## 25. Multi-Pillar Framework: Strenthening ex ante and ex post Resilience

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---

### I. Introduction and motivation
- Pacific island countries (PICs) are among the most susceptible to natural disasters (earthquakes, cyclones, tsunamis, hurricanes, floods) and climate change risks, with location and small size heightening vulnerability.
- This study is the first IMF cross-country quantification of the impact of natural disasters on Pacific islands’ economies (previous IMF analyses were country-by-country).
- Integrating prospective fiscal and growth costs of natural disasters into macro frameworks and DSAs can:
  - Determine ex-ante the magnitude of fiscal and financial buffers required.
  - Inform the fiscal space available for disaster- and climate-related infrastructure.
  - Help tailor IMF policy advice.
- The paper presents a multi-pillar strategy involving national, regional, and multilateral responses, including IMF engagement, to strengthen ex-ante and ex-post resilience.

### II. Stylized facts
- Occurrence and frequency:
  - The region experienced about 2,400 tropical cyclones in the last 60 years (World Bank Group, 2013a).
  - PICs have suffered more natural disasters over the last four decades than small states in other regions.
- Probability:
  - Based on historical frequency, the probability of a natural disaster averages more than 20 percent a year across the small Pacific states and Papua New Guinea (PNG).
  - Joint probability of disasters hitting more than one PIC at the same time is generally below 5 percent, with a maximum of 12 percent (Fiji and PNG).
- World Risk Index:
  - Among 171 countries, 6 Pacific islands rank among the first 16 countries at highest risk; Vanuatu is ranked first.
  - Pacific island countries average: 19 (World Risk Report 2014); Non-small states average: 7; Small states (excl. PICs) average: 8.
- Damage and losses:
  - Annual damage and losses averaged 2.3 percent of GDP in the Pacific islands during 1980-2014.
  - PIC small states mean: 2.3 (percent of GDP); Non-small states mean: 0.3; Small states (excl. PICs) mean: 1.7.
  - Damage and losses in percent of GDP averaged (median) 9.5 percent of GDP for extreme events (selected examples shown).
  - Example vulnerability: a rise of 50cm in sea level would lead to a loss of 80 percent of the land in the Majuro Atoll of Marshall Islands.
- Interaction with macro volatility:
  - Natural disasters contribute to higher revenue volatility and GDP volatility in PICs relative to peers.
  - Intensity measure used: intensity = [(number of deaths + 0.33*number of people affected)/population]*100.

### III. Macroeconomic impact — framing and empirical summary
- Macroeconomic consequences:
  - Natural disasters destroy or damage infrastructure and capital, creating macroeconomic volatility, reducing growth prospects, and typically worsening fiscal positions.
  - Natural disasters often expand public debt via more borrowing due to lower revenues or increased spending.
- Empirical literature and other estimates:
  - Fomby and others (2013): negative effects on growth from droughts and storms; no significant aggregate GDP effect from earthquakes.
  - Raddatz (2009): small countries hurt more by windstorms, helped by moderate floods.
  - Loayza and others (2009): small disasters may boost short-run growth via reconstruction, but large disasters have negative short-run growth effects.
  - World Bank Group: natural disasters in the PICs cause damage, every year on average, of nearly 2 percent of GDP (about US$248 million).
  - ADB: climate change economic costs for the Pacific islands of 2.2-3½ percent of GDP annually, rising to as high as 12.7 percent by 2100; preparing for climate effects may cost between 1½-2½ percent of GDP a year.

### IV. Estimating macro-fiscal impacts — methods and core results
- Methodologies:
  - Panel vector autoregression (VAR) model for short-term impact on growth and fiscal aggregates (revenue, expenditure, fiscal balance).
  - Panel autoregressive distributed lag (ARDL) model for long-term effects on GDP growth.
  - Event analysis for growth and fiscal performance during and after disasters.
- Data:
  - Natural disasters include earthquakes, storms, floods, droughts, using EM-DAT (CRED). Panel data are unbalanced.
- VAR model — short-term impacts (panel for Fiji, Samoa, Solomon Islands, Tonga, Vanuatu; 1970-2013):
  - Disaster intensity: intensity = [(number of deaths + 0.33*number of people affected)/population]*100.
  - Identification: disasters treated as exogenous; fiscal variables first-differenced; Choleski decomposition; lag = one (robustness checks with two and three lags).
  - Key short-term impulse-response findings:
    - A natural disaster with intensity affecting 1 percent of the population causes:
      - Real GDP growth to contract by about 0.5 percentage point in the year of the disaster.
      - Tax revenue to fall by 0.2 percentage point of GDP in the year of the disaster, then rise by 0.2 percentage point of GDP in the following year.
      - Government spending to rise by 0.7 percentage point of GDP in the year of the shock.
      - Fiscal balance to deteriorate by 0.5 percent of GDP in the year after the disaster.
    - Using damage and losses equal to 1 percent of GDP as the disaster variable:
      - GDP growth drops by 0.7 percentage point in the year of the disaster.
      - The average annual drop on historical data equates to 2.1 percent for all Pacific islands (based on historical damage and losses).
  - Role of grants:
    - Fiscal deterioration is mitigated somewhat by grants in affected PICs.
  - Robustness:
    - Results hold under alternative disaster intensity definitions (damage and losses percent of GDP), alternative lag specifications, and inclusion of global shocks (real world GDP growth and changes in oil prices).

