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### I. Introduction — scope and contributions
- Natural disasters described as key threats to fiscal stability, weakening public finances by increasing expenditures or reducing tax revenues and leading to rising public debt levels and downgraded sovereign credit ratings.
- Frequency of natural disasters has "quintupled" over the past 50 years; total economic losses from natural hazards estimated at approximately USD 6 trillion.
- The first three years of the 2020s alone accounted for USD 228 billion in losses (United Nations, 2021; World Bank, 2024).
- Paper focus: examine the relationship between natural disaster risks and the strength of fiscal rules, testing whether increased risks lead to stronger fiscal frameworks within the same year that natural disaster risks increased.
- Two contributions:
  - Extend literature by including natural disaster risks as determinants of fiscal rules (building on Badinger & Reuter, 2017a).
  - Develop a new index for measuring the strength of fiscal rules, building on IMF (2009) and Schaechter et al. (2012).

### II. Methodology and index construction
- Sample and period:
  - Panel data for 104 countries operating fiscal rules from 2000 to 2021.
  - Sample composition (as of 2021): 34 advanced economies (AE), 47 emerging market economies (EM), and 23 low-income countries (LIC).
  - ERI classification: 36 high ERI, 26 medium ERI, and 42 low ERI countries.
- Fiscal rules strength index:
  - Derived from IMF fiscal rules database for 104 countries, 2000–2021.
  - Sub-indices by rule type: debt, budget balance, expenditure, and revenue rules.
  - Sub-indices by key characteristics: number of fiscal rules, coverage, legal basis, formal enforcement, supporting procedures, flexibility.
  - PCA used to aggregate variables into an overall fiscal rules strength index from variables: (i) enforcement score; (ii) coverage score; (ii) legal basis score; (iii) supranational rules score; (iv) index of supporting procedures for monitoring of compliance and enforcement; (v) flexibility score; (vi) average number of fiscal rules; and (vii) the ratio of national to total fiscal rules.
  - PCA weighting retains more than 80 percent of the original data variance.
  - Index standardized to have a zero mean and a standard deviation of one.
  - Caveat: "The fiscal rules strength index measures de jure strength and does not measure de facto adherence to fiscal rules."
- Natural disaster risk measure:
  - Exposure Risk Index (ERI) from the World Risk Index (WRI) (2023 World Risk Report by Bündnis Entwicklung Hilft).
  - WRI components: (i) exposure to natural disaster risks; and (ii) vulnerability (susceptibility, lack of coping capacities, lack of adaptive capabilities).
  - ERI risk categories per World Risk Report thresholds: low (0 – 0.56), medium (0.57 – 1.76), high (1.77 – 100).
- Empirical approach:
  - Panel two-stage least squares (2SLS) estimation with an autoregressive (AR) term to address endogeneity.
  - Instruments include past exposure risk index of close neighbors, working-age population, political stability, GDP, GDP growth, rule of law, government expenditure as a percent of GDP, lag of inflation targeting, lag of monetary union, government effectiveness.
  - Structural model: fr_{i,t} = β1 X_{i,t} + v_i + t_i + e_{i,t}, where fr is fiscal rules index and X includes macro, institutional, and ERI variables.

### III. Data and stylized facts
- Dataset: 104 countries, 2000–2021; ERI and fiscal rule index aligned over same period.
- Rule prevalence and patterns:
  - "Budget balance and debt rules are most frequently used across all countries."
  - "Expenditure and revenue rules are implemented relatively less in LIC."
- Evolution of ERI and fiscal rules:
  - Relative to base year 2000, increase in ERI "preponderantly in LIC."
  - Average ERI generally high in AE but decreasing relative to base year over past two decades; average ERI increasing in EM and LIC.
  - Medium-ERI countries' risk has been increasing at "0.71 percent on average."
  - Aggregate fiscal rules strength index increased rapidly after the global financial crisis; advanced economies as frontrunners.
  - By 2021, indexes for all three ERI categories had converged to "approximately to 0.47."
- Regional average ERI (exact values):
  - Asia (APD): 16.14
  - Americas (WHD): 11.59
  - Middle East (MCD): 3.21
  - Europe (EUR): 2.46
  - Africa (AFR): 0.79
- Notable country ERI outliers:
  - APD: Japan (43.88), Indonesia (38.54), India (35.91)
  - WHD: Mexico (50.02), United States (39.81), Colombia (31.49)
- Descriptive statistics (selected exact values):
  - All countries — Fiscal Rules Strength Index: Mean 0.434; Min 0.000; Q1 0.294; Median 0.378; Q3 0.557; Max 1.000
  - All countries — Exposure Risk Index (ERI): Mean 5.776; Min 0.020; Q1 0.203; Median 0.870; Q3 3.815; Max 50.200
  - AE — Fiscal Rules Strength Index: Mean 0.513; ERI: Mean 6.143
  - EM — Fiscal Rules Strength Index: Mean 0.427; ERI: Mean 7.301
  - LIDC — Fiscal Rules Strength Index: Mean 0.313; ERI: Mean 2.116

