
- Average loss of nominal policy space between 2019 and 2020: about 15 percent of GDP.
- Income-group average changes in policy space (percent of GDP):
  - AE: −19.4 (Overall); Monetary: −11.9; Reserves: −7.0; Debt: 0.0; GFN: −0.5
  - EM: −20.0 (Overall); Monetary: −10.7; Reserves: −6.8; Debt: −0.1; GFN: −2.4
  - LIC: −5.6 (Overall); Monetary: −1.6; Reserves: −2.4; Debt: −0.1; GFN: −1.6
  - All: −15.3 (Overall); Monetary: −8.0; Reserves: −5.4; Debt: −0.1; GFN: −1.8
- Loss composition (share of total response, percent):
  - AEs: Fiscal response 100 percent; Monetary 61.2; Reserves 36.0; Debt 0.2; GFN 2.6
  - EMs: Fiscal 100 percent; Monetary 53.4; Reserves 34.1; Debt 0.4; GFN 12.1
  - LICs: Fiscal 100 percent; Monetary 28.4; Reserves 41.8; Debt 2.0; GFN 27.8
  - All: Fiscal 100 percent; Monetary 52.4; Reserves 35.5; Debt 0.5; GFN 11.5
- Key aggregate findings:
  - Loss of nominal fiscal space: 13.4 percent of GDP (drives almost entire loss).
  - Fiscal response accounts for 88 percent of total response on average.
  - Monetary space changed marginally due to historically low interest rates.
  - Reserve space accounts for 11.5 percent of the total response on average.
  - Country-level summary: almost 80 countries representing about 90 percent of global GDP and 75 percent of population seem to have enough policy space or can mobilize additional resources for an immediate response.
  - 39 countries have no policy space and may need emergency assistance; they represent less than 1.5 percent of global GDP but about 5 percent of population.
  - Total of some 98 countries (about 8 percent of global GDP and 19 percent of global population) most likely will need significant assistance.

### Robustness and limitations
- Robustness:
  - PCA on 11 variables produced first 4 principal components accounting for 69 percent of variance; PCA-predicted values correlate 0.718 with the effective policy space index.
  - Correlations by income group: AEs 0.69; EMs 0.59; LICs ~0.3 (index performs better for AEs and EMs).
- Limitations and caveats:
  - Index is static and may miss dynamic interactions (fiscal-monetary feedbacks).
  - Bounding effective components at zero mutes some negative interactions but simplifies tractability.
  - Discrete adjustments (e.g., setting EFS=0 for LICs in high risk/in distress) may oversimplify nuanced cases.
  - Quantitative thresholds (reserve adequacy, debt sustainability) are uncertain in crises.
  - Institutional assessments involve judgment and may not capture country-specific nuances.
  - Short-term focus does not capture medium-term trade-offs or institutional changes that can act as policy instruments.

### Policy implications and uses
- The effective policy space index:
  - Provides a quantitative, conditional snapshot of short-term readiness to address catastrophic events.
  - Can guide decisions on strengthening and using effective policy space, and support cross-country comparability and aggregation for assessing potential financial needs.
  - Can be incorporated as an additional variable in analytical frameworks on policy responses.
- Practical implications from Covid-19 application:
  - Many countries relied primarily on fiscal responses, depleting nominal fiscal space by 13.4 percent of GDP on average.
  - A subset of countries (39) lack effective policy space and need emergency financing and possible debt restructuring.
  - Another subset (59) may mobilize conditional resources but remain highly vulnerable.

*Source: Introduction and selected pages (13–17, 33–52, 60) of "Policy Space Index: Short-Term Response to a Catastrophic Event" (wpiea2022123-print-pdf).*

### Introduction ...........................................................................................................

### Introduction

### Overview
- The Covid-19 crisis raised the question: what policy space does a country have to respond to a ‘black swan’ event?
- Governments and central banks deployed stimulus packages amounting to nearly US$ 12 trillion of fiscal policy support and a massive injection of liquidity from central banks.
- The size of policy responses depended mainly on (i) the policy space available before the pandemic and (ii) institutional features of each economy.

### Purpose and contribution of the paper
- Purpose: to propose an effective policy space index to assess short-term policy space available to each country to respond to catastrophic events like Covid-19.
- Contributions:
  - Provides an overview and stocktaking of the policy space discussion and its components.
  - Proposes a synthetic policy space index.
  - Illustrates the index by assessing short-term policy space immediately available to countries to fight the Covid-19 crisis.
- Key illustrative finding: the index suggests that at least 98 countries (about 8 percent of global GDP and 19 percent of population) have no or very limited policy space and may require emergency assistance.

### How this paper’s concept differs from IMF fiscal space
- The paper’s concept of policy space is distinct from the IMF’s concept of fiscal space.
- IMF (2018a) defines fiscal space as the room for undertaking discretionary fiscal policy by raising expenditure or reducing taxes relative to existing baseline without compromising market access and debt sustainability.
- This paper narrowly defines fiscal space as borrowing space and space related to gross financing needs.
- The paper’s index is quantitative and conditional on institutional features; data limitations may lead to differences from IMF assessments that can include judgment.

### Literature review highlights
- Policy space has been discussed in several contexts:
  - Early 2000s WTO discussions: policy space defined as scope for domestic policies framed by international disciplines; debates on whether international rules restricted developing countries’ policy choices.
  - Post-Global Financial Crisis (GFC) 2008-09: concerns that governments lacked policy space to stimulate growth; lower bound on policy interest rates and high debt constrained responses.
  - Covid-19 (2020): renewed focus on short-term policy space as many authorities realized limitations of their policy tools.
- Prior metrics proposals:
  - Romer and Romer (2018): measured monetary policy space with a dummy for policy rate above zero lower bound and fiscal space by gross debt-to-GDP ratio; found large differences in post-crisis output declines depending on availability of both spaces.
  - Gallagher, Sklar, Thrasher (2019): quantified policy space in trade and investment treaties by scoring indicators.
- Gap addressed: few studies suggested a synthetic index summarizing policy space across multiple macroeconomic dimensions.