- ARDL model — long-term impacts (panel for Fiji, Papua New Guinea, Samoa, Tonga, Vanuatu; 1970-2014):
  - Specification: dependent = log real GDP; explanatory = population (log), capital stock (log), damage and losses (percent of GDP). Capital stock via perpetual inventory method.
  - Key long-run findings:
    - Damage and losses coefficient: -0.003 (damage and losses in percent of GDP). Because real GDP is in log and damage in percent, the coefficient is interpreted multiplicatively (see event analysis).
    - For damage and losses equal to 1 percent of GDP, growth in the Pacific islands falls on average by 0.3 percentage point over 10 years.
    - Actual damage and losses during 1980-2014 reduced trend growth by 0.7 percentage point.
    - Given actual average growth during 1980-2014 averaged 2.6 percent, the average growth would have been 3.3 percent without natural disasters.
    - Example scenario: if pre-disaster GDP grows at 3 percent, 10-year cumulative growth = 34 percent. For damage and losses equal to 60 percent of GDP, growth falls by 18 percentage points (60 * 0.3), yielding a 10-year cumulative growth loss of 16 percent.
  - Appendix D, Table 1 (Fixed Effects Estimates of the Long-Run Effects):
    - Capital stock (log): 0.344***
    - Population (log): 0.628***
    - Damage and losses (percent of GDP): -0.003**
    - F-statistics for cointegration: 4.851***
    - Observations: 225
    - Number of countries: 5
    - Estimation period: 1970-2014.

- Event analysis — definition and results:
  - Episode defined as damage and losses of at least 10 percent of GDP (1970-2013); median damage 26 percent of GDP.
  - Key findings:
    - Coefficient of damage and losses as percent of GDP is -0.003 (real GDP in log; damage in percent of GDP). The coefficient is multiplied by 100 for interpretation in percent terms.
    - Illustration: damage and losses of 60 percent of GDP → 60 * 0.3 = 18 (i.e., a 18 percentage-point cumulative 10-year growth loss).
    - Average growth outcomes (Table 2 illustration):
      - Annual GDP growth (percent): Before natural disaster 3.0; After natural disaster 1.3; Difference 1.7
      - Cumulative GDP growth over 10 years (percent): Before natural disaster 34.0; After natural disaster 16.0; Difference 18.0
    - Main finding: a loss in output relative to the pre-disaster GDP growth trend persists after two years, with the fiscal balance remaining as negative as in the year of the disaster.
    - For the events analyzed, growth was zero in the year of the disaster on average; growth rebounds fairly quickly but remains below pre-disaster growth on average two years after the disaster.
    - Fiscal balance deterioration remains large after the event, consistent with VAR results.

