## sipea2026004

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---

### A. Introduction and Context
- The Philippines is highly exposed to natural hazards intensified by climate change, including stronger typhoons, heavier rainfall, higher temperatures, and rising sea levels.
- World Bank estimates: cumulative economic costs of climate change could reach 7.6 percent of GDP by 2030 and 13.6 percent by 2040 (World Bank CCDR 2022).
- The Philippines has integrated climate resilience into the Development Plan (PDP 2023-2028) and National Adaptation Plan (NAP 2023-2050), aligned with UN SDG 13 targets 13.1 and 13.2.
- World Risk Index (2025): the Philippines ranked as the most at-risk country worldwide for the 15th consecutive year, with a score of 46.56 out of 100.
- This Selected Issues Paper extends previous empirical research by incorporating additional climate variables to quantify macroeconomic costs and analyze monetary and fiscal policy trade-offs, with a focus on typhoons.

### B. Climate Trends and Shocks in the Philippines
- Temperature and precipitation
  - Average increase of approximately 1.1°C with respect to 1901-1930, most of which recorded after 1960.
  - Country-wide precipitation has remained historically stable and is projected to remain largely unchanged in most of the country, with a modest but not statistically significant increase in the north (+150mm/year).
- Sea level and typhoons
  - Sea level has continued to rise historically and is projected to continue increasing.
  - The country experiences, on average, the most intense Category-5 typhoons twice per year, while Categories 3 and 4 occur more frequently.
  - A single Category-5 typhoon can inflict losses equivalent to about 0.1 percent of GDP in the agriculture sector alone.
  - Geographic concentration of exposure: seven regions have more than 15 percent probability of experiencing Category-5 typhoons in a year, while four regions face more than 25 percent likelihood.

### C. Macroeconomic Impact of Typhoons (Empirical Evidence)
- Methodology and identification
  - Impact of typhoons estimated using local projection methods following Jorda et al (2005), using regional macroeconomic data (17 regions, 2000–2022) and typhoon data from the National Disaster Risk Reduction and Management Council (NDRRMC).
  - Lasso regressions indicate typhoons are the most statistically relevant climate variable in explaining GDP per capita growth.
- Key empirical impacts of Category-5 typhoons
  - Inflation
    - Headline CPI increases by around 0.4 percent, with the peak impact approximately one quarter after the typhoon.
    - Food CPI increases by around 0.7 percent, with the peak impact approximately one quarter after the typhoon.
    - Inflationary pressure concentrated in food and agriculture-related components; impacts on other sectors’ inflation are limited.
  - Output and productivity
    - Regional real GDP falls by approximately 0.4 percent on impact.
    - Agricultural labor productivity declines (regional estimates report magnitudes used in model calibration).
  - Aggregate GDP impact
    - Average aggregate GDP impact of typhoons approximated at around 0.2-0.3 percent of GDP.
    - Regional impact calibration: 0.4 percent regional impact × 2/17 (average fraction of regions impacted by a category-5 typhoon), scaled for persistence, frequency, and smaller-category typhoons yields the aggregate 0.2-0.3 percent estimate.
  - Sectoral labor productivity impacts (calibration):
    - Utilities, Mining, Manufacturing and Transportation: around 2 percent.
    - Construction: around 4 percent.
  - Typhoons act similarly to supply shocks: raise inflation while reducing economic activity.

### D. Long-term Climate Change Costs and Adaptation
- High-warming SSP3-7.0 scenario: average temperatures 3.1°C above pre-industrial levels.
- Gradual warming under SSP3-7.0 can reduce real GDP growth, resulting in a 1 to 5 percent real GDP loss in 2100 compared to a scenario in which temperature continues increasing following present trends.
- Sea-level rise costs and adaptation
  - Without adaptation: costs as large as 1.8 percent annually.
  - CIAM simulations with optimal protection and retreat: reduce costs by as much as 95 percent, from 2.1 to 0.1 percent of GDP, annually.
- Adaptation timing assumptions
  - Assumes the Philippines will need 30 years to adapt to the new temperature level.
  - Adaptation in 20 years allows to reduce losses by 50% but without any adaptation losses would double.

### E. Monetary Policy Implications and Institutional Responses
- Monetary policy challenges
  - Severe climate events lower GDP and raise prices, challenging achievement of the Bangko Sentral ng Pilipinas (BSP) inflation target.
  - A year with multiple, consecutive, and strong typhoons can become macro-critical.
  - Trade-offs arise between anchoring inflation expectations and supporting post-disaster recovery.
- BSP institutional responses since 2020
  - Integrating climate and environmental factors into macroeconomic models and monetary policy analysis.
  - Capacity building for staff in climate risk modeling.
  - Publication of the Philippine Sustainable Finance Taxonomy Guidelines.
  - Inclusion of climate considerations into the BSP Systemic Risk Crisis Management framework.
  - Institutionalizing requirements for banks to conduct climate scenarios and stress-testing.
- Policy priority
  - Anchor inflation expectations (especially when food prices are volatile) while ensuring financial conditions remain supportive of investment and capital rebuilding.
  - Address food price salience through clear communication and credible policy actions to reduce salience bias and foster more rational, broad-based expectation formation.