### Policy Space Matrix and working definition used in the paper
- Working definition: policy space is a country’s ability to finance measures needed to respond to shocks in the short run.
- Characteristics of this definition:
  - Includes financing from all available sources (existing or newly created instruments, own or borrowed resources).
  - Focuses on measures that do not undermine macroeconomic stability during the current year.
  - Differs from broader literature definitions by:
    - Combining financing across fiscal, monetary, reserves, and debt dimensions rather than focusing mainly on fiscal or borrowing space.
    - Being confined to very short-term reactions to catastrophic events, excluding structural or longer-term measures to expand space.
    - Measuring policy space directly in national currency and as percent of each country’s GDP to enhance policy relevance.

*Source: Introduction — wpiea2022123-print-pdf*

### 16.   In the long run, policy space is part of a macroeconomic framework. In this broader context,

### 16. In the long run, policy space is part of a macroeconomic framework. In this broader context,

### Long-run concept of policy space
- Policy space depends on and reflects fiscal, monetary, exchange rate, structural and other macroeconomic policies.
- Should be defined relative to current state variables: real growth, inflation, fiscal deficit, current account deficit, and debt.
- Assessment requires a dynamic forward-looking model; can potentially incorporate inequality, gender, and climate change variables.
- In this broad sense, policy space is a country’s capacity to tolerate lower growth, higher inflation, worse fiscal and current account deficits, larger public and external debt, and potentially higher inequality, gender disparity, and faster climate change, without compromising macroeconomic stability.
- The assessment of policy space in the long run is beyond the scope of this paper.

### Short-term policy space: matrix framework (5x3)
- Quantitative components: fiscal stance, monetary stance, and reserve stance, operationalized through limits/thresholds on debt and other fiscal indicators, floors on reserves, or lower bounds on central bank policy rate.
- Qualitative components (institutional environment) include:
  - Currency status: reserve or national
  - Income group: AE, EM, LIC
  - Access to capital markets: full, limited, none
  - Debt distress risk: low, moderate, high, in distress
  - Exchange rate regime: flexible, soft peg, hard peg
- Two fiscal components distinguished:
  - Fiscal space measured quantitatively in percent of GDP
  - ‘Fiscal risks’ measured by investors’ sentiments and willingness to lend (access to capital markets and risk of debt distress)
- Assessment of qualitative components involves substantial expert judgment.

### Nominal versus effective policy space
- Nominal policy space = threshold − observed policy indicator (e.g., debt-to-GDP, reserves as months of imports); can be positive or negative.
- Effective policy space = nominal space adjusted for institutional environment (rows of the 5x3 matrix).
  - Green: institutional environment amplifies nominal space.
  - Grey: neutral; allows entirety of nominal space to be used.
  - Yellow/Red: partially or strongly restricts nominal space.
  - Example: flexible exchange rate → monetary space usable; fixed exchange rate → monetary space heavily/fully restricted even if nominal space exists.
- Effective space is always non-negative.

### Data and sample for the index
- Index constructed using IMF’s World Economic Outlook (WEO) database as published in October 2020.
- Database includes 178 countries accounting for 98 percent of global GDP and 97 percent of global population in 2020.
- 20 countries and territories excluded due to lack of sufficient data.
- To address heterogeneity, calculations are done for country groups based on WEO income classification: advanced economies (AE), emerging markets (EM), and low-income countries (LIC). LICs defined as countries eligible to PRGT facilities.

### Fiscal space: definition and measurement
- IMF (2018a) definition: room for undertaking discretionary fiscal policy by raising expenditure or reducing taxes relative to existing baseline without compromising market access and debt sustainability.
- Fiscal space here expanded to include borrowing space and financing space.
- Fiscal space considerations include:
  - Liquidity: market access at reasonable conditions; assessed via DSA indicators related to cost and reliability of market access.
  - Solvency: public debt sustainability; indicators include level and trajectory of debt, gross financing needs, realism of projected adjustment path.
  - Dynamic analysis: whether discretionary fiscal measures preserve fiscal space.
  - Judgment: initial conditions, fiscal multipliers, monetary policy, fiscal credibility, nature and form of spending, complementarity with structural reforms.
- IMF quantitative fiscal space assessments consist of three blocks:
  - Block 1: initial state of the economy (macroeconomic, fiscal, external, cyclical conditions, public contingent liabilities, structural gaps).
  - Block 2: diagnosis based on indicators of financing availability on favorable terms and debt burden, and future fiscal adjustment needs.
  - Block 3: simulation of macro-fiscal impact of alternative discretionary fiscal policies.
- Aggregation typically based on worst or average of included indicators; final desk assessment uses these blocks plus country-specific factors.
- IMF fiscal space assessments are made for a subset of the membership: 69 countries.

### Fiscal benchmarks and classification
- Indicative debt sustainability benchmarks:
  - For advanced economies (AE): debt level benchmark 85 percent of GDP; gross financing needs benchmark 20 percent of GDP.
  - For emerging market economies (EM): benchmarks 70 and 15 percent of GDP respectively.
- If debt level and gross financing needs remain below benchmark in the last year before projections and over the projection period → contribution to fiscal space considered positive; breach for at least one year → contribution negative.
- IMF fiscal space classification (post-2019 revision) categories:
  - No fiscal space (previously part of “limited”): fiscal sustainability and market financing in question or market financing prohibitively expensive.
  - Fiscal space at risk: clear but not imminent risks to fiscal sustainability; marginal fiscal loosening possible.
  - Some fiscal space: meaningful temporary fiscal measures possible within certain limits.
  - Substantial fiscal space: no significant constraint to undertaking temporary fiscal measures.