### V. Policy implications and multi-pillar strategy highlights
- Purpose of integrating disaster risks into macro frameworks and DSAs:
  - Inform the size of fiscal/financial buffers and contingency financing.
  - Guide priorities for investments in disaster resilience and climate adaptation.
- Multi-pillar strategy components:
  - Ex-ante resilience:
    - identify and undertake risk assessment; explicitly integrate risks into fiscal frameworks and budget planning.
    - provide self-insurance by building policy buffers (lower debt, higher fiscal balances and reserves).
    - reduce risks by enhancing preparedness, investing in “smart” infrastructure and enhancing debt-management capacity.
    - transfer risk through private or sovereign insurance and multilateral risk-sharing mechanisms (e.g., international safety net).
  - Ex-post resilience (coping and recovery):
    - timely access to financing (grants, concessional lending, insurance mechanisms).
    - prioritized reconstruction spending with risk-reducing measures (e.g., resettlement away from coastline where feasible).
    - reconstruction as opportunity to accelerate growth-enhancing structural reforms.
- Examples and mechanisms:
  - Pacific Catastrophe Risk Insurance Pilot:
    - Launched January 2013; concluded second year pilot phase in October 2014; pilot began third phase in November 2014 and is now expected to be concluded in October 2015.
    - Current participating countries: the Cook Islands, the Marshall Islands, Samoa, Tonga, and Vanuatu.
    - Government of Japan provided an additional US$1 million to fund premium subsidies for four participating countries (Cook Islands self-funding).
    - January 2014 payouts: Tonga received US$1.3 million; Vanuatu received US$1.9 million after Cyclone Pam. Damages and losses were US$45.4 million in Tonga and US$467 million in Vanuatu.
  - Membership in multilateral organizations as ex ante pooling of global resources (risk-pooling mechanism).
  - Regional discussions: Melanesian Spearhead Group (MSG) discussing an emergency Stabilization Fund for balance of payments crises.
- Financing context:
  - Donor financing remains important; building buffers has opportunity costs; participation in insurance mechanisms promising but disbursements limited.
  - Global climate finance target noted: raising US$100 billion a year by 2020 (established at 2010 United Nations Climate Change Conference in Cancun).
  - Access to global funding for PICs is challenging because of capacity constraints; bilateral funding remains critical; donor coordination should be strengthened given limited administrative capacity.

### VI. The role of the IMF — surveillance, financing, and capacity development
- Surveillance — integration and policy messages:
  - Integrate natural disaster risks into macro frameworks to determine the magnitude of buffers (or self-insurance) needed and of outside insurance.
  - Allow sufficient flexibility to help redeploy spending rapidly.
  - Ensure sufficient policy space (external reserves and low debt) to mitigate potential balance of payments shortfalls.
  - After an event: greater transparency for disaster assistance, strengthen coordination among development partners and authorities, pursue growth-enhancing structural reforms.
  - Include costs of natural disasters and climate change in debt sustainability analysis and scenario analysis in Article IV consultations (examples cited: Kiribati, Tonga, Samoa).
- IMF financial support instruments and cases:
  - Rapid Credit Facility (RCF): rapid financial support in a single, up-front payout for low-income countries; financing under the RCF carries a zero interest rate through 2016, has a grace period of 5½ years, and a final maturity of 10 years.
  - Rapid Financing Instrument (RFI): similar to RCF; financing terms same as Flexible Credit Line, Precautionary and Liquidity Line and Stand-By Arrangements; repayment within 3¼ to 5 years.
  - Samoa cases:
    - Tropical Cyclone Evan (December 2012): damage estimated at 30 percent of GDP. One-off disbursement of US$8.6 million under RCF approved May 2013.
    - 2009 tsunami: damage and losses estimated at 25 percent of GDP; IMF financial assistance equivalent to US$9.3 million provided.
  - Vanuatu case:
    - Devastating cyclone of March 2015: IMF Board approved disbursement of about US$11.9 million under the RCF and disbursement of about US$11.9 million under the RFI.
    - Disbursements intended to help cope with immediate balance-of-payments needs and catalyze donor support.
  - IMF Catastrophe Containment and Relief (CCR) Trust:
    - Transformed from PCDR Trust in February 2015.
    - Allows the Fund to join international debt relief efforts when poor countries are hit by catastrophic natural disasters or public health disasters.
    - CCR Trust available to 38 low-income countries eligible for concessional borrowing through the PRGT and which also have either a per capita income below US$1,215—or, for small states, a population below 1.5 million and a per capita income below US$2,430. Among the PICs, only Solomon Islands meet these criteria.
    - A country qualifies if hit by a disaster that directly affects at least one third of the population and destroys more than a quarter of the country’s productive capacity (or caused by damage exceeding100 percent of GDP).
  - Augmentation of an existing program: when a country with an IMF-supported program is hit by a natural disaster, augmented financing under the existing program could provide additional support; an IMF program can catalyze international assistance even without augmentation (example: Solomon Islands, 2014).
- Capacity development and technical assistance:
  - TA on public financial management (PFM) improves budget planning and transparency, helps incorporate disaster risks into fiscal planning.
  - IMF supports PFM reform and contributes to Public Expenditure and Financial Accountability (PEFA) assessments.
  - IMF provides TA for environmental tax reforms to get energy prices to reflect harmful environmental side-effects.
  - Pacific Financial Technical Assistance Centre (PFTAC) and World Bank DRFI Program delivered a regional workshop (March 2015) addressing:
    - special budgetary procedures for providing rapid access to emergency funding,
    - the macroeconomic and fiscal impact of natural disasters,
    - incorporating disasters risks into the fiscal planning process, and
    - disaster risk financing.