### F. Model, Calibration, and Scenarios (IMF GDN and DIGNAD)
- IMF Global Dynamic Network (GDN) model features
  - 16-sector, 2-country model calibrated to the Philippines and the rest of the world using Philippine Statistics Authority IO tables and the 2018 GLORIA MIRO database.
  - Investment adjustment costs; two types of capital: agriculture-specific and other non-agriculture capital.
  - Intermediate inputs difficult to replace across goods.
  - Sectoral pricing frictions: agricultural goods prices more flexible and volatile; IT and communications less responsive.
  - Slow adjustment of inflation beliefs and disproportionate focus on food prices in expectations (“food price salience”).
- Calibration details
  - Productivity shocks assumed AR(1) with persistence parameter of 0.4.
  - Shock calibration matches empirical estimates of a typical year of typhoons using capital destruction and productivity shocks and NDRRMC damage data.
  - Downside “bad year” scenario scales baseline by ~3-to-1 ratio comparing a category-5 causing more damage than 90 percent of observed typhoons.
  - Monetary policy reaction function (baseline rule similar to BSP’s PAMPh) preserved exactly as in source:
    - i_t = 0.7 × i_{t−1} + 0.3 × ( r̄ + C C C C C C_t t t r t t t t t + 2.5 × ( C C C C C C C I I I I I I_t t t i I I I I_t t+3 − C C C C C C_t t t r t t t t t t ) + 0.3 × O O O O_t t O O O O_t t G G_t_t O O )

- DIGNAD model (Debt-Investment-Growth and Natural Disasters)
  - Simulates natural disaster shocks in a dynamic small open economy, introducing shocks to public and private capital, productivity, and public investment efficiency during reconstruction.
  - Adaptation infrastructure is more expensive but yields higher returns and lower depreciation than standard infrastructure.
  - Integrates investment-growth nexus, fiscal adjustment, and private sector response.

### G. Results: Baseline vs Downside Scenarios
- Baseline: single typical category-5 typhoon — limited macroeconomic impact.
- Downside scenario (year with multiple severe typhoons)
  - Quarterly GDP falling by 0.25 percent.
  - Quarter-on-quarter inflation rising by 0.4 percentage points (1.7 percentage points annualized).
  - Composition of initial GDP decline: consumption about one-third; net exports two-thirds (imports rise temporarily; exports fall more sharply and persistently).
  - After first year: decline shifts towards investment and consumption with lower contribution from net exports.
  - Nominal interest rates rise, but real interest rates fall by 50 basis points (bps) in the first year, providing temporary stimulus.
  - Food price spikes have disproportionate impact on inflation expectations due to salience, producing a lasting impact on expectations and inflation despite temporary price shocks.
- Drivers and sensitivity
  - In the downside scenario about 50 percent of the CPI response and most of the RGDP response stem from agricultural capital losses rather than productivity declines.
  - Reducing persistence of productivity shocks to zero leaves simulations largely unchanged.
  - Food price salience in inflation expectations amplifies persistence of inflation and weakens the economy.

### H. Sectoral Dynamics
- Construction experiences the largest decline in labor productivity, followed by mining (per calibrated shocks).
- Agriculture and mining are most flexible-price sectors; prices increase immediately after climate impacts.
- Agriculture: production contracts and prices rise due to agricultural capital destruction.
- Other sectors: substitution away from agriculture can increase demand and production; mining receives a large boost in demand and prices rise considerably because it is upstream and pricing is flexible.