### Borrowing space
- Borrowing space = country’s capacity to borrow without undermining debt sustainability; closely linked to debt vulnerabilities.
- Borrowing space integrated in the IMF’s debt sustainability framework (DSF).
- DSF classifies LICs into debt-carrying capacity categories: strong, medium, weak, based on composite indicator drawing on historical performance and outlook for real growth, international reserves coverage, remittance inflows, global environment, and World Bank CPIA index.
- Different indicative thresholds for debt burdens depending on debt-carrying capacity; thresholds on external debt set in terms of PV of GDP and in nominal terms.
- A 5-percent discount rate used since 2013 to calculate PV of external debt.
- Borrowing space can differ from fiscal space when non-debt financing options exist (e.g., disposal of government financial assets, sale of non-financial assets, privatization, or in fiscal dominance, monetary financing).

### Financing space and gross financing needs
- Gross financing needs (GFN) is the second component of fiscal space; low budget deficits and smaller upcoming debt repayments increase room for discretionary fiscal action.
- In this paper, nominal fiscal space narrowly defined as the sum of borrowing space and financing space.
- Borrowing space calculation: difference between public debt-to-GDP ratio and its sustainability threshold.
- Financing space calculation: difference between gross financing needs as percent of GDP and the corresponding sustainability threshold.
- Thresholds for both variables defined as function of income group (AE, EM, LIC) and access to capital markets (0, 1, 2).
- For LICs, sustainability thresholds are calculated in present value; for AEs and EMs thresholds based on nominal debt stock.
- Access to capital markets grouped into three categories: none, limited, full.

### Borrowing and Financing Thresholds (matrix values)
- Matrix of thresholds (Public Debt / Gross Financing Needs) in percent of GDP by income group and access to capital markets:
  - Access = None: AE 70 / 5?; EM 53 / 5?; LIC* 51 / 07? (Note: figure in source presents tabular values aligned with AE EM LIC and access categories; preserve source presentation when reproducing.)
  - Access = Limited: AE 70 / 60?; EM 55 / 15?; LIC 15 / 10?
  - Access = Full: AE 85 / 70?; EM 70 / 20?; LIC 15 / 15?
- *Thresholds for LICs are in present value.
- (Source table in the chapter presents these thresholds; refer to that tabular presentation for exact alignment.)

### Monetary space
- Monetary space = central bank’s ability to change policy rates at discretion to achieve macro objectives.
- Constraint from zero lower bound on policy rates, particularly in many advanced economies post-global financial crisis.
- Existence of cash prevents cutting policy rates much below zero.
- Examples of monetary space measures:
  - Romer and Romer (2017): dummy variable equal to 1 if policy interest rate > 1.25 percent at end of previous half-year, 0 otherwise.
  - Wu and Xia (2016): constructed a shadow rate to convert the interest rate term structure into a short-term rate.
- Monetary policy space also affected by output gap and difference between real and neutral real interest rate.

*Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022123-print-pdf.pdf*

### 33.   Monetary space can also be defined as central banks’ leeway to increase inflation. Relative to

### Monetary space can also be defined as central banks’ leeway to increase inflation

### Monetary space: definitions and measurement
- Monetary space exists if headline inflation is projected to remain below target, or if central banks at target intend to raise temporarily the target to give more monetary space to fight the crisis.
- In countries where inflation is well below target, central banks have substantial discretion to reduce policy rates or implement quantitative easing to boost inflation to the targeted level.
- Countries where inflation is below target generally have policy space for additional monetary expansion before inflation expectations change and they reach the targeted inflation level, at least in the short run.
- Lower and upper bounds to the inflation target can affect the use of policy space — countries with inflation above target, but below the upper bound, may assess policy space differently than by simply looking at the target; similar considerations apply for inflation above the upper bound but with relatively low inflation and central bank credibility.
- For countries without an inflation targeting framework, an average 2020 inflation target for their respective income group is used as an implicit inflation target.
- The effective zero bound on policy interest rates is not treated as an absolute impediment; the paper applies an effective lower bound of -1 percent to capture episodes where interest rates dropped below zero in some AEs. On technical grounds, there is no reason why interest rates cannot be set deeply negative “if backed up by measures to prevent cash hoarding by financial firms” (Lilley and Rogoff, 2020).
- Monetary space (operational definition in this paper): central banks’ ability to stimulate the economy without compromising price stability, measured as an intersection of growth and inflation constraints. Central banks are assumed to exhaust conventional monetary policy tools before using unconventional policies such as QE.

### Key operational assumptions for monetary space
- Effective lower bound on policy interest rates: -1 percent.
- Inflation must remain reasonably close to the target for monetary space to be available.
- Conventional tools are assumed to be exhausted prior to using unconventional policies.

### Reserve space: concepts and operational thresholds
- Reserve space is the third quantitative component of policy space and is derived from reserve adequacy metrics.
- Traditional reserve adequacy metrics include:
  - Months of prospective imports (import cover).
  - Ratio of reserves to short-term debt.
  - Ratio of reserves to broad money (with an upper end of a prudent range typically set at 20 percent).
- Widely used adequacy standards historically: 3 months of import coverage and 100 percent cover of short-term debt (Greenspan-Guidotti rule).
- Combination metrics:
  - Expanded Greenspan-Guidotti: short-term debt plus the current account deficit to reflect potential 12-month financing need.
  - Wijnholds and Kapteyn (2001): short-term debt and broad money, considering exchange rate regimes and country risks.
  - Jeanne and Rancière (2011): optimal reserve model suggesting many EMs optimally hold reserves at around 80-100 percent of short-term debt plus the current account deficit.
- IMF ARA framework: assesses reserve adequacy by groups (developed, emerging, developing and credit constrained economies) using a broad view of potential risks, sources of shocks, and vulnerabilities.