### VII. Policy recommendations (from source)
- Explicitly recognize costs of natural disasters and climate change in baseline macro-frameworks and debt sustainability analyses.
- Build policy buffers (lower debt, higher fiscal balances and reserves) to enhance ex ante resilience, while acknowledging opportunity costs and limits for small states.
- Enhance preparedness by investing in “smart” infrastructure and improving debt-management capacity.
- Pursue risk transfer mechanisms: private/sovereign insurance and multilateral risk-pooling arrangements.
- Strengthen donor coordination and improve access to global climate finance by addressing capacity constraints.
- Use reconstruction as an opportunity for growth-enhancing structural reforms.
- Continue IMF support via surveillance, financial instruments (RCF, RFI, CCR Trust, program augmentation), and capacity development (PFM, TA, training).

### VIII. Conclusions (source summary)
- Pacific island countries face severe challenges from natural disasters and climate change, impacting both potential growth and public finances.
- A more strategic approach is needed to address increasing frequency and magnitude of events.
- Explicitly incorporating disaster and climate costs into macro frameworks and DSAs is important.
- Building policy buffers is particularly relevant for small Pacific states, but external assistance and insurance schemes will remain necessary.
- The IMF can help through continued technical assistance and training to enhance countries’ risk management capacities and foster ex ante resilience.

*Source: IMF staff study — "25. Multi-Pillar Framework: Strenthening ex ante and ex post Resilience" (content from _wp15125).*

### Appendix A: Data Sources................................................................................................

### Appendix A: Data Sources

### Appendix listings
- Appendix A: Data Sources...................................................................................................... 24
- Appendix B: Determinants of real revenue ............................................................................ 25
- Appendix C: VAR  Model ...................................................................................................... 26
- Appendix D: Autoregressive distributed lag (ARDL) model ................................................. 30
- Appendix E.Selected Programs and Funds ............................................................................. 31
- References ............................................................................................................................... 36

### List of Figures
- 1. Average Number of Natural Disasters per Yer .................................................................6
- 2. Pacific Island Countries: Number of Natural Disasters per Yer .......................................6
- 3. Pacific Island Countries: Joint Probablity of Occurrence of a Natural Disaster ...............7
- 4. Probability of Occurence of Natural Disasters ..................................................................7
- 5. Natural Disaster Risks – World Risk Index, 2014 ............................................................7
- 6. Natural Disaster Risks – World Risk Index, 2014, Top 16 ...............................................7
- 7. Damage and Losses of Extreme Damage Events, Top 25 ................................................8
- 8. Average Annual Damage and Losses ................................................................................8
- 9. GDP volatility and Intnesity of Natural Disasters 1990-2013 ..........................................9
- 10. Fiscal Balance and Intnesity of Natural Disasters 1990-2013 ........................................9
- 11. Volatilty of Revenue (excl. Grants) ..............................................................................10
- 12. Revenue Volatilty and Intnesity of Natural Disasters 1990-2013 .................................10
- 13. Pacific Island Countries: International Reserves, March 2015 .....................................10
- 14. Pacific Island Countries: Public and Publicly Guranteed Debt, 2014 ...........................10
- 15. Pacific Island Countries: Fiscal Balance, 2014 .............................................................10
- 16. Pacific Island Countries: Headline Inflation, March 2015 ............................................10
- 17. Pacific Island Countries: Short-Run Impact Natural Disasters  on  GDP Growth ........13
- 18. Response of Growth and Fiscal Aggregates to a Natural Disaster with Intensity 
  Equivalent to 1 Percent of the Population Affected........................................................14
- 19. Response of Growth and Fiscal Aggregates to 1 Percent of GDP Damage Shock .......14
- 20. Pacific Island Countries: Long-Run Impact of Natural Disasters on Trend GDP ........15
- 21. Pacific Island Countries: Long-Run Impact of Natural Disasters on Trend Growth ....15
- 22. Pacific Island Countries: Long-Run Impact of Natural Disasters on Trend Growth ....15
- 23. Real GDP Growth Before and After Natural Disasters— Event Analysis ...................16
- 24. Fiscal Balance Before and After Natural Disasters— Event Analysis .........................16

*Source: Appendix A: Data Sources (page listings and List of Figures) from the supplied PDF content.*

### 25. Multi-Pillar Framework: Strenthening ex ante and ex post Resilience .........................18

### 25. Multi-Pillar Framework: Strenthening ex ante and ex post Resilience

### I. Introduction and motivation
- Pacific island countries (PICs) are among the most susceptible to natural disasters (earthquakes, cyclones, tsunamis, hurricanes, floods) and climate change risks, with location and small size heightening vulnerability.
- This study is the first IMF cross-country quantification of the impact of natural disasters on Pacific islands’ economies (previous IMF analyses were country-by-country).
- Integrating prospective fiscal and growth costs of natural disasters into macro frameworks and DSAs can:
  - Determine ex-ante the magnitude of fiscal and financial buffers required.
  - Inform the fiscal space available for disaster- and climate-related infrastructure.
  - Help tailor IMF policy advice.
- The paper presents a multi-pillar strategy involving national, regional, and multilateral responses, including IMF engagement, to strengthen ex-ante and ex-post resilience.