### IV. Empirical results — magnitudes and heterogeneity
- Full-panel 2SLS (all countries) — key coefficient magnitudes (all reported as percent changes per 1 percent change in ERI):
  - Aggregate effect: Log Exposure risk index: 0.06* (FR)
  - By rule type:
    - Expenditure rule (ER): 0.14*
    - Debt rule (DR): 0.10*
    - Revenue rule (RR): 0.08*
    - Balanced budget rule (BBR): 0.09*
  - Interpretation: higher magnitude for expenditure rules reflects relative ease of reallocating spending toward climate adaptation while maintaining rule strength.
  - Sample sizes and fit (selected): No. of Observations: FR 1,079; ER 1,070; DR 1,083; RR 1,078; BBR 1,086. R-Square: FR 0.64; ER 0.65; DR 0.61; RR 0.64; BBR 0.63.
- Disaggregated results by IMF country groups (selected exact coefficients):
  - Advanced Economies (AE):
    - Log Exposure risk index: -0.02*, -0.05*, -0.03*, -0.07*, -0.03* (FR, ER, DR, RR, BBR)
    - No. of Observations: 317; 316; 324; 324; 324. R-Square: 0.60; 0.61; 0.75; 0.75; 0.61.
    - Interpretation: AE coefficients negative, suggesting fiscal rules strengthened regardless of ND risk; escape clauses may partly explain.
  - Emerging Markets (EM):
    - Log Exposure risk index: 0.05*, 0.05*, 0.03*, 0.08*, 0.02* (FR, ER, DR, RR, BBR)
    - No. of Observations: 538; 423; 535; 530; 522. R-Square: 0.60; 0.59; 0.60; 0.70; 0.60.
    - Interpreted average: a 1 percent ND risk increase strengthens fiscal rules by about 0.05 percent on average (range: 0.03 percent for debt rule to 0.08 percent for revenue rule).
  - Low-income Developing Countries (LIDC):
    - Log Exposure risk index: 0.13*, 0.60*, 0.23*, 0.14*, 0.12* (FR, ER, DR, RR, BBR)
    - No. of Observations: 244; 217; 134; 254; 258. R-Square: 0.69; 0.72; 0.94; 0.62; 0.67.
    - Interpreted: fiscal rule strength increases by about 0.12 percent to 0.6 percent for a 1 percent ND risk increase.
- Disaggregated results by ERI level (selected exact coefficients):
  - High-ERI:
    - Log Exposure risk index: 0.15*, 0.21*, 0.13*, 0.08*, 0.24* (FR, ER, DR, RR, BBR)
    - No. of Observations: 245; 274; 236; 245; 293. R-Square: 0.67; 0.71; 0.67; 0.80; 0.65.
    - Interpreted: a 1 percent rise in ND risk strengthens fiscal rules by about 0.15 percent (range: 0.08 percent revenue rule to 0.24 percent budget balance rule).
  - Medium-ERI:
    - Log Exposure risk index: 0.03, 0.71*, 0.18*, 0.33*, 0.10* (FR, ER, DR, RR, BBR)
    - No. of Observations: 702; 393; 0227; 829; 298 (as reported). R-Square: 0.78; 0.97; 0.62; 0.74; 0.88.
    - Interpreted: a 1 percent rise strengthens fiscal rules by about 0.03 percent on aggregate; expenditure rule coefficient notably large in reported table.
  - Low-ERI:
    - Log Exposure risk index: 0.10*, 0.10*, 0.32*, 0.14*, 0.11* (FR, ER, DR, RR, BBR)
    - No. of Observations: 298; 298; 298; 258; 298. R-Square: 0.70; 0.84; 0.88; 0.58; 0.68.
    - Interpreted: countries with low exposure also tend to strengthen fiscal rules as ND rises (response ranges reported).
- Robustness checks:
  - Alternative risk measure (ECFRMH) from IMF-INFORM used; regression coefficients using ECFRMH are positive and statistically significant across fiscal rule indices — qualitative alignment with ERI results.
- Instrument strength diagnostics (First-stage F-statistics, All countries; reported F-statistics with p-values):
  - Log Exposure risk index: 1.83 (0.17)
  - Log Debt-to-GDP ratio: 0.35 (0.55)
  - Inflation: 0.02 (0.90)
  - Log GDP per capita: 0.03 (0.86)
  - Log Government fragmentation: 0.01 (0.93)
  - Political regime: 0.16 (0.68)
  - Log Government stability: 0.94 (0.33)
  - Log Age dependency: 0.19 (0.66)
  - Log Checks and balances: 1.26 (0.26)
  - Inflation targeting: 0.08 (0.77)
  - Monetary union: 0.42 (0.51)
  - Note: Null Hypothesis: ; p-values in parentheses (p < 0.1 rejects null hypothesis).

### V. Policy implications and recommendations
- Recalibrate rule-based fiscal frameworks to:
  - Account explicitly for natural disaster risks when designing and calibrating fiscal rules.
  - Create fiscal space and fiscal buffers that can be mobilized during emergencies.
  - Support adaptation investments and risk-aware medium-term planning.
- Strengthen legal foundations, formal enforcement mechanisms, flexibility, and supporting procedures in fiscal rule frameworks to mitigate fiscal impacts of natural disasters.
- Adopt structural, financial, and post-disaster resilience strategies.
- Consider escape clauses or emergency mechanisms within fiscal rules to address rising disaster risks effectively.
- Specific fiscal-rule design considerations:
  - (i) Explicitly cover expenditures related to natural disasters, including emergency and recovery costs.
  - (ii) Embed fiscal rules in national law to enhance stability.
  - (iii) Add robust enforcement mechanisms, such as independent fiscal councils.
  - (iv) Incorporate predefined escape clauses or adjustment mechanisms for disaster episodes.
  - (v) Establish detailed procedures for emergency budgeting, fund reallocation, and inter-agency coordination.
- Rationale: evidence suggests countries strengthen fiscal rules as natural disaster risks rise; making frameworks dynamic and adaptive can better manage macro-fiscal vulnerabilities.

### VI. Appendices and diagnostics (summary of supporting materials)
- Appendices include: Appendix 1 to Appendix 10 (country coverage, ERI distribution, explanatory variables, comparisons, descriptive statistics, evolution of indexes, scatterplots, regulatory enforcement, regression results).
- Figures referenced: Figure 1 to Figure 7 (titles and focus areas listed in source).
- Appendix highlights (selected exact items):
  - Appendix 4 compares ERI and ECFRMH (ECFRMH coverage: 2013–2021; "there is no significant changes prior to 2021 for ECFRMH index to draw meaningful analysis results").
  - Appendix 5 descriptive statistics — exact summary numbers reported in the source (see section III above).
  - Appendix 8 scatterplots use averages across 2000–2021; method note: “A data point is calculated based on average fiscal rule strength index and exposure risk index across 2000-2021. The average does not reflect the strength of the current fiscal rule.”
  - Appendix 9 finds “Approximately 60 percent of countries operating fiscal rules have medium to high exposure to natural disaster risk” and notes that “Countries with high-ERI are marked by higher debt-to-GDP ratio, while there is no outstanding characteristics in primary balance-to-GDP distribution.”
  - Appendix 10 presents first-stage F-statistics and full 2SLS coefficient tables (selected values reported above).

*IMF Working Papers — Fiscal Framework and Risk of Natural Disasters (content unit: wpiea2025038-print-pdf).*

### REFERENCES .............................................................................................................

### wpiea2025038-print-pdf - REFERENCES .............................................................................................................

### I. Introduction
- Natural disasters are described as key threats to fiscal stability, weakening public finances by increasing expenditures or reducing tax revenues and leading to rising public debt levels and downgraded sovereign credit ratings.
- Frequency of natural disasters has "quintupled" over the past 50 years; total economic losses from natural hazards estimated at approximately USD 6 trillion.
- The first three years of the 2020s alone accounted for USD 228 billion in losses (United Nations, 2021; World Bank, 2024).
- Paper focus: examine the relationship between natural disaster risks and the strength of fiscal rules, testing whether increased risks lead to stronger fiscal frameworks within the same year that natural disaster risks increased.
- Two stated contributions:
  - Extend literature by including natural disaster risks as determinants of fiscal rules (building on Badinger & Reuter, 2017a).
  - Develop a new index for measuring the strength of fiscal rules, building on IMF (2009) and Schaechter et al. (2012).

### II. Methodology and Index Construction
- Sample and period:
  - Panel data for 104 countries operating fiscal rules from 2000 to 2021.
  - Sample includes 34 advanced economies (AE), 47 emerging market economies (EM), and 23 low-income countries (LIC) as of 2021.
  - ERI classification: 36 high ERI, 26 medium ERI, and 42 low ERI countries.
- Fiscal rules strength index:
  - Derived from IMF fiscal rules database for 104 countries, 2000–2021.
  - Sub-indices by rule type: debt, budget balance, expenditure, and revenue rules.
  - Sub-indices by key characteristics: number of fiscal rules, coverage, legal basis, formal enforcement, supporting procedures, flexibility.
  - PCA used to aggregate variables into an overall fiscal rules strength index; constructed from variables: (i) enforcement score; (ii) coverage score; (ii) legal basis score; (iii) supranational rules score; (iv) index of supporting procedures for monitoring of compliance and enforcement; (v) flexibility score; (vi) average number of fiscal rules; and (vii) the ratio of national to total fiscal rules.
  - PCA weighting retains more than 80 percent of the original data variance.
  - Index standardized to have a zero mean and a standard deviation of one.
  - Noted caveat: "The fiscal rules strength index measures de jure strength and does not measure de facto adherence to fiscal rules."
- Natural disaster risk measure:
  - Exposure Risk Index (ERI) from the World Risk Index (WRI) (2023 World Risk Report by Bündnis Entwicklung Hilft).
  - WRI components: (i) exposure to natural disaster risks; and (ii) vulnerability (susceptibility, lack of coping capacities, lack of adaptive capabilities).
  - This paper uses the ERI (first component) capturing climate-related and non-climate-related disasters.
  - ERI risk categories per World Risk Report thresholds: low (0 – 0.56), medium (0.57 – 1.76), high (1.77 – 100).
- Empirical approach:
  - Panel two-stage least squares (2SLS) estimation method employed to assess impact of natural disaster risks on fiscal rules.
  - PCA and panel methods follow methodologies referenced (IMF (2009); Schaechter et al. (2012); Badinger & Reuter (2017a)).