### Paper’s operational definition of nominal reserve space and adequacy thresholds
- Nominal reserve space is defined as the level of reserves exceeding two reserve adequacy metrics.
- Adequacy thresholds depend on the country’s income group and access to capital markets. Selected matrix thresholds (as used in Figure 4) include:
  - AE: Adequate above 1 month of imports; no short-term debt cover required.
  - EM and LIC: Adequate if the country maintains 3 or more months of prospective imports; additionally, if they have at least some constraints to international capital markets, EM and LIC reserves need to accrue to 100% of short-term debt. If not constrained, assume only 50% of short-term debt cover is adequate.
- The paper adopts a simpler approach given that more complex combination metrics and detailed ARA-based metrics are not available for most countries.

### Institutional environment and its components
- Policy space critically depends on the institutional environment. Five components considered:
  - International status of the local currency (reserve currency countries (RCCs) vs non-RCCs).
  - Country’s income group.
  - Access to international capital markets.
  - Risk of debt distress.
  - Exchange rate regime.

### International status of national currency (RCCs vs non-RCCs)
- Reserve currency definition and context:
  - Reserve currencies are foreign currencies held in significant quantities by central banks as part of foreign exchange reserves and provide a store of value and ready access to international liquidity.
  - IMF COFER identifies currencies held as foreign exchange reserves; currencies separately identified and reported in COFER include the U.S. dollar, Euro, Chinese renminbi, Japanese yen, Pound sterling, Australian dollar, Canadian dollar, Swiss franc, and other currencies reported as one group.
  - At least 26 countries can issue reserve currencies.
  - Iancu et al. (2020) define reserve currencies as currencies separately identified and reported in COFER: eight currencies currently in use (the SDR currencies—US dollar, euro, Japanese yen, British pound, and Chinese renminbi, plus the Swiss franc, Canadian dollar, and Australian dollar—comprising 97 percent of total allocated reserves), and three currencies preceding and later replaced by the euro.
  - The SDR basket currencies (U.S. dollar, euro, Chinese yuan, Japanese yen, and U.K. pound sterling) can also be considered reserve currencies.
- Paper’s operational definition of RCCs: the United States and countries in the Euro area (their central banks issue reserve currencies: US dollar and Euro, respectively).
- Implications for reserve needs and policy space:
  - In RCCs, money cannot ‘run out’—governments can always borrow from their own central banks; balance sheet expansion risks are manageable if inflation expectations remain anchored.
  - RCCs and countries with predictable access to reserve currencies likely need lower reserve buffers; reserve currency issuers and countries with standing central bank swap lines are unlikely to need sizable reserves for precautionary purposes.
  - Non-RCCs face institutional disadvantages: limited external demand for their currency constrains central bank balance sheet expansion; credibility concerns can lead to currency substitution, exchange rate depreciation, and imported inflation.

### Income group effects on policy space
- Higher income level amplifies policy space due to stronger institutional stability and resilience.
- Advanced economies (AEs) advantages highlighted:
  - Higher sustainability thresholds for public debt and GFN: 85/20 relative to 70/15 percent for public debt and GFN respectively.
  - Capacity to set negative policy rates (so far only central banks in AEs have set negative interest rates).
  - Lower reserve adequacy thresholds: only 1 month of import coverage for AEs relative to 3 months for EMs and LICs, and lower short-term debt coverage ratios.
- Combination of higher debt/GFN thresholds, negative policy rates, and lower reserve coverage creates additional policy space in AEs relative to other countries.

### Access to capital markets: measurement and implications
- Access to capital markets can limit or expand a country’s policy space. Market access influences fiscal space via borrowing capacity at reasonable risk premia.
- Indicators signaling market access include sovereign bond spreads and debt characteristics (public debt held by non-residents, public debt in foreign currency, short-term debt share, and external financing requirements).
- Operational measure in the paper: Moody’s global ratings (130 countries covered).
  - Ratings on long-term obligation in foreign currency reflect opinions on relative credit risk for obligations with original maturity of one year or more.
  - The paper defines an indicator taking three values based on Moody’s classifications:
    - Full access (investment grade ratings) = 2.
    - Limited access (non-investment grade ratings with speculative elements) = 1.
    - No access (noninvestment grade ratings with obligations in or near default) = 0.
  - If no classification is available, assume a value of 0 (no access).

### Risk of debt distress and its constraint on policy space
- Risk of debt distress severely limits policy space.
- For LICs under the DSF (IMF, 2020b), risk signals are derived by comparing debt burden indicators with indicative thresholds over a projection period.
- Four ratings for risk of external public debt distress:
  - Low risk: none of the debt burden indicators breach thresholds under baseline and stress tests.
  - Moderate risk: none breach thresholds under baseline, but at least one breaches under stress tests.
  - High risk: any external debt burden indicator breaches its threshold under the baseline, but the country does not currently face repayment difficulties.
  - In debt distress: country already experiencing difficulties servicing debt, evidenced by arrears, ongoing or impending debt restructuring, or indications of high probability of a future debt distress event (e.g., large near-term breaches of debt and debt service indicators, or significant or sustained breach of thresholds).

*Source: IMF Working Paper (content from pages 13–17 of the provided PDF).*

### 52.   There are no IMF debt distress ratings or methodology for countries other than LICs.  The reform of

### wpiea2022123-print-pdf - 52.   There are no IMF debt distress ratings or methodology for countries other than LICs.  The reform of

### Debt distress ratings and public debt limits policy
- There are no IMF debt distress ratings or methodology for countries other than LICs.
- The reform of the policy on public debt limits (IMF, 2020f):
  - encouraged adequate debt disclosure to the IMF;
  - allowed for greater tailoring of debt conditionality for LICs;
  - encouraged the broader use of debt conditionality in present value terms accommodated non-concessional borrowing (subject to safeguards);
  - clarified the definition of concessional debt.
- For all countries other than LICs, debt sustainability is assessed relative to country-specific circumstances; IMF DSAs include bottom-line assessments of debt distress risks that:
  - vary from country to country;
  - use no common terminology related to risks of debt distress;
  - may include multiple qualifications reflecting each country’s specificity.
- Because of low comparability, debt distress risk considerations were incorporated into the policy space measure only for LICs (countries for which harmonized ratings are already produced and published by the Fund). This also reflects an additional adjustment for the extra risk that lending to LICs can have.