### II. Stylized facts
- Occurrence and frequency:
  - The region experienced about 2,400 tropical cyclones in the last 60 years (World Bank Group, 2013a).
  - PICs have suffered more natural disasters over the last four decades than small states in other regions.
- Probability:
  - Based on historical frequency, the probability of a natural disaster averages more than 20 percent a year across the small Pacific states and Papua New Guinea (PNG).
  - Joint probability of disasters hitting more than one PIC at the same time is generally below 5 percent, with a maximum of 12 percent (Fiji and PNG).
- World Risk Index:
  - Among 171 countries, 6 Pacific islands rank among the first 16 countries at highest risk; Vanuatu is ranked first.
  - Pacific island countries average: 19 (World Risk Report 2014); Non-small states average: 7; Small states (excl. PICs) average: 8.
- Damage and losses:
  - Annual damage and losses averaged 2.3 percent of GDP in the Pacific islands during 1980-2014.
  - PIC small states mean: 2.3 (percent of GDP); Non-small states mean: 0.3; Small states (excl. PICs) mean: 1.7.
  - Damage and losses in percent of GDP averaged (median) 9.5 percent of GDP for extreme events (selected examples shown).
  - Example vulnerability: a rise of 50cm in sea level would lead to a loss of 80 percent of the land in the Majuro Atoll of Marshall Islands.
- Interaction with macro volatility:
  - Natural disasters contribute to higher revenue volatility and GDP volatility in PICs relative to peers.
  - Figures illustrate relationships between intensity of disasters (intensity = [(number of deaths + 0.33*number of people affected)/population]*100) and GDP volatility, and fiscal balance.

### III. Macroeconomic impact of natural disasters — framing and empirical summary
- Macroeconomic consequences:
  - Natural disasters destroy or damage infrastructure and capital, creating macroeconomic volatility, reducing growth prospects, and typically worsening fiscal positions.
  - Natural disasters often expand public debt via more borrowing due to lower revenues or increased spending.
- Empirical literature highlights:
  - Fomby and others (2013): negative effects on growth from droughts and storms; no significant aggregate GDP effect from earthquakes.
  - Raddatz (2009): small countries hurt more by windstorms, helped by moderate floods.
  - Loayza and others (2009): small disasters may boost short-run growth via reconstruction, but large disasters have negative short-run growth effects.
- Other estimates:
  - World Bank Group: natural disasters in the PICs cause damage, every year on average, of nearly 2 percent of GDP (about US$248 million).
  - ADB: climate change economic costs for the Pacific islands of 2.2-3½ percent of GDP annually, rising to as high as 12.7 percent by 2100; preparing for climate effects may cost between 1½-2½ percent of GDP a year.

### IV. Estimating the macro-fiscal impact of natural disasters — methods and core results
- Methodologies used:
  - Panel vector autoregression (VAR) model for short-term impact on growth and fiscal aggregates (revenue, expenditure, fiscal balance).
  - Panel autoregressive distributed lag (ARDL) model for long-term effects on GDP growth.
  - Event analysis for growth and fiscal performance during and after disasters.
- Data:
  - Natural disasters include earthquakes, storms, floods, droughts, using EM-DAT (CRED). Panel data used varies by model and is unbalanced due to data limitations.