### III. Background on Fiscal Rules (Literature and Context)
- Fiscal rules historically shown to strengthen budgetary discipline and curtail excessive deficits (Badinger & Reuter, 2017b; Neyapti, 2013; Eyraud et al., 2018).
- Budget balance rules noted as most effective in improving primary balances (Bergman et al., 2016; Enzinger, 2014).
- Fiscal rules have been reinforced after major crises: 1990s Euro area, early 2000s emerging economies, post-2008 Global Financial Crisis, and COVID-19 pandemic (Schaechter et al., 2012; Davoodi et al., 2022).
- Determinants of stricter fiscal rules (Badinger & Reuter, 2017a): greater government fragmentation, stronger interest group lobbies, weaker checks and balances, stable parliamentary regimes, monetary union membership or inflation targeting central bank, lower inflation, slower GDP growth.
- Climate change as macro-critical: global average temperatures approaching 1.5°C above pre-industrial levels; for small developing countries, climate-related investments estimated to cost 2–3% of GDP annually through 2030 (Aligishiev et al., 2022).
- Green public financial management (PFM) and green medium-term fiscal frameworks (MTFFs) advocated to integrate climate priorities into budgeting and debt objectives (Caselli et al., 2022; Caselli et al., 2024; Aydin et al., 2023).
- Akanbi et al. (2023) extends fiscal rules calibration toolkit to incorporate diverse debt-reduction timeframes, natural disaster shocks, and government climate investment profiles; recommends maintaining substantial fiscal buffers and prioritizing adaptation investments by setting lower debt limits and implementing expenditure or budget balance rules.

### IV. Data and Stylized Facts (Key Empirical Observations)
- Dataset: 104 countries with observations from 2000 to 2021; ERI and fiscal rule index aligned over same period.
- Key stylized facts (Figures 3–5 referenced):
  - (1) Rule prevalence:
    - "Budget balance and debt rules are most frequently used across all countries."
    - "Expenditure and revenue rules are implemented relatively less in LIC."
  - (2) Evolution of ERI:
    - Relative to base year 2000, increase in ERI "preponderantly in LIC."
    - Average ERI is generally high in AE but decreasing relative to base year over past two decades.
    - Average ERI increasing in EM and LIC.
    - High-ERI and low-ERI countries maintained base year risk level on average; medium-ERI countries' risk has been increasing at "0.71 percent on average."
  - (3) Evolution of fiscal rules strength:
    - Aggregate fiscal rules strength index increased rapidly after the global financial crisis; advanced economies as frontrunners.
    - Post-2008 recovery: about half of OECD countries announced medium-term fiscal plans for about three years (2010 – 2013), including fiscal rules to reduce public debt (OECD, 2011).
    - Termed the "second generation" of fiscal rules (Eyraud et al., 2018).
    - Gap in strength widened among AE, EM, and LIC since 2011 due to elevated debt in AE and further strengthening of fiscal rules.
  - (4) Convergence by ERI levels:
    - Strength of aggregate fiscal rules strength index gradually converged in recent years.
    - In 2000s, high-ERI countries adopted stricter fiscal rules (higher index from 2000 to 2013).
    - Since 2010, medium- and low-ERI countries strengthened fiscal rules faster than high-ERI countries.
    - By 2015, hierarchical ordering shifted; by 2021, indexes for all three ERI categories had converged to "approximately to 0.47."
  - (5) Europe:
    - European countries exhibit a high aggregate fiscal rule strength index despite relatively low ERI.
    - Strong European index attributed to supranational regulations by the European Union.
- Empirical results summary (from Introduction and later sections):
  - Natural disaster risks "significantly influence the design of fiscal frameworks," with countries strengthening fiscal rules as disaster risks rise.
  - Over the past two decades, fiscal rules have become more robust in response to these risks.
  - Disaggregated panel estimations suggest "additional efforts are needed to ensure natural disaster risks are comprehensively accounted for in fiscal rule design."
  - The strength of the relationship between ERI and fiscal rules is influenced by macroeconomic and institutional factors.

### V. Policy Implications and Recommendations (as presented)
- Recalibrate rule-based fiscal frameworks to:
  - Account explicitly for natural disaster risks when designing and calibrating fiscal rules.
  - Create fiscal space and fiscal buffers that can be mobilized during emergencies.
  - Support adaptation investments and risk-aware medium-term planning.
- Strengthen legal foundations, formal enforcement mechanisms, flexibility, and supporting procedures in fiscal rule frameworks to mitigate fiscal impacts of natural disasters.
- Adopt structural, financial, and post-disaster resilience strategies (Cevik and Huang, 2018; IMF, 2022).
- Consider escape clauses or emergency mechanisms within fiscal rules to address rising disaster risks effectively.
- Maintain substantial fiscal buffers and prioritize adaptation investments; set lower debt limits and implement expenditure or budget balance rules to provide greater fiscal flexibility (Akanbi et al., 2023).

### VI. Appendices and Figures (contents listed)
- Figures included: Figure 1 to Figure 7 (Titles and focus areas listed).
- Appendices included: Appendix 1 to Appendix 10 (country coverage, ERI distribution, explanatory variables, comparisons, descriptive statistics, evolution of indexes, scatterplots, regulatory enforcement, regression results).
- Note: Further detail analysis referenced in appendices (e.g., Appendix 4 and 5 for alternative ERI and expected variations; Appendix 6 for ERI development between 2000 and 2021).

*IMF Working Papers — Fiscal Framework and Risk of Natural Disasters (content unit: wpiea2025038-print-pdf - REFERENCES).*

### Appendix 7 and 8 shows development of average fiscal rules across 2000 and 2021 and scatterplots of different types of f

### wpiea2025038-print-pdf - Appendix 7 and 8 shows development of average fiscal rules across 2000 and 2021 and scatterplots of different types of fiscal rule index and ERI, respectively

### Distribution and trends of fiscal rules and exposure risk
- Regional average Exposure Risk Index (ERI):
  - Asia (APD): 16.14
  - Americas (WHD): 11.59
  - Middle East (MCD): 3.21
  - Europe (EUR): 2.46
  - Africa (AFR): 0.79
- Notable country ERI outliers mentioned:
  - APD: Japan (43.88), Indonesia (38.54), India (35.91)
  - WHD: Mexico (50.02), United States (39.81), Colombia (31.49)
- Figure descriptions (text in source):
  - Counts capture countries with fiscal rules in place historically at least once as of 2021.
  - Fiscal rules distribution by IMF classification and by ERI level (Debt rule, Expenditure rule, Revenue rule, Budget balance rule).
- Time-series indices shown (2000–2021):
  - Exposure Risk Index (average; 2000=100) displayed separately for High ERI, Medium ERI, Low ERI, and by country groupings AE, EM, LIC.
  - Aggregate Fiscal Rules Strength Index (average) displayed separately for High ERI, Medium ERI, Low ERI, and by country groupings AE, EM, LIC.
- Historical policy note:
  - EU fiscal governance reforms during 2011–12 included the "Fiscal Compact" and the "Six Pack".
  - Activation of escape clauses during the 2020 pandemic led to a modest reduction in the growth of the aggregate fiscal rule strength index for the Eurozone.