### Exchange rate regime and its effect on policy space
- Exchange rate regime has an ambiguous impact on policy space:
  - fixed exchange rate regime severely limits policy space;
  - floating exchange rate regime amplifies policy space.
- Members of currency unions: from individual point of view, flexible exchange rate is not unrestrictive because exchange rate with other members is fixed; policy space depends on the need to defend a certain level of exchange rate.
- Countries with floating exchange rate regimes (no explicit or implicit commitment to a specific level) have more policy space than countries with any form of managed exchange rates.
- Classification followed: hard pegs, soft pegs, and floating regimes (AREAER classification; paper follows types into hard pegs, soft pegs, floating regimes).

### Policy Space Index — overview
- Effective policy space is a conditional measure combining quantitative and institutional components.
- Nominal policy space components (expressed in percent of GDP):
  - fiscal space;
  - monetary space;
  - reserve space.
- Nominal space for each quantitative component is defined as distance to its respective threshold weighted by a probability of constraint (probability increases as indicator moves closer to threshold).
- Each quantitative component is adjusted for country institutional environment: exchange rate regime, and risk of debt distress (only for LICs).
- Effective policy space index = aggregated index of three quantitative and five institutional components.
- The calculations assess space to fight a negative shock (e.g., the Covid-19 pandemic in 2020) and are for short-term policy actions.

### Effective policy space — Step 1: Effective fiscal space (EFS)
- Fiscal space composed of borrowing space and financing space.
- Thresholds used: those displayed in Figure 3 (depend on country income level and access to capital market); thresholds applied to level of debt and current value of gross financing needs.
- Probabilistic approach: assume normal distribution with mean equal to median levels of public debt and gross financing needs (since 2007), and standard deviation such that DSF thresholds are at 2 standard deviations from the mean (so τ = μ + 2σ).
- In practice, country j’s effective borrowing and financing space B_j and F_j are computed as:
  - B_j = [ τ_d(I_j; κ_j) − D_j + RCC_j × α_d ] × Φ[ μ_d(I_j; κ_j); σ_d(I_j; κ_j) ]
  - F_j = [ τ_f(I_j; κ_j) − GFN_j + RCC_j × α_f ] × Φ[ μ_f(I_j; κ_j); σ_f(I_j; κ_j) ]
  - where τ_d, τ_f depend on access to capital markets κ_j and income level I_j; RCC_j = 1 if reserve currency country; Φ[μ;σ] is cumulative normal distribution with mean μ and standard deviation σ defined so τ = μ + 2σ.
- Adjustment for debt distress applied exclusively for LICs and discrete: countries in high risk of or in debt distress have no fiscal space regardless of underlying indicators (assuming no official financing available).
- Effective fiscal space:
  - EFS_j = (B_j + F_j) × (1 − 1{dist})
  - where 1{dist} = 1 if country j is in high risk of or in debt distress, 0 otherwise.
- RCC premium estimates (from panel RE model):
  - GLS RE estimate of α_d suggests RCCs have held, on average, 27.8 percent of GDP more in public debt than non-RCCs (controlling for fiscal risk and other characteristics). Practically, RCCs get a “premium” of +27.8 to their debt threshold.
  - POLS estimator suggests a premium of +29.
  - For gross financing needs, data suggests a premium of about 14 percent of GDP.
  - For consistency, the RE estimator is kept.

### Effective policy space — Step 2: Effective monetary space (EMS)
- Monetary space determined by:
  1) room for central bank to lower policy rates and/or conduct unconventional monetary policy;
  2) country’s current inflation rate and central bank’s inflation target.
- Probabilistic approach for policy rate:
  - Normal distribution with mean 1.25 and standard deviation 0.625 for policy rate (restricts central banks from lowering rates below 0; 0 is 2σ away from the mean).
  - For AEs: mean = 1.25, standard deviation = 1.125 (so policy rate can effectively go as low as −1).
  - Data used: central banks’ policy rates (IMF, 2020e). When data not available, average of income group used.
- Inflation threshold approach:
  - central bank’s inflation target used as threshold;
  - μ chosen to be ¾ of the target and σ chosen to be ¼ of the target (thus allow country to exceed inflation target by 25% (1σ) in short term).
  - Maximum tolerable inflation in short term: π_j^max = μ_j + 2σ_j = 0.75 τ_j^π + 0.5 τ_j^π = 1.25 τ_j^π (where τ_j^π is country j’s inflation target).
  - Central bank credibility modelled as inverse of average deviation from annual inflation target in last 10 years (countries with inflation near target have additional monetary space).
- Effective room for additional inflation:
  - E[Δπ_j] = (π_j^max − π_j) / σ_τ × Φ[ μ_π(I_j) ; σ_π(I_j) ]
  - where σ_τ = sqrt( (1/n) Σ_{i=1}^n (π_i − τ_j^π)^2 ).
- Translate into monetary policy action using elasticity estimates from two panel autoregressive processes (quarterly data, 4 lags, country fixed effects).
  - Estimated equations (summed lag coefficients give short-term elasticities ψ_cpi and ψ_gdp):
    - cpi_{i,t} = c1 + A(L) X_{i,t} + γ1 R_t + δ1 D_t + u_{i,1} + ε_{i,t,1}
    - gdp_{i,t} = c2 + B(L) X_{i,t} + γ2 R_t + δ2 D_t + u_{i,2} + ε_{i,t,2}
    - X_{t,i} = [ cpi_{t,i}, gdp_{t,i}, R_{t,i} ].
  - Using these, number of percentage points central bank can reduce policy rate until max tolerable inflation is reached, and resulting short-run GDP boost, are computed.
- Effective monetary space:
  - EMS_j = [ E[Δπ_j] / ψ_cpi × ψ_gdp ] × (1 − 1{hard})
  - where 1{hard} = 1 if country has pegged exchange rate regime, 0 otherwise. (Adjustment because pegged regimes limit independent monetary policy; soft pegs allow limited flexibility.)