### I. Fiscal Adaptation Spending, Financing, and DIGNAD Policy Simulations
- Historical and programmed spending
  - Actual spending on climate adaptation averaged 1.7 percent of GDP annually during 2022-2024.
  - Historical climate change expenditure: 1.0 percent of GDP during 2015-2021.
  - Programmed climate change adaptation expenditure for 2025 amounts to 3.9 percent of GDP; proposed spending for 2026 is projected at 2.7 percent of GDP.
  - Philippines CCDR (World Bank, 2022) estimates cost of new climate-resilient infrastructure at about 0.6 percent of GDP (conservative; excludes retrofitting).
- Trade-offs and financing constraints
  - Relying solely on borrowing could lead to higher public debt and complicate consolidation under the Medium-Term Fiscal Framework.
  - If resilient infrastructure investment is undertaken without additional debt, policies required may include revenue mobilization and/or expenditure rationalization.
  - A model-based approach is recommended to analyze trade-offs and implications for growth and debt sustainability.
- DIGNAD calibrated steady state values (In percent)
  - Trend per capita growth rate in absence of natural disasters: 5.0
  - Imports to GDP ratio: 35.8
  - Remittances to GDP ratio: 8.8
  - Consumption tax (VAT) rate: 12.0
  - Labor income tax rate: 24.4
  - Public domestic debt to GDP ratio: 41.3
  - Public concessional debt to GDP ratio: 8.7
  - Public external commercial debt to GDP ratio: 10.7
  - Private external debt to GDP: 11.3
  - Public investment to GDP ratio: 5.9
  - Cost ratio adaptation vs standard investment: 25.0
  - Return to standard infrastructure: 30.0
  - Return to adaptation infrastructure: 50.0
  - Depreciation rate of standard public infrastructure: 7.5
  - Depreciation rate of resilient public infrastructure: 3.0
  - Public investment efficiency: 53.0
- Active policy simulations (resilient investment = 0.3 percent of GDP)
  - Financing scenarios
    - Government borrowing: domestic to external borrowing ratio 80 to 20 percent.
    - Tax reform: mobilize revenue to fund resilient investments.
    - Rationalizing spending: reallocate within existing expenditure.
  - Simulation outcomes (qualitative)
    - Recurrent typhoons reduce GDP and deteriorate fiscal position under BAU.
    - Active resilient investment can raise output above BAU over time.
    - Government borrowing increases public debt-to-GDP and crowds out private investment.
    - Tax reform yields the most favorable growth outcomes without increasing debt, though it dampens consumption initially.
    - Rationalizing spending yields GDP effects similar to borrowing scenario but preserves private investment better.
    - Across scenarios, resilient infrastructure can offset recurrent shocks over time; financing choice matters for debt and private investment.

### J. Policy Conclusions and Recommendations
- Whole-of-government response is needed to address the structural macroeconomic risk from climate change.
- Monetary policy
  - Trade-offs between anchoring inflation expectations and supporting post-disaster recovery are central.
  - A policy stance that supports rebuilding while maintaining credibility in inflation expectations is most effective.
  - BSP’s PAMPh rule is a useful starting point with data-dependent adjustments as shocks dissipate and inflation persistence becomes clearer.
  - Clear communication to counteract food price salience is important.
- Fiscal policy
  - Investment in climate-resilient infrastructure mitigates macro impacts; resilient investment entails higher upfront fiscal costs but higher long-term returns.
  - Creating fiscal space through stronger revenue mobilization and more efficient spending is critical to finance resilience without increasing public debt.
  - Mobilizing private investment through public-private partnerships could be considered (not modeled).
  - Enhancing green public financial management in line with Public Investment Management Assessment Update and Climate PIMA recommendations will improve effectiveness and accountability.

### K. Annex highlights: methods and model validation
- Local projections method
  - Region- and sector-level data (17 regions, 2000–2022) used to estimate typhoon impacts on CPI, GDP, Consumption, Investment, labor productivity, and employment.
  - NDRRMC provides data on cost of damages by cyclone intensity (categories 1–5); event dates from EM-DAT.
  - Results robust to clustering standard errors at the regional level except for agricultural GDP impact.
- IMF GDN model calibration and validation
  - Price adjustment frequencies estimated by matching peak inflation responses to a 100-basis point monetary policy shock between Jorda local projections and the GDN model.
  - Shock calibration matches economic losses in agriculture and infrastructure and value-added in 6 sectors.
  - Example calibrated shock values (In percent):
    - Typical Category 5: Damages in Agriculture/GDP 0.2583, Damages in Infrastructure/GDP 0.0771.
    - Bad Year: Damages in Agriculture/GDP 0.7900, Damages in Infrastructure/GDP 0.2357.
  - Model performance closely matches data estimates for value-added per worker and damage shares.

*Source: sipea2026004 (Selected Issues Paper, Philippines, International Monetary Fund).*

### References ____________________________________________________________________________ 36

### sipea2026004 - References

### A. Introduction and Context
- The Philippines is highly exposed to natural hazards intensified by climate change, including stronger typhoons, heavier rainfall, higher temperatures, and rising sea levels.
- World Bank estimates: cumulative economic costs of climate change could reach 7.6 percent of GDP by 2030 and 13.6 percent by 2040 (World Bank CCDR 2022).
- The Philippines has integrated climate resilience into the Development Plan (PDP 2023-2028) and National Adaptation Plan (NAP 2023-2050), aligned with UN SDG 13 targets 13.1 and 13.2.
- World Risk Index (2025): the Philippines ranked as the most at-risk country worldwide for the 15th consecutive year, with a score of 46.56 out of 100.
- This Selected Issues Paper extends previous empirical research (including Armas et al. 2024) by incorporating additional climate variables to quantify macroeconomic costs and to analyze monetary and fiscal policy trade-offs, with a focus on typhoons.