### Empirical approach and theoretical framing
- Model augmented from Badinger and Reuter (2017a) to study determinants of fiscal rules with added Exposure Risk Index to capture natural disaster risks.
- Five main theoretical rationales for fiscal rules considered:
  - (i) Common pool theory — captured by government fragmentation and age dependency.
  - (ii) Information asymmetry — captured by average education, checks and balances, government accountability.
  - (iii) Impatience and short-sightedness / political competition — captured by government stability and political regime variables.
  - (iv) Spillovers and outside pressure — captured by monetary union and inflation targeting variables.
  - (v) Macroeconomic and fiscal conditions — captured by inflation rate, debt levels, and GDP per capita (includes rising risks of natural disaster shocks).
- Trend variable included to control for temporal effects due to pronounced trends in fiscal rule index and ERI.

### Econometric specification and identification
- Structural panel model (notation from source):
  - fr_{i,t} = β1 X_{i,t} + v_i + t_i + e_{i,t}  (Equation (1))
    - fr is the fiscal rules index for country i and year t.
    - X includes: government fragmentation, age dependency, checks and balances, political regime, government stability, inflation targeting, monetary union, inflation, GDP per capita, debt level, Exposure Risk Index.
    - v_i: country fixed effect; t_i: time trend; e_{i,t}: idiosyncratic error.
- Endogeneity concerns addressed via two-stage least squares (2SLS) with an autoregressive (AR) term.
- Core instruments used (as proxies):
  - past exposure risk index of close neighbors — to proxy exposure risk index
  - working-age population — to proxy age dependency
  - political stability — to proxy government stability
  - GDP — to proxy GDP per capita
  - GDP growth — to proxy inflation
  - rule of law — to proxy political regime
  - government expenditure as a percent of GDP — to proxy public debt
  - lag of inflation targeting — to proxy inflation targeting
  - lag of monetary union — to proxy monetary union
  - government effectiveness — to proxy government fragmentation
- Reduced-form equation specified:
  - X_{i,t} = α1 X_1_{i,t} + w_i + μ_{i,t}  (Equation (2))
- Instrument diagnostics:
  - The results of the first stage regression with the F-statistics for weak instruments are shown in Appendix 10 (as noted).

### Empirical results — panel estimates and magnitudes
- Full-panel 2SLS results (entire sample):
  - Aggregate effect: a 1 percent rise in the natural disaster risk index leads to a 0.06 percent increase in the overall strength of fiscal rules.
  - By fiscal rule index, a 1 percent increase in natural disaster risks is associated with strength increases ranging:
    - Revenue rule: 0.08 percent
    - Expenditure rule: 0.14 percent
  - Interpretation: higher magnitude for expenditure rules reflects relative ease of reallocating spending toward climate adaptation while maintaining rule strength.
  - Statistical significance: coefficients of the natural disaster variable are all statistically significant at least at 10 percent level across all country groupings (see Appendix 10).
- Disaggregated panel results (by country grouping and ERI level) — mixed findings:
  - By IMF country groups (AE, EM, LIC):
    - EM and LIC tend to strengthen fiscal rules as ND risk rises.
      - EM: a 1 percent increase in ND risk strengthens fiscal rules by about 0.05 percent on average (range: 0.03 percent for debt rule to 0.08 percent for revenue rule).
      - LIC: fiscal rule strength increases by about 0.12 percent to 0.6 percent for a 1 percent ND risk increase.
    - AE: negative coefficients in AE panel groupings suggest fiscal rules are being strengthened regardless of ND risk; provision for escape clauses may partly explain this result.
      - AE panel indicates fiscal rule strength could rise as much as 0.07 percent when ND risk declines by 1 percent across most fiscal rule indices.
  - By ERI level (High-ERI, Medium-ERI, Low-ERI):
    - High-ERI: a 1 percent rise in ND risk strengthens fiscal rules by about 0.15 percent (range: 0.08 percent revenue rule to 0.24 percent budget balance rule).
    - Medium-ERI: a 1 percent rise in ND risk strengthens fiscal rules by about 0.03 percent (range: 0.1 percent budget balance rule to 0.8 percent expenditure rule).
    - Low-ERI: results indicate countries also tend to strengthen fiscal rules as ND rises (detailed coefficients in Appendix 10).

### Policy recommendations and fiscal rule design considerations
- To better manage macroeconomic and fiscal risks from natural disasters, fiscal rules could be reinforced by:
  - (i) Explicitly covering expenditures related to natural disasters, including emergency and recovery costs, for more comprehensive financial protection (Heller, 2005).
  - (ii) Strengthening the legal framework of fiscal rules by embedding them in national law to enhance stability and reduce circumvention during crises (Debrun et al., 2009).
  - (iii) Adding robust enforcement mechanisms, such as independent fiscal councils, to oversee compliance, monitor deviations, and recommend corrective actions during emergencies (Blanchard and Leigh, 2013).
  - (iv) Incorporating flexibility into fiscal rules by adding predefined escape clauses or adjustment mechanisms to allow necessary adjustments during natural disasters while maintaining long-term fiscal sustainability (Porteba and von Hagen, 1999).
  - (v) Establishing detailed procedures for emergency budgeting, fund reallocation, and inter-agency coordination for effective management during disasters (Alesina et al., 2008).
- Rationale: enhancing these aspects can help countries better manage the economic and fiscal pressures arising from natural disasters.

*IMF WORKING PAPERS — Fiscal Framework and Risk of Natural Disasters (Appendices 7–8, excerpts).*

### Appendix 9 examines if countries with higher ERI has built stronger fiscal frameworks, regardless of the push for fiscal

### wpiea2025038-print-pdf - Appendix 9 examines if countries with higher ERI has built stronger fiscal frameworks, regardless of the push for fiscal

### Key empirical setup
- Sample: 104 countries operating fiscal rules from 2000 to 2021.
- Empirical method: panel 2SLS estimation strategy.
- Statistical significance: regression results are reported as statistically significant at least at 10 percent level; intervals indicate the 95 percent confidence intervals.

### Main regression results
- Entire-panel result:
  - Natural disaster risks matter in determining fiscal frameworks for all countries; an increasing natural disaster risk broadly leads to strengthening of fiscal rules.
  - Regression charts reported for: Aggregate Index, Debt Rule, Expenditure Rule, Revenue Rule, Budget Balance Rule.
  - Response in countries with low exposure to ND risks (Low-ERI) ranges from about 0.11 percent (revenue rule) to 0.32 percent (expenditure rule).
- Disaggregated-panel result:
  - Results vary across six panel groupings (including Advanced Economies, Emerging Market Economies, Low-Income Countries, High-ND Risk, Medium-ND Risk, Low-ND Risk).
  - Mixed results imply that some countries currently operating fiscal rules will require more effort to incorporate natural disaster risks into their fiscal frameworks.