- Key elasticity estimates reported (sum of lagged coefficients; coefficients and standard errors shown):
  - CPI elasticity (ψ_cpi): −0.178*** (standard error 0.012)
  - GDP elasticity (ψ_gdp): −0.132*** (standard error 0.041)
  - Note: *** significant at 1%; ** significant at 5%; * significant at 10%.

### Effective policy space — Step 3: Effective reserve space (ERS)
- Probabilistic approach: reserve adequacy thresholds (Figure 4) defined as μ + 2σ for each income group; μ set equal to median reserves as months of imports in last 15 years.
- Capacity to use reserves constrained by exchange rate regime:
  - hard peg: reserves viewed primarily to defend peg;
  - floating exchange rate: reserves less critical.
- Countries classified into three exchange rate groups: flexible, soft pegs, hard pegs.
- Adjustment for RCCs: threshold effectively reduced to 0 (no minimum threshold) reflecting RCCs’ ability to issue own currency.
- Effective Reserve Space formula (country j):
  - ERS_j = Max{ ( R_j − (1 − 1{AE}) × ( SD_j × ρ(κ) ) / M_{j;t+1} × 12 − (1 − RCC_j) × τ_j^R (I_j) ) − R̅ × 1{soft} ; 0 } × Φ[ μ_R(I_j) ; σ_R(I_j) ] × (1 − 1{hard})
  - where:
    - R_j = country j’s reserves (dollars);
    - M_{j;t+1} = next year’s imports (dollars);
    - SD_j = short-term debt (dollars);
    - ρ(κ_j) = 1 if limited/no access to capital markets, 0.5 if full access;
    - R̅ = median level of reserves held by countries with soft peg (assumed in 2020 to be 4.1 percent of GDP);
    - 1{AE}, 1{soft}, 1{hard} are indicator functions;
    - For EM and LICs with limited access, 100% coverage of short-term debt required (Greenspan-Guidotti rule).
  - If country has a hard peg, effective reserve space = 0.

### Effective policy space — Step 4: Total effective policy space
- Total effective policy space for country j:
  - Effective Policy Space_j = EFS_j + EMS_j + ERS_j
- All components expressed in percent of GDP and additive.
- Components are bounded below by zero (effective components cannot be negative even if nominal indices are negative).
- Rationale: negative policy space is practically equivalent to zero capacity to respond to shocks; the approach allows additive aggregation while acknowledging potential correlations/interactions reflected in variable levels.

### Limitations and caveats
- Index is static; may miss dynamic interactions among components (e.g., large fiscal deficit eroding monetary space via monetary financing).
- Bounding effective components at zero mutes some interactions (e.g., fiscal-monetary feedbacks).
- Use of discrete adjustments (e.g., setting effective fiscal space to zero when VE fiscal risk is high) simplifies and improves tractability but may oversimplify complex assessments.
- VE ratings and access classifications may not fully capture nuances (e.g., country with moderate fiscal space could still receive high-risk VE rating; countries with low credit classification can still have some market access).
- The approach does not model dynamic behavior or monetary–fiscal interaction effects (outside scope of paper).

*Italic: Content derived from the provided excerpt of wpiea2022123-print-pdf.*

### 60.   Several groups of countries can be distinguished by effective policy space. The 178 countries

### Effective Policy Space During Covid-19

### Classification of countries by effective policy space
- Sample: 178 countries (out of 187 countries in the WEO database as of October 2020).
- Countries are classified into five groups by decreasing level of available policy space (Figure 8).

### Group 1 — Reserve currency countries with unrestricted policy space (RCC Type 1)
- Characteristics:
  - Reserve currency countries (RCCs) with full access to capital markets and low fiscal risks.
  - Virtually unrestricted resources to fight crises in the short run, comparable to the Covid-19 magnitude.
  - Some European countries classified as having unlimited effective policy space because debt levels are below the sustainability thresholds defined for RCCs and Euro-area membership implies no real minimum reserve level.
  - Institutional environment: flexible exchange rate regime, full access to capital markets, benefits of issuing reserve currencies.
- Counts and shares:
  - Number of countries: 12
  - All advanced economies
  - Representing over one third of global GDP
  - Representing almost 8 percent of world’s population

### Group 2 — Reserve currency countries with somewhat restricted policy space (RCC Type 2)
- Characteristics:
  - RCCs with limited access to capital markets and mounting signs of fiscal risks.
  - Still enough resources in the short run to fight most crises but mounting vulnerabilities need addressing.
  - Euro-area membership can be a constraint as well as a source of reserve availability (ECB can expand balance sheet; availability limited only by inflation).
- Counts and shares:
  - Number of countries: 8
  - All advanced economies
  - Producing about 3 percent of world GDP

### Group 3 — Non-reserve currency countries with substantial policy space
- Definition:
  - Total policy space in excess of the median decrease in real GDP in the year 2020; threshold set at 5 percent.
- Examples:
  - African EM example: effective policy space of 9.7 percent of GDP = monetary space 1.2 percent of GDP + reserve space 8.5 percent of GDP + zero fiscal space; combined monetary and reserve space exceed crisis cost assessed at an average of 6.3 percent of GDP.
  - European AE outside the Euro area example: no monetary or reserve space due to fixed exchange rate, but substantial fiscal space (debt well below sustainability threshold) and full access to capital markets.
- Counts and shares:
  - Number of countries: 60
  - Accounting for about 69 percent of global population
  - Producing roughly half of global GDP
  - Many advanced economies with non-reserve currencies, oil producers, and other commodity exporters included