### B. Climate Trends and Shocks in the Philippines
- Historical temperature change:
  - Average increase of approximately 1.1°C with respect to 1901-1930, most of which recorded after 1960.
- Precipitation:
  - Country-wide precipitation has remained historically stable and is projected to remain largely unchanged in most of the country, with a modest but not statistically significant increase in the north (+150mm/year).
- Sea level:
  - Sea level has continued to rise historically and is projected to continue increasing.
- Typhoon frequency and concentration:
  - The country experiences, on average, the most intense Category-5 typhoons twice per year, while Categories 3 and 4 occur more frequently.
  - A single Category-5 typhoon can inflict losses equivalent to about 0.1 percent of GDP in the agriculture sector alone.
  - Geographic concentration of exposure: seven regions have more than 15 percent probability of experiencing Category-5 typhoons in a year, while four regions face more than 25 percent likelihood.

### C. Macroeconomic Impact of Climate Shocks and Trends
- Methodology:
  - Impact of typhoons estimated using local projection methods following Jorda et al (2005), using regional macroeconomic data from the Philippine Statistics Authority and typhoon data from the National Disaster Risk Reduction and Management Council.
  - Regional analysis increases sample size and statistical significance relative to country-level macro data.
  - Lasso regressions indicate typhoons are the most statistically relevant climate variable in explaining GDP per capita growth, motivating the focus on typhoons.
- Impacts of Category-5 typhoons:
  - Inflation:
    - Headline CPI increases by around 0.4 percent, with the peak impact approximately one quarter after the typhoon.
    - Food CPI increases by around 0.7 percent, with the peak impact approximately one quarter after the typhoon.
    - Inflationary pressure is concentrated in food and agriculture-related components; impacts on other sectors’ inflation are limited.
  - Output and productivity:
    - Regional real GDP falls by approximately 0.4 percent on impact.
    - Agricultural labor productivity declines (magnitude reported in regional results; specific numeric decline beyond description of decline is provided in regional estimates).
- Additional notes:
  - Typhoons act similarly to supply shocks, creating inflationary pressure while reducing economic activity.
  - Annex I provides technical details on data and regression specifications.

*Source: sipea2026004 - References (Selected Issues Paper, Philippines, International Monetary Fund).*

### 2.5 percent.

### 2.5 percent.

### Impacts of Typhoons on GDP and Productivity
- Average aggregate GDP impact of typhoons approximated at around 0.2-0.3 percent of GDP.
- Local projections indicate typhoons act as adverse supply shocks, likely raising inflation while dampening economic activity.
- Regional impact calibration:
  - 0.4 percent regional impact multiplied by 2/17 (average fraction of regions impacted by a category-5 typhoon), scaled up to account for persistence, frequency and impact of category-1 to category-4 typhoons yields the aggregate estimate of 0.2-0.3 percent.
- Sectoral labor productivity impacts (used to calibrate the model in Section D):
  - Utilities, Mining, Manufacturing and Transportation: around 2 percent.
  - Construction: around 4 percent.
- Comparison: Estimated aggregate impact is slightly smaller than Hallegatte et al (2022) which finds a once-in-ten-year typhoon reduces GDP by around 1 percent in the Philippines.

### Long-term Climate Change Costs and Adaptation
- High-warming SSP3-7.0 scenario: average temperatures 3.1°C above pre-industrial levels.
- Gradual warming under SSP3-7.0 can reduce real GDP growth, resulting in a 1 to 5 percent real GDP loss in 2100 compared to a scenario in which temperature continues increasing following present trends.
- Sea-level rise costs:
  - Without adaptation: costs as large as 1.8 percent annually.
  - CIAM simulations with optimal protection and retreat: reduce costs by as much as 95 percent, from 2.1 to 0.1 percent of GDP, annually.
- Adaptation timing assumptions:
  - Assumes the Philippines will need 30 years to adapt to the new temperature level.
  - Adaptation in 20 years allows to reduce losses by 50% but without any adaptation losses would double.

### Monetary Policy Implications and Institutional Responses
- Severe climate events have important implications for monetary policy given impacts on inflation and output; events challenge achieving the Bangko Sentral ng Pilipinas (BSP) inflation target.
- Since 2020 BSP actions include:
  - Integrating climate and environmental factors into macroeconomic models and monetary policy analysis.
  - Capacity building for staff in climate risk modeling.
  - Publication of the Philippine Sustainable Finance Taxonomy Guidelines.
  - Inclusion of climate considerations into the BSP Systemic Risk Crisis Management framework.
  - Institutionalizing requirements for banks to conduct climate scenarios and stress-testing.
- Key finding: a year with multiple, consecutive, and strong typhoons can become macro-critical by lowering GDP and raising prices; tradeoffs between anchoring inflation expectations and supporting post-disaster recovery are central.