### Interpretation of determinants of fiscal rules (signs and implications)
- Common pool theory:
  - Government fragmentation: positive coefficient → more fragmented governments associated with stronger fiscal rules.
  - Age dependency (population dependence on government): negative coefficient → less dependent populations associated with stronger fiscal rules.
  - Alternative interpretations exist where coefficients reverse sign in some cases.
- Information asymmetry:
  - Generally, weaker systems of political checks and balances correlate with stronger fiscal rules (negative coefficient), but in some advanced-economy instances coefficients are positive (suggesting higher-educated citizens hold governments more accountable).
- Impatience/short-sightedness and political competition:
  - Government term length and/or political stability: positive effect on fiscal rule strength (longer political regimes associated with stronger fiscal rules).
  - Exception: in advanced economies with strong checks and balances, shorter political regimes or less stable governments may not weaken fiscal rules.
- Spillovers and outside pressure:
  - Membership in a monetary union and/or presence of inflation-targeting central bank associated with stronger fiscal rules to reduce cross-border spillovers.
- Economic and fiscal conditions:
  - Fiscal rules generally strengthened in periods of declining inflation (partially confirming IMF (2009)).
  - Many countries may strengthen fiscal rules after macroeconomic shocks (high inflation and low growth).
  - Rising government debt level triggers introduction or strengthening of fiscal rules, possibly to facilitate consolidation.

### Robustness checks
- Comparison with established literature:
  - Selected panels (Debt rule, Budget balance rule, Aggregate fiscal rule index) align with determinants in Badinger and Reuter (2017).
- Alternative risk measure:
  - IMF climate driven INFORM risk index (ECFRMH) used as alternate natural disaster risk measure.
  - Regression coefficients using ECFRMH are all positive and statistically significant across each fiscal rule index — indicating that as natural disaster risks rise (by ECFRMH), fiscal rules tend to be strengthened.
  - Note: weaknesses of ECFRMH (described in Section III and Appendix 4) could affect coefficient magnitudes, but qualitative alignment with ERI results is observed.

### Stylized facts and descriptive statistics
- Approximately 60 percent of countries operating fiscal rules have medium to high exposure to natural disaster risk.
- The aggregate fiscal rules strength index has increased over the last two decades, suggesting a strong positive correlation between fiscal rules and natural disaster exposure.

### Policy implications and recommendations
- Strengthen fiscal rules as natural disaster risks rise:
  - The evidence suggests countries should consider strengthening their fiscal rules amidst rising risk of natural disasters.
- Make fiscal frameworks dynamic and adaptive:
  - Emphasize dynamic fiscal frameworks that can adapt to evolving natural disaster risks.
  - Integrate natural disaster risk considerations into the design and calibration of fiscal frameworks to mitigate recurrent macro-fiscal vulnerabilities.
- Integrate climate policy strategically within fiscal frameworks:
  - Enhance structural, financial, and post-disaster resilience to ensure long-term sustainability of fiscal policy.
- Research agenda:
  - Possible future research could explore the impact that rising frequency of natural disasters are having on actual fiscal expenditures.

*Source: https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025038-print-pdf.pdf*

### Appendix 2. ERI and Countries Distribution by Different Groupings

### Appendix 2. ERI and Countries Distribution by Different Groupings

### ERI distribution and country groupings
- Source of ERI: Bündnis Entwicklung Hilft, World Risk Report (2022); counts capture ERI measure and country classification as of 2021.
- ERI distributions are presented by region (AFR, APD, EUR, MCD, WHD) and by income/classification groupings (IMF classification and World Bank classification). (Charts in the source show counts by ERI level—Low ERI, Medium ERI, High ERI—and counts by country group.)

### Appendix 3 — Explanatory variables (variables, description, year, source)
- Natural disaster risk
  - Exposure risk index — Extent to which populations in hazard-prone areas are exposed to and burdened by the impacts of extreme natural events of the negative consequences of climate change; 2000-2022; World Risk Index Report.
- Debt-to-GDP ratio
  - General government gross debt (% of GDP); 1980-2022; IMF-WEO.
- Inflation
  - Inflation rate, average consumer prices, percentage change; 1980-2022; IMF-WEO.
- GDP per capita
  - Real GDP per capita; 1980-2022; IMF-WEO.
- Political regime
  - Parliamentary (2), Assembly-elected President (1), Presidential (0); 1975-2020; WB-DPI.
- Government stability
  - Term length of governments (years); 1975-2020; WB-DPI.
- Age dependency
  - Population aged below 15 or above 64 (% of total); 1980-2022; WB-WDI.
- Government fragmentation
  - Fractionalization index; 1975-2020; WB-DPI.
- Information asymmetry
  - Checks and balances — Measure of the institutional constraints faced by authorities; 1975-2020; WB-DPI.
- Inflation targeting
  - 1 if central bank operates with inflation targeting, 0 otherwise; 1980-2022; Hammond (2012).
- Monetary union
  - 1 if in currency union, 0 otherwise; 2000-2022; Authors' input.

### Appendix 4 — Comparing ERI and ECFRMH (IMF climate-driven INFORM index)
- Conceptual alignment
  - The alternative exposure risk index produced by the IMF (ECFRMH) aligns with the ERI.
  - ECFRMH has three dimensions: climate-driven hazard and exposure (ECFRMH), vulnerability (ECFRMV), and lack of coping capacity (ECFRMC). The comparison uses ECFRMH.
- Temporal coverage and variation
  - ECFRMH coverage: 2013–2021 (shorter period than ERI).
  - ECFRMH shows a small degree of index variation over time; “there is no significant changes prior to 2021 for ECFRMH index to draw meaningful analysis results.”
- Empirical relationship
  - The scatterplot of average values of ERI and ECFRMH between 2013 and 2021 shows a positive correlation: “the ERI used in our model aligns with the alternative exposure risk measurement from different source.”
- Sources for comparison: Bündnis Entwicklung Hilft, World Risk Report (2022); IMF-INFORM database; Authors’ calculations.
- Note: “A single point represents the average value of ERI and ECFRMH between 2013 and 2021.”