Subsection: VAR model — short-term impacts (panel for Fiji, Samoa, Solomon Islands, Tonga, Vanuatu; 1970-2013)
- Disaster intensity definition: intensity = [(number of deaths + 0.33*number of people affected)/population]*100.
- Identification: natural disasters are treated as exogenous; fiscal variables first-differenced to ensure stationarity; Choleski decomposition used; lag structure set to one (robustness checks with two and three lags reported).
- Key short-term findings (impulse responses):
  - A natural disaster with intensity affecting 1 percent of the population causes:
    - Real GDP growth to contract by about 0.5 percentage point in the year of the disaster.
    - Tax revenue to fall by 0.2 percentage point of GDP in the year of the disaster, then rise by 0.2 percentage point of GDP in the following year.
    - Government spending to rise by 0.7 percentage point of GDP in the year of the shock.
    - Fiscal balance to deteriorate by 0.5 percent of GDP in the year after the disaster.
  - Using damage and losses equal to 1 percent of GDP as the disaster variable:
    - GDP growth drops by 0.7 percentage point in the year of the disaster.
    - The average annual drop on historical data equates to 2.1 percent for all Pacific islands (based on historical damage and losses).
- Role of grants:
  - The fiscal deterioration is mitigated somewhat by grants in affected PICs.
- Robustness:
  - Results hold under alternative disaster intensity definitions (damage and losses percent of GDP), alternative lag specifications, and inclusion of global shocks (real world GDP growth and changes in oil prices).

Subsection: ARDL model — long-term impacts (panel for Fiji, Papua New Guinea, Samoa, Tonga, Vanuatu; 1970-2014)
- Specification: dependent variable = log real GDP; explanatory variables = population (log), capital stock (log), damage and losses (percent of GDP). Capital stock constructed via perpetual inventory method.
- Key long-run findings:
  - For damage and losses equal to 1 percent of GDP, growth in the Pacific islands falls on average by 0.3 percentage point over 10 years.
  - Actual damage and losses during 1980-2014 reduced trend growth by 0.7 percentage point.
  - Given actual average growth during 1980-2014 averaged 2.6 percent, the average growth would have been 3.3 percent without natural disasters.
  - Example scenario: if pre-disaster GDP grows at 3 percent, 10-year cumulative growth = 34 percent. For damage and losses equal to 60 percent of GDP, growth falls by 18 percentage points (60 * 0.3), yielding a 10-year cumulative growth loss of 16 percent.
- Quantified average estimated losses:
  - Average estimated GDP losses for 1980-2014: 0.7 percentage point (trend growth impact).

### V. Policy implications and multi-pillar strategy highlights
- Need to mainstream disaster and climate-change prospective costs into macro frameworks and DSAs to:
  - Inform the size of fiscal/financial buffers and contingency financing.
  - Guide priorities for investments in disaster resilience and climate adaptation.
- Multi-pillar strategy (national, regional, multilateral, including IMF engagement) can provide a strategic, less ad hoc approach to:
  - Strengthen ex-ante resilience: build buffers, invest in risk-reducing infrastructure, adopt forward-looking adaptation strategies, climate-proof infrastructure, improve risk-based planning and capacity.
  - Strengthen ex-post resilience: timely access to financing (grants, concessional lending, insurance mechanisms), prioritized reconstruction spending, and debt sustainability support.
- Empirical findings underscore urgency:
  - Short-run and long-run impacts are non-trivial: e.g., a 1 percent of GDP damage shock reduces GDP growth by 0.7 percentage point in the year of the disaster (short-run) and 0.3 percentage point over 10 years (long-run average effect).
  - Average annual damage and losses and projected climate costs imply persistent negative drag on growth and public finances without stronger resilience measures.

*Source: IMF staff study — "25. Multi-Pillar Framework: Strenthening ex ante and ex post Resilience" (chapter/section content provided in the source PDF).*

### Appendix D, Table 1 shows that in explaining the impact of natural disasters on GDP growth, the coefficient

### _wp15125 - Appendix D, Table 1 shows that in explaining the impact of natural disasters on GDP growth, the coefficient

### Event analysis — definition and methodology
- Natural disaster episode defined as one that results in damage and losses of at least 10 percent of GDP.
- Event analysis focuses on relationship between growth performance and natural disaster shocks before, during, and after an episode; does not attempt to determine causality.
- Analysis based on natural disasters with damage and losses larger than 10 percent of GDP during 1970-2013. Average of two years before and average of two years after. The median damage of these natural disasters was 26 percent of GDP.

### Key empirical findings from event analysis and Appendix D, Table 1
- Coefficient of damage and losses as a percent of GDP is -0.003 (real GDP in logarithm; damage and losses in percent of GDP). Because real GDP is expressed in logarithm, while damage and losses are expressed in percent of GDP, the coefficient is multiplied by 100.
- Illustration: Assumes damage and losses of 60 percent of GDP. The number 60 is multiplied by 0.3. The number 18 is equivalent to 0.3*60. See table Appendix D. Table 1 for the coefficient on damage and losses. Since real GDP is in log while damage and losses are expressed in percent of GDP, the coefficient 0.003 is multiplied by 100.
- Average growth outcomes (Table 2 illustration):
  - Annual GDP growth (in percent): Before natural disaster 3.0; After natural disaster 1.3; Difference 1.7
  - Cumulative GDP growth over 10 years (in percent): Before natural disaster 34.0; After natural disaster 16.0; Difference 18.0
- A main finding: a loss in output relative to the pre-disaster GDP growth trend persists after two years, with the fiscal balance remaining as negative as in the year of the disaster.
- For the events analyzed, growth was zero in the year of the disaster on average; while growth rebounds fairly quickly, on average two years after the disaster it is below the growth rate prior to the natural disaster shock.
- Fiscal balance deterioration remains large after the event, consistent with VAR results and likely reflecting infrastructure rehabilitation and rebuilding.