### Model, Calibration, and Scenarios
- Model: IMF’s Global Dynamic Network (GDN) model — 16-sector, 2-country model calibrated to the Philippines and the rest of the world using Philippine Statistics Authority IO tables and the 2018 GLORIA MIRO database.
- Modeling features:
  - Investment adjustment costs for each type of capital, with two types of capital assumed: agriculture-specific capital and other non-agriculture capital.
  - Intermediate inputs difficult to replace across goods.
  - Sectoral pricing frictions: agricultural goods prices more flexible and volatile; IT and communications less responsive.
  - Slow adjustment of inflation beliefs and disproportionate focus on food prices in expectations (“food price salience”).
- Calibration approach:
  - Match empirical estimates of a typical year of typhoons (including a category-5 typhoon) using capital destruction and productivity shocks.
  - Use National Disaster Risk Reduction and Management Council data for damages.
  - Productivity shocks assumed to be an AR(1) process with persistence parameter of 0.4.
  - Downside “bad year” scenario scales baseline by ~3-to-1 ratio comparing a category-5 causing more damage than 90 percent of observed typhoons (e.g., Super Typhoon Pablo in 2012) to the average category-5 typhoon.
- Monetary policy reaction function (baseline rule similar to BSP’s PAMPh):
  - i_t = 0.7 × i_{t−1} + 0.3 × ( r̄ + C C C C C C_t t t r t t t t t + 2.5 × ( C C C C C C C I I I I I I_t t t i I I I I_t t+3 − C C C C C C_t t t r t t t t t t ) + 0.3 × O O O O_t t O O O O_t t G G_t_t O O )
  - where i_t is the nominal interest rate and r̄ is the neutral rate of interest.
  - (Equation preserved exactly as in source.)

### Results: Baseline vs Downside Scenarios
- Single typical category-5 typhoon (baseline): limited macroeconomic impact.
- Downside scenario (year with multiple severe typhoons):
  - Quarterly GDP falling by 0.25 percent.
  - Quarter-on-quarter inflation rising by 0.4 percentage points (1.7 percentage points annualized).
  - Initial GDP decline composition: consumption about one-third; net exports two-thirds (imports rise temporarily to compensate for destroyed capital and food; exports fall more sharply and persistently).
  - After first year: decline shifts towards investment and consumption with lower contribution from net exports.
  - Nominal interest rates rise in downside scenario, but real interest rates fall by 50 basis points (bps) in the first year, providing temporary stimulus.
  - Food price spikes have disproportionate impact on inflation expectations due to salience, producing a lasting impact on expectations and inflation despite temporary price shocks.

### Sectoral Dynamics
- Construction experiences the largest decline in labor productivity, followed by mining (per calibrated shocks).
- Agriculture and mining are most flexible-price sectors; prices increase immediately after climate impacts.
- Agriculture: production contracts and prices rise due to agricultural capital destruction.
- Other sectors: substitution away from agriculture can increase demand and production; mining receives a large boost in demand and prices rise considerably because it is upstream and pricing is flexible.

### Drivers and Sensitivity
- In the downside scenario about 50 percent of the CPI response and most of the RGDP response stem from agricultural capital losses rather than productivity declines.
- Reducing persistence of productivity shocks to zero leaves simulations largely unchanged, indicating productivity shock persistence is not a key driver.
- Food price salience in inflation expectations has an important impact: linking slow-moving inflation expectations to food prices leads to lasting beliefs of future inflation from a temporary food price rise, making inflation more persistent and weakening the economy.

### Monetary Policy Trade-offs and Counterfactuals
- Three monetary policy rules simulated under the downside scenario:
  - (i) BSP’s PAMPh rule (blue);
  - (ii) strict CPI targeting where CPI inflation is always kept at target (green);
  - (iii) “dovish” rule with a one-quarter delay in tightening (red).
- Outcomes:
  - PAMPh and dovish rules support activity and rebuilding because real interest rates fall on impact; output remains above potential GDP while CPI inflation remains elevated.
  - Strict CPI targeting neutralizes the typhoon’s impact on inflation but leads to a large decline in real GDP and a negative output gap.
- Conclusion: a monetary policy reaction function that supports rebuilding while maintaining credibility to keep expectations anchored is the most appropriate; PAMPh rule is a useful starting point with data-dependent adjustments as shocks dissipate and inflation persistence becomes clearer.