### Appendix 5 — Descriptive statistics of Fiscal Rules Strength Index and ERI
- Key qualitative observations (verbatim):
  - “The exposure risk index (ERI) tends to be right-skewed and features notable outlier countries, whereas the fiscal rules strength index does not exhibit the same degree of skewness.”
  - “Within the ERI, middle-income countries (both Emerging Markets and Upper-Middle-Income) show a higher third quartile compared to other income groups.”
  - “Regarding the fiscal rules strength index, higher-income countries generally have both a higher mean and median compared to lower-income groups.”
  - “The computed time and cross-section standard deviation is significantly different from each other, suggesting that much of the variations in both fiscal rules and exposure risk index are not periodical.”
  - “Cross-country variations in ERI are also significantly larger than fiscal rules index.”
  - “LICs having the lowest cross-section and time variations in both fiscal rules and exposure risk indexes.”
  - “Exposure risk indexes shows several variations across cross-country and with not much time variations within country groups.”
- Selected numeric descriptive statistics (preserve exact values from source)
  - Summary statistics (Mean, Min, Q1, Median, Q3, Max) — Fiscal Rules Strength Index and Exposure Risk Index (ERI)
    - All countries
      - Fiscal Rules Strength Index: Mean 0.434; Min 0.000; Q1 0.294; Median 0.378; Q3 0.557; Max 1.000
      - Exposure Risk Index (ERI): Mean 5.776; Min 0.020; Q1 0.203; Median 0.870; Q3 3.815; Max 50.200
    - AE
      - Fiscal Rules Strength Index: Mean 0.513; Min 0.000; Q1 0.378; Median 0.523; Q3 0.652; Max 0.881
      - ERI: Mean 6.143; Min 0.020; Q1 0.180; Median 1.035; Q3 3.120; Max 44.120
    - EM
      - Fiscal Rules Strength Index: Mean 0.427; Min 0.059; Q1 0.374; Median 0.378; Q3 0.496; Max 1.000
      - ERI: Mean 7.301; Min 0.070; Q1 0.300; Median 1.200; Q3 12.240; Max 50.200
    - LIDC
      - Fiscal Rules Strength Index: Mean 0.313; Min 0.210; Q1 0.266; Median 0.266; Q3 0.378; Max 0.580
      - ERI: Mean 2.116; Min 0.070; Q1 0.120; Median 0.250; Q3 2.030; Max 27.070
    - HIC
      - Fiscal Rules Strength Index: Mean 0.503; Min 0.000; Q1 0.378; Median 0.503; Q3 0.643; Max 0.881
      - ERI: Mean 5.607; Min 0.020; Q1 0.400; Median 1.135; Q3 2.808; Max 44.120
    - UMIC
      - Fiscal Rules Strength Index: Mean 0.416; Min 0.059; Q1 0.351; Median 0.378; Q3 0.471; Max 1.000
      - ERI: Mean 7.812; Min 0.090; Q1 0.250; Median 0.830; Q3 11.568; Max 50.200
    - LIC+LMIC
      - Fiscal Rules Strength Index: Mean 0.333; Min 0.210; Q1 0.266; Median 0.294; Q3 0.378; Max 0.767
      - ERI: Mean 3.855; Min 0.070; Q1 0.130; Median 0.490; Q3 2.490; Max 36.050
    - LMIC
      - Fiscal Rules Strength Index: Mean 0.350; Min 0.210; Q1 0.266; Median 0.378; Q3 0.378; Max 0.767
      - ERI: Mean 6.426; Min 0.070; Q1 0.490; Median 2.055; Q3 5.490; Max 36.050
    - LIC
      - Fiscal Rules Strength Index: Mean 0.308; Min 0.580; Q1 0.266; Median 0.266; Q3 0.294; Max 0.001
      - ERI: Mean 0.214; Min 0.070; Q1 0.078; Median 0.160; Q3 0.233; Max 0.670
    - High ERI
      - Fiscal Rules Strength Index: Mean 0.435; Min 0.000; Q1 0.349; Median 0.405; Q3 0.571; Max 0.916
      - ERI: Mean 15.655; Min 1.840; Q1 3.148; Median 11.930; Q3 26.540; Max 50.200
    - Medium ERI
      - Fiscal Rules Strength Index: Mean 0.440; Min 0.210; Q1 0.294; Median 0.412; Q3 0.580; Max 0.804
      - ERI: Mean 1.082; Min 0.480; Q1 0.788; Median 1.050; Q3 1.460; Max 1.760
    - Low ERI
      - Fiscal Rules Strength Index: Mean 0.429; Min 0.154; Q1 0.294; Median 0.378; Q3 0.524; Max 1.000
      - ERI: Mean 0.213; Min 0.020; Q1 0.100; Median 0.160; Q3 0.250; Max 0.670
- Time and cross-section variations (preserve exact numeric strings as in source)
  - Country Groups — Cross-section variations Time variations Cross-section variations Time variations Cross-section variations Time variations
    - All countries 0.15340 0.054810.82440.03121.72000.0045
    - AE 0.17280.083011.66640.03131.58170.0020
    - EM 0.12080.055011.86090.03941.61540.0071
    - LIDC 0.06890.01285.57290.01441.53570.0029
    - HIC 0.16460.077110.59420.02861.50600.0016
    - UMIC 0.12100.060312.95500.04211.65380.0075
    - LIC+LMIC 0.09010.01518.38830.02351.66780.0057
    - LMIC 0.10480.018410.30270.03911.88020.0059
    - LIC 0.04690.01050.19240.00151.19700.0056
    - High ERI 0.16580.064613.81280.0790
    - Medium ERI 0.14240.04853.40840.0129
    - Low ERI 0.15060.05030.15340.0016
  - (Labels in source map ECFRMH (from the IMF-INFORM database), Fiscal Rules Strength Index, ERI (from the World Risk Report).)

### Appendix 6 — Evolution of average exposure index
- Evolution presented:
  - (a) by ERI level.
  - (b) by the IMF classification.
- Charts in source show temporal patterns of average ERI across groups (2000–2021 range).

### Appendix 7 — Average Fiscal Rules Strength Index across years
- Presented breakdowns:
  - (a) by region.
  - (b) by ERI level.
  - (c) by the IMF country classification.
- Charts show average fiscal rules strength index evolution across years and across the groupings above.

### Appendix 8 — Scatterplots: Fiscal rules strength types vs ERI
- Method note (verbatim): “A data point is calculated based on average fiscal rule strength index and exposure risk index across 2000-2021. The average does not reflect the strength of the current fiscal rule.”
- Panels and highlights in source:
  - (a) Aggregate Fiscal Rules Strength Index vs log(Exposure risk index)
    - Axes: log (Aggregate fiscal rules strength index) and log (Exposure risk index)
    - Regions shown: AFR, APD, EUR, MCD, WHD
  - (b) High ERI — scatterplots for Debt rule, Expenditure rule, Balanced budget rule, Revenue rule, Overall fiscal rule vs Exposure risk index (index scales and country points shown).
    - Example country points listed in source for (b): CMR, KEN, TZA, AUS, KHM, IND, IDN, JPN, MYS, NZL, THA, TLS, VNM, BEL, FRA, DEU, GRC, ITA, NLD, PRT, RUS, ESP, GBR, IRN, PAK, ARG, BRA, CAN, CHL, COL, CRI, ECU, MEX, PAN, PER, USA.
  - (c) Medium ERI — similar panels for each fiscal rule type; example country points in source: COG, CIV, GNQ, GAB, GNB, MUS, NAM, NGA, SEN, LKA, HRV, ISL, IRL, LVA, LTU, MNE, NOR, POL, ROU, SWE, CYP, GEO, ISR, ATG, BHS, DMA, JAM, URY.
  - (d) Low ERI — similar panels for each fiscal rule type; example country points in source: BEN, BWA, BFA, BDI, CPV, CAF, TCD, EST, LBR, MLI, NER, RWA, SSD, TGO, UGA, MDV, MNG, SGP, AND, AUT, BGR, CZE, DNK, FIN, HUN, LUX, MLT, SRB, SVK, SVN, CHE, ARM, AZE, TKM, GRD, PRY, KNA, LCA, VCT.
- Sources: Bündnis Entwicklung Hilft, World Risk Report (2022); IMF Fiscal Affairs Department fiscal rules database; Authors’ calculations.