### Group 4 — Non-reserve currency countries with limited and/or conditional policy space
- Characteristics:
  - Sub-group with limited effective policy space: some positive effective space but insufficient to cover all Covid-19 financing needs in short run.
  - Sub-group with conditional effective policy space: no effective policy space but some nominal fiscal space (additional debt carrying capacity) that could be filled by international organizations and bilateral donors under terms and conditions (concessional and non-concessional).
  - LICs in debt distress do not have conditional effective policy space.
- Examples:
  - Middle Eastern EM example: effective policy space equal to 1 percent of GDP (insufficient versus 5 percent crisis cost); nominal fiscal space 17.1 percent of GDP ⇒ conditional policy space equal to 17.1 percent of GDP.
  - LIC in Southeast Asia example: fiscal space 2.0 percent of GDP and monetary space 0.1 percent of GDP with a soft peg and limited access to capital markets; no reserve space; effective policy space 2.1 percent of GDP; additional financing available 1 percent of GDP ⇒ total conditional policy space 3.1 percent of GDP.
- Counts and shares:
  - Number of countries: 59
  - Accounting for 13 percent of global population
  - Producing 6.3 percent of global GDP
  - Group composition: advanced, middle-income and LICs with country-specific constraints

### Group 5 — Non-reserve currency countries with no policy space
- Characteristics:
  - Policy space index equal to zero; nominal components may indicate some space but institutional characteristics prevent its use.
  - No room even for conditional financing; must rely on grants and donors willing to take substantial default risks.
- Examples:
  - Small African country: some reserve space but fixed exchange rate and no space across other dimensions ⇒ effective policy space equal to zero; must rely entirely on external help.
  - Another African country: substantial fiscal space but in debt distress with no access to capital markets ⇒ cannot use available space.
- Counts and shares:
  - Number of countries: 39
  - Representing 1.4 percent of global GDP
  - Representing 5.4 percent of global population

### Aggregate impacts of the Covid-19 crisis on policy space (2019–20)
- Average loss of nominal policy space between 2019 and 2020: about 15 percent of GDP.
- Income-group differences in average change in policy space (in percent of GDP):
  - AE: -19.4 (Overall); Monetary: -11.9; Reserves: -7.0; Debt: 0.0; GFN: -0.5
  - EM: -20.0 (Overall); Monetary: -10.7; Reserves: -6.8; Debt: -0.1; GFN: -2.4
  - LIC: -5.6 (Overall); Monetary: -1.6; Reserves: -2.4; Debt: -0.1; GFN: -1.6
  - All: -15.3 (Overall); Monetary: -8.0; Reserves: -5.4; Debt: -0.1; GFN: -1.8
- Loss composition (share of total response, percent):
  - AEs: Fiscal response represented 100 percent of total; Monetary 61.2; Reserves 36.0; Debt 0.2; GFN 2.6
  - EMs: Fiscal 100 percent; Monetary 53.4; Reserves 34.1; Debt 0.4; GFN 12.1
  - LICs: Fiscal 100 percent; Monetary 28.4; Reserves 41.8; Debt 2.0; GFN 27.8
  - All: Fiscal 100 percent; Monetary 52.4; Reserves 35.5; Debt 0.5; GFN 11.5
- Key findings on crisis response composition:
  - Loss of nominal fiscal space: 13.4 percent of GDP (drives almost entire loss).
  - Fiscal response accounts for 88% of the total response on average.
  - Monetary space changed only marginally (persistence of historically low interest rates).
  - Reserve space accounts for 11.5 percent of the total response (on average used as last resort).
  - Income-level relationships:
    - Fiscal response share: AE 97 percent of total; EM 87 percent; LICs 70 percent.
    - Reserve reliance: LICs 28 percent share; AE 2.6 percent; EMs 12.1 percent.

### Robustness checks of the policy space index
- Principal Component Analysis (PCA) approach used to validate index.
- Variables included in PCA (11 variables): 1) debt-to-GDP ratio, 2) gross financing needs as percent of GDP, 3) reserves in months of imports, 4) CPI inflation, 5) VE’s fiscal risk index, 6) Moody’s 2020 sovereign debt ratings, 7) categorical exchange rate rigidity (1 flexible, 2 soft peg, 3 hard peg), 8) dummy for reserve currency countries (0 non-RCC, 1 RCC), 9) current account balance (percent of GDP), 10) real GDP growth, 11) GDP per capita (proxy for income group).
- PCA results:
  - First 4 principal components account for 69 percent of total variance and are used to compute a PCA-based index.
  - Regression of the effective policy space index on the 4 principal components yields predicted values with correlation 0.718 with the original effective policy space measure, supporting validity and robustness.
  - Correlations by income group between PCA measure and original index:
    - AEs: 0.69
    - EMs: 0.59
    - LICs: about 0.3
  - Index performs better for AEs and EMs than for LICs; LICs require more country-specific modeling and data may have measurement issues.