### Policy Implications and Recommendations
- Monetary policy:
  - A year with multiple severe typhoons can create trade-offs between anchoring inflation expectations and supporting reconstruction.
  - Policy priority: anchor inflation expectations (especially when food prices are volatile) while ensuring financial conditions remain supportive of investment and capital rebuilding.
  - Balanced approach recommended: support rebuilding while maintaining credibility to keep expectations anchored; use PAMPh as starting framework with data-dependent adjustments.
  - Address food price salience through clear communication and credible policy actions to reduce salience bias and foster more rational, broad-based expectation formation.
- Fiscal and adaptation policy:
  - Philippine Development Plan (2023-2028), National Climate Change Action Plan (NCCAP) (2011-2028), and National Adaptation Plan (NAP) (2023-2050) shape adaptation and resilience priorities.
  - Financial Sector Forum produced Philippine Sustainable Finance Taxonomy Guidelines (SFTG).
  - Climate Change Expenditure Tagging (CCET) since 2015 monitors execution of NCCAP priorities.
  - Actual spending on climate adaptation averaged 1.7 percent of GDP annually during 2022-2024.

*Source: sipea2026004 - 2.5 percent.*

### 1.0 percent of GDP during 2015-2021. Moreover, programmed climate change adaptation

### sipea2026004 - 1.0 percent of GDP during 2015-2021. Moreover, programmed climate change adaptation

### Climate adaptation spending and priorities
- Programmed climate change adaptation expenditure for 2025 amounts to 3.9 percent of GDP, while proposed spending for 2026 is projected at 2.7 percent of GDP.
- Main strategic priorities under climate adaptation expenditure include water sufficiency, sustainable energy, and climate smart industries and services.
- The Philippines Country Climate and Development Report (CCDR) (World Bank, 2022) estimates the cost of new climate-resilient infrastructure at about 0.6 percent of GDP; this estimate is described as conservative because it does not account for retrofitting existing infrastructure.
- Historical climate change expenditure noted: 1.0 percent of GDP during 2015-2021.

### Trade-offs and financing constraints
- Enhancing resilience to natural disasters entails trade-offs: relying solely on borrowing could lead to higher public debt and complicate consolidation under the Medium-Term Fiscal Framework.
- If resilient infrastructure investment is undertaken without additional debt, policies required may include revenue mobilization and/or expenditure rationalization.
- A model-based approach is recommended to analyze trade-offs and implications for growth and debt sustainability under different policy options.

### Model-based scenario analysis (DIGNAD)
- The Debt-Investment-Growth and Natural Disasters (DIGNAD) model simulates natural disaster shocks in a dynamic small open economy framework, introducing shocks to public and private capital, productivity, and public investment efficiency during reconstruction.
- Adaptation infrastructure in the model is more expensive but yields higher returns and has lower depreciation than standard infrastructure.
- Model integrates multiple channels: investment-growth nexus, fiscal adjustment, and private sector response.

### Calibrated parameters and steady state values (In percent)
- Trend per capita growth rate in absence of natural disasters: 5.0
- Imports to GDP ratio: 35.8
- Remittances to GDP ratio: 8.8
- Consumption tax (VAT) rate: 12.0
- Labor income tax rate: 24.4
- Public domestic debt to GDP ratio: 41.3
- Public concessional debt to GDP ratio: 8.7
- Public external commercial debt to GDP ratio: 10.7
- Private external debt to GDP: 11.3
- Public investment to GDP ratio: 5.9
- Cost ratio adaptation vs standard investment (i.e., adaptation is % expensive than standard): 25.0
- Return to standard infrastructure: 30.0
- Return to adaptation infrastructure: 50.0
- Depreciation rate of standard public infrastructure: 7.5
- Depreciation rate of resilient public infrastructure: 3.0
- Public investment efficiency: 53.0

### Baseline natural disaster scenario and calibration
- Baseline assumes continuous, recurring shocks each year of average magnitude that destroy public and private capital and reduce total factor productivity (TFP), producing a permanent downward level shift in GDP relative to a no-shock counterfactual.
- Recurrent typhoons are calibrated to reduce output by 0.1 percent of GDP every year, with persistent effects of about 4 years.
- Output impacts stem from public and private capital destruction, productivity shocks, and reduced public investment efficiency.
- Typhoon fiscal impacts: lower revenue due to output loss, higher spending for emergency response and reconstruction.

### Active policy simulations (resilient investment = 0.3 percent of GDP)
- Active scenario assumption: government invests in resilient infrastructure set at 0.3 percent of GDP (half the World Bank 2022 suggestion).
- Three financing scenarios under the active case:
  - Government borrowing scenario: financing via borrowing with a domestic to external borrowing ratio of 80 to 20 percent.
  - Tax reform scenario: tax reforms create fiscal space to fund resilient investments.
  - Rationalizing spending scenario: finance through expenditure prioritization and shifting composition toward resilient infrastructure while keeping total expenditure unchanged.
- Comparison baseline: passive “business-as-usual” (BAU) where the government does not increase adaptation investment despite recurring shocks.