### Event analysis figures (selected values preserved)
- Figure 23 (Event Analysis—Real GDP Growth Before and After Natural Disasters) shows a before/after comparison (in percent); based on events with damage and losses larger than 10 percent of GDP during 1970-2013; median damage 26 percent of GDP.
- Figure 24 (Event Analysis—Fiscal Balance Before and After Natural Disasters) shows before (average 2 years previous), disaster year, and after (average 2 years after) comparisons (in percent); based on same sample; median damage 26 percent of GDP.

### Multi-pillar framework for disaster risk management (key pillars and ex ante/ex post measures)
- Key ex ante pillars:
  - identifying and undertaking risk assessment; at the national level, ex ante resilience entails identifying risks and explicitly integrating risks into the fiscal frameworks and budget planning.
  - providing self-insurance by building policy buffers to enhance resilience to shocks (lower debt, higher fiscal balances and reserves).
  - reducing risks by enhancing preparedness, including by investing in “smart” infrastructure that can better cope with climate change and natural hazards and by enhancing debt-management capacity.
  - transferring risk through private or sovereign insurance and through multilateral risk-sharing mechanisms (e.g., international safety net).
- Examples and mechanisms:
  - Pacific Catastrophe Risk Insurance Pilot: launched in January 2013; concluded second year pilot phase in October 2014; pilot began third phase in November 2014 and is now expected to be concluded in October 2015. Current participating countries: the Cook Islands, the Marshall Islands, Samoa, Tonga, and Vanuatu. The government of Japan provided an additional US$1 million to fund premium subsidies for four of the participating countries (Cook Islands is self funding). In January 2014, Tonga received a payout of US$1.3 million; Vanuatu received US$1.9 million after Cyclone Pam. Damages and losses were respectively US$45.4 million in Tonga and US$467 million in Vanuatu.
  - Membership in multilateral organizations as ex ante pooling of global resources (risk-pooling mechanism).
  - Regional discussions: Melanesian Spearhead Group (MSG) discussing an emergency Stabilization Fund for balance of payments crises.
- Ex post (coping and resilient recovery) actions:
  - emergency response and reconstruction, including risk-reducing measures such as resettlement away from coastline where feasible and infrastructure investment.
  - Reconstruction as opportunity to accelerate growth-enhancing structural reforms.
- Financing context:
  - Donor financing remains important; building buffers has opportunity costs; participation in insurance mechanisms promising but disbursements limited.
  - Global climate finance target noted: raising US$100 billion a year by 2020 (established at 2010 United Nations Climate Change Conference in Cancun).
  - Access to global funding for Pacific island countries is challenging because of capacity constraints; bilateral funding remains critical; donor coordination should be strengthened given limited administrative capacity.