### Conclusions and policy implications
- The effective policy space index:
  - Differs from IMF’s approach to assessing fiscal space.
  - Can be used to take a snapshot of a country’s readiness to address a catastrophic event in the short run, showing overall magnitude and components of policy space.
  - Can guide decisions on strengthening and using effective policy space, and support cross-country comparability and aggregation to assess potential financial needs.
  - Can be included as an additional variable in analytical frameworks on policy responses.
- Key aggregate conclusions from Covid-19 application:
  - Almost 80 countries representing about 90 percent of global GDP and 75 percent of population seem to have enough policy space or can mobilize additional resources for an immediate response.
  - 39 countries have no policy space and may need emergency assistance in the form of direct financing and debt restructuring; these countries represent less than 1.5 percent of global GDP but about 5 percent of population.
  - About 59 countries may have limited policy space and can conditionally mobilize additional short-term resources; they are highly vulnerable in the short run.
  - Total of some 98 countries, producing about 8 percent of global GDP and home to about 19 percent of global population, most likely will need significant assistance in fighting the crisis.
  - All countries on average lost about 15 percent of their nominal policy space because of measures deployed in response to the Covid-19 crisis.
- Limitations and areas for further work:
  - Quantitative thresholds (reserve adequacy, debt sustainability) are highly uncertain in crises.
  - Qualitative institutional assessments may reflect judgment and distort outcomes.
  - Short-term focus may not capture medium-term trade-offs (e.g., monetary policy use vs. price stability).
  - Institutional environment can change and act as a policy instrument (floating exchange rates, capital controls, accepting higher borrowing costs, negotiating debt relief).
  - Need for more granular, country-specific, and income-group-specific definitions and indicators (fiscal vs. borrowing space; exchange rate regimes; capital controls; fiscal and debt distress risks).

*Source: Authors’ calculations (IMF Working Paper excerpt).*

### References

### References — Policy Space Index: Short-Term Response to a Catastrophic Event (Working Paper No. WP/2022/123)

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- IMF, 2020f. Reform of the Policy on Public Debt Limits in IMF-Supported Programs. IMF Policy Paper No.20/06. https://www.imf.org/en/Publications/Policy-Papers/Issues/2020/11/11/Reform-of-the-Policy-on-Public-Debt-Limits-in-IMF-Supported-Programs-49876.  

### Policy debates, working papers, and institutional perspectives
- Gallagher, K.P., Sklar, S. and Thrasher, R., 2019. Quantifying the Policy Space for Regulating Capital Flows in Trade and Investment Treaties. G-24 Working Paper. Intergovernmental Group of Twenty Four. http://www.bu.edu/gdp/2019/04/03/quantifying-the-policy-space-for-regulating-capital-flows-in-trade-and-investment-treaties.  
- Grabel, I., 2011. Not Your Grandfather's IMF: Global Crisis, ‘Productive Incoherence’ and Developmental Policy Space. Cambridge Journal of Economics, 35(5), pp.805-830.  
- Greenspan, A., 1999. Currency Reserves and Debt. Federal Reserve System. https://www.federalreserve.gov/BoardDocs/Speeches/1999/19990429.htm.  
- Wijnholds, J. and Kapteyn, A., 2011. Reserve Adequacy in Emerging Market Economies. IMF Working Paper. WP/01/143 https://www.imf.org/external/pubs/ft/wp/2001/wp01143.pdf.  
- Muchhala B. (ed.), 2007. The Policy Space Debate: Does a Globalized and Multilateral Economy Constrain Development Policies? Woodrow Wilson International Center for Scholars, Asia Program Special report, No.136. https://www.wilsoncenter.org/sites/default/files/media/documents/publication/asia_136FINAL.pdf.  
- Ocampo, J.A. and Vos, R., 2008. Policy Space and the Changing Paradigm in Conducting Macroeconomic Policies in Developing Countries. Press & Communications CH 4002 Basel, Switzerland, p.1-28p.  
- Page, Sh., 2007. Policy Space: Are WTO Rules Preventing Development? https://www.odi.org/publications/82-policy-space-are-wto-rules-preventing-development? Overseas Development Institute (ODI), Policy Brief, January.  
- Turner, A., 2015. The Case for Monetary Finance–An Essentially Political Issue. In 16th Jacques Polak Annual Research Conference, IMF, November. https://www.imf.org/external/np/res/seminars/2015/arc/pdf/Turner_pres.pdf.  
- Gallagher, K.P., Sklar, S. and Thrasher, R., 2019. Quantifying the Policy Space for Regulating Capital Flows in Trade and Investment Treaties. G-24 Working Paper. Intergovernmental Group of Twenty Four. http://www.bu.edu/gdp/2019/04/03/quantifying-the-policy-space-for-regulating-capital-flows-in-trade-and-investment-treaties.  

### Media, speeches, and institutional remarks
- Chen Y. and R. Woo, 2020. Plenty of Policy Room in China to Cushion Coronavirus Impact. Reuters, April 24. https://www.reuters.com/article/us-china-economy-ndrc/plenty-of-policy-room-in-china-to-cushion-coronavirus-impact-says-state-planner.  
- Landau J.-P. 2020. Money and Debt: Paying for the Crisis. Vox EU. June 23. https://voxeu.org/article/money-and-debt-paying-crisis?  
- Managing Director Georgieva’s Remarks at the Conference on Lessons from the Global Financial Crisis in The Age of COVID-19. November 23, 2020. Available at https://www.imf.org/en/News/Articles/2020/11/23/sp112320md-remarks-oap-on-lessons-from-gfc-in-the-age-of-covid19.  
- Moody’s, 2020. Rating Symbols and Definitions. Moody’s Investors Service. January. https://www.moodys.com/researchdocumentcontentpage.aspx?docid=PBC_79004.   
- Soto A., 2020. With Little Policy Room, Africa Central Banks Fret Over Debt. Bloomberg, February 5. https://www.bloomberg.com/news/articles/2020-02-06/with-little-policy-room-africa-central-banks-sound-debt-alarm.   
- Kozul-Wright R. and K. Gallagher, 2020. Developing Countries Urgently Need More Policy space to Fight COVID-19. https://www.opendemocracy.net/en/oureconomy/combat-covid-19-developing-countries-need-be-granted-more-policy-space.  

*Policy Space Index: Short-Term Response to a Catastrophic Event — Working Paper No. WP/2022/123*

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_Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022123-print-pdf.pdf_