### Simulation results (qualitative summary of Figure 10 and model outcomes)
- Recurrent typhoons reduce GDP relative to the steady state baseline with no climate shocks; fiscal position deteriorates (lower revenues, higher reconstruction spending, and lower denominator for debt-to-GDP).
- In active scenarios, investment in resilient infrastructure can lead output to rise above the BAU scenario over time, as benefits of resilient investment exceed negative impacts of typhoons.
- Government borrowing scenario:
  - Positive growth effect is less pronounced due to crowding-out of private investment.
  - Public debt-to-GDP ratio increases over time relative to BAU.
- Tax reform scenario:
  - Largest initial impact on consumption but yields relatively more favorable overall impact on growth.
  - Mobilizing revenue yields a smaller multiplier effect than cuts in public spending.
  - Dampens private consumption but has a relatively benign effect on private investment.
  - Public debt-to-GDP ratio decreases because resilience investment no longer requires borrowing.
- Rationalizing spending scenario:
  - Similar impact on GDP as the government borrowing scenario.
  - Private investment is higher than in borrowing scenario due to absence of crowding-out, offset by lower current public spending.
- Across scenarios, resilient infrastructure can offset the impact of recurrent climate shocks on GDP over time.
- Relying solely on borrowing to finance resilient investment could increase the debt burden over time.
- Revenue mobilization helps achieve a higher level of GDP since it does not crowd out private investment and other public spending.

### Conclusions and policy implications
- Climate change is a structural macroeconomic risk for the Philippines due to high exposure to natural disasters; a whole-of-government response is needed.
- Monetary policy implications:
  - Trade-offs exist between anchoring inflation expectations and supporting post-disaster recovery.
  - Sectoral models like the GDN are useful to capture supply-chain propagation and sector-specific shocks.
  - A policy stance supporting rebuilding while maintaining credibility in inflation expectations is most effective; BSP’s inflation-targeting framework, supported by clear public communication and data-dependent adjustments, is deemed well-suited.
- Fiscal policy implications:
  - Investment in climate-resilient infrastructure mitigates macro impacts of disasters; resilient investment entails higher upfront fiscal costs but higher long-term returns (mitigating productivity losses, safeguarding capital, sustaining growth).
  - Creating fiscal space through stronger revenue mobilization and more efficient spending is critical to finance resilience without increasing public debt.
  - Mobilizing private investment through public-private partnership modalities could be an option (not modeled).
  - Enhancing green public financial management in line with recommendations of the Public Investment Management Assessment Update and Climate PIMA will improve effectiveness and accountability of adaptive investment.

### Annex highlights: methods and model validation
- Local projections method:
  - Region- and sector-level data (17 regions, 2000–2022) used to estimate typhoon impacts on CPI, GDP, Consumption, Investment, labor productivity, and employment.
  - NDRRMC provides data on cost of damages by cyclone intensity (categories 1–5); event dates from EM-DAT.
  - Local projections specification provided; results robust to clustering standard errors at the regional level except for agricultural GDP impact.
- IMF GDN model calibration and validation:
  - Price adjustment frequencies estimated by matching peak inflation responses to a 100-basis point monetary policy shock between Jorda local projections and the GDN model.
  - Shock calibration matches economic losses in agriculture and infrastructure and value-added in 6 sectors.
  - Tables report sector-level model vs data responses and shock sizes for a typical Category 5 typhoon and a bad year of typhoons (see Tables I.2, I.3, I.4 in source).
  - Examples of calibrated shock values (In percent):
    - Typical Category 5: Damages in Agriculture/GDP 0.2583, Damages in Infrastructure/GDP 0.0771.
    - Bad Year: Damages in Agriculture/GDP 0.7900, Damages in Infrastructure/GDP 0.2357.
  - Model performance closely matches data estimates for value-added per worker and damage shares in reported tables.

*https://www.imf.org/-/media/files/publications/selected-issues-papers/2026/english/sipea2026004.pdf*

### References

### References

### Tools, Manuals, and Technical Reports
- Aligishiev, Z., C. Ruane, and A. Sultanov, 2023, "User Manual for the DIGNAD Toolkit," IMF TechnicalNotes and Manuals 2023/03, International Monetary Fund, Washington, DC.
- Dakila Jr, F.G., Bautista, D.M., Dacio, J.E., Amodia, R.A., Castañares, S.J.A., Alhambra, P.R.R., Ocampo, J.C.G., Marquez, C.J.P., Romaraog, M.R.S, Karam, M.P.D. and Baksa, D., 2024. A Monetary and Financial Policy Analysis and Forecasting Model for the Philippines (PAMPh2. 0). International Monetary Fund.
- G.K. Ozhan, N. Sander, S. Wende, and S. Yang, 2025, “Monetary Policy under Network-Level Bottlenecks.” Technical Report.
- Massetti, E. and F. Tagklis, 2024, FADCP Climate Dataset: Temperature and Precipitation. Reference Guide, Fiscal Affairs Department, International Monetary Fund, Washington DC.