### Appendix 9 — ERI and regulatory enforcement on fiscal frameworks (fiscal stance and fund participation)
- Fiscal stance assessment
  - Fiscal stances assessed with the average of debt-to-GDP and primary balance-to-GDP ratios between 2000 and 2021.
  - Empirical observation: “Countries with high-ERI are marked by higher debt-to-GDP ratio, while there is no outstanding characteristics in primary balance-to-GDP distribution.”
- Fund arrangement participation
  - “Countries with high-ERI do not necessarily participate in the fund arrangements because high-ERI countries are composed of high- and low-income level countries.”
  - Implication (verbatim): “This implies that the push for fiscal frameworks adoption is not highly relevant with strengthening of fiscal frameworks.”
- Multicollinearity check
  - “The scatterplot of ERI and fiscal indicators show that there is no observable relationship among the explanatory variables, addressing potential multicollinearity problem.”
- Visuals and notes
  - Fiscal characteristics charts present points for many countries (examples listed in source) showing Average Gross Debt to GDP and Average Exposure Risk Index and Average Primary Balance-to-GDP vs Average Exposure Risk Index for High-ERI countries only.
  - Note from source: “A single point represents the average of gross debt and cyclically-adjusted primary balance to GDP from 2000 to 2021.”
  - Additional notes:
    - 1/ “Non-participating indicates the countries that have not participated in any fund program since 2022 based on the IMF Monitoring of Fund Arrangements (MONA) database.”
    - 2/ “Several countries are missing, particularly in low-income countries, due to lack of available data.”
- Sources: Bündnis Entwicklung Hilft, World Risk Report (2022); IMF-WEO; IMF-MONA database; Authors’ calculations.

*Source: wpiea2025038-print-pdf - Appendix 2. ERI and Countries Distribution by Different Groupings (content extracted from the supplied PDF).*

### Appendix 10. Regression Results

### Appendix 10. Regression Results

### (a) First-Stage Regression (F-statistics), All Countries
- Instruments represent the following:
  - Instrument 1: Lag of exposure risk index
  - Instrument 2: Government expenditure as percent of GDP
  - Instrument 3: GDP growth
  - Instrument 4: GDP
  - Instrument 5: Government effectiveness
  - Instrument 6: Rule of law
  - Instrument 7: Political stability
  - Instrument 8: Working age population
  - Instrument 9: Government accountability
  - Instrument 10: Lag of inflation targeting
  - Instrument 11: Lag of monetary union
- reported F-statistics (with p-values in parentheses):
  - Log Exposure risk index: 1.83 (0.17)
  - Log Debt-to-GDP ratio: 0.35 (0.55)
  - Inflation: 0.02 (0.90)
  - Log GDP per capita: 0.03 (0.86)
  - Log Government fragmentation: 0.01 (0.93)
  - Political regime: 0.16 (0.68)
  - Log Government stability: 0.94 (0.33)
  - Log Age dependency: 0.19 (0.66)
  - Log Checks and balances: 1.26 (0.26)
  - Inflation targeting: 0.08 (0.77)
  - Monetary union: 0.42 (0.51)
- Note: Null Hypothesis: ; p-values are in parentheses (p < 0.1 rejects null hypothesis)

### (b) 2SLS Regression, All Countries
- Note: Dependent variables — FR, ER, DR, RR, and BBR represent overall fiscal rules, expenditure rule, debt rule, revenue rule, and balanced budget rule respectively.
- Coefficient estimates reported (asterisk indicates significance at least at 10 percent level ( p < 0.1)):
  - Log Exposure risk index: 0.06*, 0.14*, 0.10*, 0.08*, 0.09*
  - Log Debt-to-GDP ratio: 0.12*, 0.07*, 0.11*, 0.11*, 0.07*
  - Inflation: -0.03*, -0.07*, -0.05*, -0.02*, -0.02*
  - Log GDP per capita: -0.02*, -0.10*, -0.04*, -0.01*, -0.02*
  - Log Government fragmentation: 0.10*, 0.54*, 0.01*, 0.39*, 0.19*
  - Political regime: 0.03*, 0.03*, 0.05*, 0.03*, 0.00*
  - Log Government stability: 0.46*, 1.22*, 0.55*, 0.79*, 0.66*
  - Log Age dependency: -0.28*, -0.55*, -0.06*, 0.68*, -0.13*
  - Log Checks and balances: -0.05*, -0.13*, -0.10*, -0.05*, -0.01*
  - Inflation targeting: 0.12*, 0.25*, 0.16*, 0.19*, 0.13*
  - Monetary union: 0.03*, 0.33*, 0.01*, 0.10*, 0.06*
  - Trend: 0.01*, 0.03*, 0.03*, 0.02*, 0.02*
  - Constant: -2.90*, 0.27*, 2.04*, -5.15*, -2.21*
- Sample and fit:
  - No. of Observations: 1,079; 1,070; 1,083; 1,078; 1,086 (total pool observations vary slightly by dependent variable)
  - R-Square: 0.64; 0.65; 0.61; 0.64; 0.63

### (c) 2SLS Regression, by the IMF country classification (Advanced Economies, Emerging Markets, Low-income Developing Countries)
- Note: Dependent variables — FR, ER, DR, RR, and BBR represent overall fiscal rules, expenditure rule, debt rule, revenue rule, and balanced budget rule respectively.
- Advanced Economies (AE) — coefficient patterns (asterisk = p < 0.1):
  - Log Exposure risk index: -0.02*, -0.05*, -0.03*, -0.07*, -0.03*
  - Log Debt-to-GDP ratio: -0.04*, -0.13*, -0.04*, -0.10*, -0.05*
  - Inflation: -0.02*, 0.09*, 0.00, -0.01, -0.01*
  - Log GDP per capita: 0.11*, 0.12*, 0.15*, 0.22*, 0.17*
  - Log Government fragmentation: -0.18*, -0.61*, -0.13*, -0.01, -0.05
  - Political regime: -0.02*, -0.16*, -0.02*, -0.01*, -0.01
  - Log Government stability: -0.32*, -0.25*, -0.51*, -0.32*, -0.46*
  - Log Age dependency: 0.19*, 0.58*, 0.52*, 0.27*, 0.35*
  - Log Checks and balances: 0.14*, 0.18*, 0.11*, 0.04*, 0.01
  - Inflation targeting: 0.48*, 0.18*, 0.79*, 0.61*, 0.73*
  - Monetary union: 0.10*, 0.52*, 0.06*, 0.46*, 0.08*
  - Trend: 0.02*, 0.03*, 0.03*, 0.01*, 0.01*
  - Constant: 2.38*, -5.05*, -4.01*, -3.40*, -3.06*
  - No. of Observations: 317; 316; 324; 324; 324
  - R-Square: 0.60; 0.61; 0.75; 0.75; 0.61
- Emerging Markets (EM) — coefficient patterns (asterisk = p < 0.1):
  - Log Exposure risk index: 0.05*, 0.05*, 0.03*, 0.08*, 0.02*
  - Log Debt-to-GDP ratio: 0.05*, 0.22*, 0.05*, 0.17*, 0.03*
  - Inflation: 0.02*, 0.06*, 0.01*, 0.02*, 0.01*
  - Log GDP per capita: 0.01*, -0.07*, -0.01*, -0.05*, -0.03*
  - Log Government fragmentation: 0.02*, -0.03*, 0.03*, 0.15*, 0.09*
  - Political regime: -0.01, -0.11*, -0.04*, -0.03*, -0.03*
  - Log Government stability: 0.49*, 0.21*, 0.11*, 0.21*, 0.03*
  - Log Age dependency: 0.18*, -0.12*, -0.03*, 0.23*, -0.04*
  - Log Checks and balances: -0.02, -0.23*, -0.17*, -0.14*, -0.10*
  - Inflation targeting: 0.06*, 0.33*, 0.26*, 0.05*, 0.21*
  - Monetary union: 0.21*, 0.61*, 0.01, 0.74*, 0.17*
  - Trend: 0.01*, 0.02*, 0.01*, 0.01*, 0.01*
  - Constant: -2.45*, -1.45*, -1.30*, -2.33*, 0.57*
  - No. of Observations: 538; 423; 535; 530; 522
  - R-Square: 0.60; 0.59; 0.60; 0.70; 0.60
- Low-income Developing Countries (LIDC) — coefficient patterns (asterisk = p < 0.1):
  - Log Exposure risk index: 0.13*, 0.60*, 0.23*, 0.14*, 0.12*
  - Log Debt-to-GDP ratio: -0.04*, -0.25*, -0.19*, 0.07*, -0.04*
  - Inflation: -0.01, -0.01, 0.02, 0.02, 0.02*
  - Log GDP per capita: 0.01*, 0.25*, -0.06*, 0.28*, 0.01*
  - Log Government fragmentation: 0.18*, 1.02*, 0.21*, 0.34*, 0.15*
  - Political regime: 0.19*, 0.23*, 
  - Log Government stability: 0.18*, 0.60*, -0.08*, 0.59*, 0.12*
  - Log Age dependency: 1.16*, 0.72*, 1.02*, 0.89*, 1.11*
  - Log Checks and balances: 0.08*, 0.43*, 0.05*, 0.19*, -0.06*
  - Inflation targeting: (coefficients reported for LIDC in the table)
  - Monetary union: 0.24*, 1.04*, 0.24*, 0.46*, 0.26*
  - Trend: 0.01*, 0.02*, 0.01*, 0.00, 0.01*
  - Constant: -6.32*, -30.48*, -5.07*, -18.02*, -5.99*
  - No. of Observations: 244; 217; 134; 254; 258
  - R-Square: 0.69; 0.72; 0.94; 0.62; 0.67