### IMF Working Papers, Country Reports, and Related Analyses
- Buffie, E.F., A. Berg, C. Pattillo, R. Portillo, and L. F. Zanna, 2012, “Public investment, growth, and debt sustainability: putting together the pieces,” IMF Working Paper 12/144.
- Cantelmo, Alessandro, Leo Bonato, Giovanni Melina, and Gonzalo Salinas, 2019, “Policy Trade-offs in Building Resilience to Natural Disasters: The Case of St. Lucia,” IMF Working Paper 19/54.
- Cantelmo, Alessandro, Giovanni Melina, and Chris Papageorgiou, 2019, “Macroeconomic Outcomes in Disaster-Prone Countries,” IMF Working Paper 19/217.
- Hallegatte, Stephane, Fabian Lipinsky, Paola Morales, Hiroko Oura, Nicola Ranger, Martijn Gert Jan Regelink and Henk Jan Reinders, 2022, “Bank Stress Testing of Physical Risks under Climate Change Macro Scenarios: Typhoon Risks to the Philippines,” IMF Working Paper 22/163.
- Melina, Giovanni, and Marika Santoro, 2021, “Enhancing Resilience to Climate Change in the Maldives,” IMF Working Paper 21/96.
- International Monetary Fund, 2023, ““Building Resilience to Natural Disasters and Climate Change: A Model Application,” Philippines: Selected Issues, Country Report No. 2023/415.
- International Monetary Fund, 2024, “Chapter 2: The Great Tightening: Insights from the Recent Inflation Episode.” World Economic Outlook October 2024.

### Philippines-Focused Studies, Reports, and Central Bank Publications
- Armas, J.C, R.J. Asi, D.R. Mandap, and G.R. Moral., 2024, Macroeconomic effects of temperature shocks in the Philippines: Evidence from impulse responses by local projections. Bangko Sentral ng Pilipinas Discussion Paper Series No. 2024-02. BSP DP202402.pdf
- Bangko Sentral ng Pilipinas. Open letters to the President of the Philippines. Bangko Sentral ng Pilipinas Price Stability - Open Letter to the President.
- Bangko Sentral ng Pilipinas, 2023, Sustainability Report.
- Mapa, C., Bunyi, M., Arcin, A., and Fuentes, E., 2024, “Dissecting consumer attention: Insights on consumers' inflation expectations in the Philippines.” Bangko Sentral ng Pilipinas (BSP) Discussion Paper, No. 2024-15.
- PAGASA, 2023, Annual Climate Bulletin. Manila, Philippines: Philippine Atmospheric, Geophysical and Astronomical Services Administration. http://dx.doi.org/10.13140/RG.2.2.27167.57768.
- United Nations, 2023, Waiting for the ‘Big One’: Natural hazards in the Philippines. UN Resident Coordinator Blog.
- World Bank, 2022, “Philippines: Country Climate and Development Report”.
- World Bank, 2023), Climate Change Knowledge Porta for Development Practitioners and Policy Makers. Philippines -  Climatology | Climate Change Knowledge Portal.

### Climate Datasets, Observational Products, and Methodological Sources
- Copernicus Climate Change Service, Climate Data Store, 2021: CMIP6 climate projections.
- Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c
- Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N., 2023, ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47
- Harris I, Osborn TJ, Jones P and Lister D, 2020, Version 4 of the CRU TS Monthly High-Resolution Gridded Multivariate Climate Dataset. Scientific Data (https://doi.org/10.1038/s41597-020-0453-3)
- Bündnis Entwicklung Hilft and Ruhr University Bochum – Institute for International Law of Peace and Armed Conflict (IFHV), 2025, WorldRiskReport 2025: Focus – Floods.

### Academic Articles and Journal Contributions
- Marto, R., C. Papageorgiou, and V. Klyuev, 2018, “Building Resilience to Natural Disasters: An Application to Small Developing States,” Journal of Development Economics, Vol. 135, pp. 574–586. https://doi.org/10.1016/j.jdeveco.2018.08.008.
- E. Pasten, R. Schoenle, and M. Weber, 2020, “The propagation of monetary policy shocks in a heterogeneous production economy”, Journal of Monetary Economics, vol. 116, Pages 1-22.
- Hallegatte and coauthors (see IMF Working Paper 22/163 entry above) — topical overlap with typhoon risk stress testing methods and macro scenario analysis.

*Source: sipea2026004 - References (PDF) provided by the International Monetary Fund*

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_Source: https://www.imf.org/-/media/files/publications/selected-issues-papers/2026/english/sipea2026004.pdf_