### (d) 2SLS Regression, by ERI level (High, Medium, Low Exposure Risk Index)
- Note: Dependent variables — FR, ER, DR, RR, and BBR represent overall fiscal rules, expenditure rule, debt rule, revenue rule, and balanced budget rule respectively.
- High Exposure Risk Index — coefficients and sample metrics:
  - Log Exposure risk index: 0.15*, 0.21*, 0.13*, 0.08*, 0.24*
  - Log Debt-to-GDP ratio: 0.11*, -0.05*, -0.04*, 0.11*, -0.08*
  - Inflation: -0.02*, 0.01*, -0.03*, -0.01*, 0.02*
  - Log GDP per capita: 0.01*, -0.12*, -0.01*, -0.05*, -0.05*
  - Log Government fragmentation: 0.26*, 0.51*, 0.54*, 0.39*, 0.26*
  - Political regime: 0.10*, -0.04, 0.25*, 0.08*, 0.18*
  - Log Government stability: 0.13*, 0.08*, 0.56*, 0.04*, 0.45*
  - Log Age dependency: 0.66*, -1.10*, 0.84*, -0.41*, -0.11*
  - Log Checks and balances: -0.06*, -0.24*, -0.12*, -0.15*, -0.24*
  - Inflation targeting: 0.22*, 0.23*, 0.04, 0.04, 0.16*
  - Monetary union: 0.15*, 0.54*, 0.15*, 0.41*, 0.29*
  - Trend: 0.01*, 0.01*, 0.01*, 0.01*, 0.01*
  - Constant: -4.12*, 4.47*, 4.55*, 0.75*, -1.19*
  - No. of Observations: 245; 274; 236; 245; 293
  - R-Square: 0.67; 0.71; 0.67; 0.80; 0.65
- Medium Exposure Risk Index — coefficients and sample metrics:
  - Log Exposure risk index: 0.03, 0.71*, 0.18*, 0.33*, 0.10*
  - Log Debt-to-GDP ratio: 0.13*, 0.34*, 0.13*, 0.24*, 0.15*
  - Inflation: -0.04*, 0.00, 0.11*, -0.02*, -0.02*
  - Log GDP per capita: -0.08*, -0.26*, -0.17*, -0.16*, -0.13*
  - Log Government fragmentation: 0.06*, -0.38*, 0.01, 0.35*, 0.07*
  - Political regime: 0.06*, 0.13*, 0.88*, 0.10*, 0.06*
  - Log Government stability: -0.23*, 0.08*, 0.90*, -0.16*, -0.34*
  - Log Age dependency: -0.43*, 0.39*, 0.05, 0.05, -0.59*
  - Log Checks and balances: -0.15*, -0.07*, -1.98*, -0.30*, -0.25*
  - Inflation targeting: 0.08*, 0.31*, 0.20*, 0.29*, 0.22*
  - Monetary union: 0.04*, 0.57*, 0.04*, 0.12*, 0.01*
  - Trend: 0.01*, 0.02*, 0.06*, 0.02*, 0.02*
  - Constant: 2.37*, -0.52, -6.68*, 0.99*, 3.56*
  - No. of Observations: 702; 393; 0227; 829; 298 (as reported)
  - R-Square: 0.78; 0.97; 0.62; 0.74; 0.88
- Low Exposure Risk Index — coefficients and sample metrics:
  - Log Exposure risk index: 0.10*, 0.10*, 0.32*, 0.14*, 0.11*
  - Log Debt-to-GDP ratio: 0.10*, 0.32*, 0.14*, 0.11*, 0.11*
  - Inflation: -0.01, 0.04*, 0.02, -0.15*, -0.02*
  - Log GDP per capita: 0.01*, 0.03*, -0.07*, -0.03*, 0.05*
  - Log Government fragmentation: 0.09*, 0.31*, 0.03*, 0.83*, -0.33*
  - Political regime: -0.01*, 0.02, 0.02, 0.05, -0.12*
  - Log Government stability: 0.28*, 0.58*, -0.22*, 0.80*, 0.52*
  - Log Age dependency: 0.34*, -0.21*, -0.73*, 1.53*, 0.18*
  - Log Checks and balances: -0.03, -0.29*, 0.13*, -0.26*, -0.10*
  - Inflation targeting: 0.27*, 0.20*, 0.65*, 0.45*, 0.30*
  - Monetary union: 0.10*, 0.58*, 0.19*, 0.71*, 0.08*
  - Trend: 0.01*, 0.01*, 0.01*, 0.02*, 0.02*
  - Constant: -3.15*, -1.54*, 2.34*, -6.72*, -3.21*
  - No. of Observations: 298; 298; 298; 258; 298
  - R-Square: 0.70; 0.84; 0.88; 0.58; 0.68

* = Significant at least at 10 percent level ( p < 0.1)

*Source: Appendix 10, Regression Results, Working Paper No. WP/2025/038 — Strengthening Fiscal Frameworks in the Presence of Rising Risk of Natural Disasters*

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_Source: https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025038-print-pdf.pdf_