### The role of the IMF — surveillance, financing, and capacity development
- Surveillance — policy messages and integration:
  - Integrate natural disaster risks into macro frameworks to determine the magnitude of buffers (or self-insurance) needed and of outside insurance.
  - Allow sufficient flexibility to help redeploy spending rapidly.
  - Ensure sufficient policy space (external reserves and low debt) to mitigate potential balance of payments shortfalls.
  - After an event: greater transparency for disaster assistance, strengthen coordination among development partners and authorities, pursue growth-enhancing structural reforms.
  - Include costs of natural disasters and climate change in debt sustainability analysis and scenario analysis in Article IV consultations; cited examples: Kiribati, Tonga (2014 Article IV Consultation Staff Report assessed cyclone Ian with damages and losses about 10 percent of GDP), Samoa (2015 Article IV Consultation Staff Report analyzed impact on debt).
- IMF financial support instruments and cases:
  - Rapid Credit Facility (RCF): rapid financial support in a single, up-front payout for low-income countries facing urgent financing needs; financing under the RCF carries a zero interest rate through 2016, has a grace period of 5½ years, and a final maturity of 10 years.
  - Rapid Financing Instrument (RFI): similar to RCF; financing terms same as Flexible Credit Line, Precautionary and Liquidity Line and Stand-By Arrangements; repayment within 3¼ to 5 years.
  - Samoa cases:
    - Tropical Cyclone Evan (December 2012): damage estimated at 30 percent of GDP. One-off disbursement of US$8.6 million under RCF approved May 2013.
    - 2009 tsunami: damage and losses estimated at 25 percent of GDP; IMF financial assistance equivalent to US$9.3 million provided.
  - Vanuatu case:
    - Devastating cyclone of March 2015: IMF Board approved disbursement of about US$11.9 million under the RCF and disbursement of about US$11.9 million under the RFI (IMF, 2015; IMF Country Report No. 15/149).
    - These disbursements intended to help cope with immediate balance-of-payments needs and catalyze donor support. 
  - IMF Catastrophe Containment and Relief (CCR) Trust:
    - Transformed from PCDR Trust in February 2015.
    - Allows Fund to join international debt relief efforts when poor countries are hit by catastrophic natural disasters or public health disasters.
    - CCR Trust available to 38 low-income countries eligible for concessional borrowing through the PRGT and which also have either a per capita income below US$1,215—or, for small states, a population below 1.5 million and a per capita income below US$2,430. Among the PICs, only Solomon Islands meet these criteria.
    - A country qualifies if hit by a disaster that directly affects at least one third of the population and destroys more than a quarter of the country’s productive capacity (or caused by damage exceeding100 percent of GDP).
  - Augmentation of an existing program: when a country with an IMF-supported program is hit by a natural disaster, augmented financing under the existing program could provide additional support; an IMF program can catalyze international assistance even without augmentation (example: Solomon Islands, 2014).
- Capacity development and technical assistance:
  - TA on public financial management (PFM) improves budget planning and transparency, helps incorporate disaster risks into fiscal planning.
  - IMF supports PFM reform and contributes to Public Expenditure and Financial Accountability (PEFA) assessments.
  - IMF provides TA for environmental tax reforms to get energy prices to reflect harmful environmental side-effects (references cited).
  - Pacific Financial Technical Assistance Centre (PFTAC) and World Bank DRFI Program delivered a regional workshop (March 2015) addressing:
    - special budgetary procedures for providing rapid access to emergency funding,
    - the macroeconomic and fiscal impact of natural disasters,
    - incorporating disasters risks into the fiscal planning process, and
    - disaster risk financing.

### Policy implications and recommendations (from source)
- Explicitly recognize costs of natural disasters and climate change in baseline macro-frameworks and debt sustainability analyses.
- Build policy buffers (lower debt, higher fiscal balances and reserves) to enhance ex ante resilience, while acknowledging opportunity costs and limits for small states.
- Enhance preparedness by investing in “smart” infrastructure and improving debt-management capacity.
- Pursue risk transfer mechanisms: private/sovereign insurance and multilateral risk-pooling arrangements.
- Strengthen donor coordination and improve access to global climate finance by addressing capacity constraints.
- Use reconstruction as an opportunity for growth-enhancing structural reforms.
- Continue IMF support via surveillance, financial instruments (RCF, RFI, CCR Trust, program augmentation), and capacity development (PFM, TA, training).

### Conclusions (source summary)
- Pacific island countries face severe challenges from natural disasters and climate change, impacting both potential growth and public finances.
- A more strategic approach is needed to address increasing frequency and magnitude of events.
- Explicitly incorporating disaster and climate costs into macro frameworks and DSAs is important.
- Building policy buffers is particularly relevant for small Pacific states, but external assistance and insurance schemes will remain necessary.
- The IMF can help through continued technical assistance and training to enhance countries’ risk management capacities and foster ex ante resilience.

*Source: _wp15125 - Appendix D, Table 1 shows that in explaining the impact of natural disasters on GDP growth, the coefficient (IMF staff content provided in the input).*

### APPENDIX A: DATA SOURCES

### _wp15125 - APPENDIX A: DATA SOURCES

### Data sources and coverage (Appendix A)
- Data were collected from different sources depending on data availability and built backward provided no inconsistencies are generated in splicing the series.
- Table A.1. Data Sources (series and sources used):
  - Natural Disaster (In U.S. millions): EM-DAT
  - Nominal GDP (In millions of national currency): WEO, WDI and IMF staff reports
  - Real GDP Growth (In percent): WEO, WDI and IMF staff reports
  - Fiscal Balance (In millions of national currency): IMF staff reports
  - Tax revenue (In millions of national currency): IMF staff reports
  - Government expenditure (In millions of national currency): IMF staff reports
- Country/time coverage listed in Table A.1:
  - Fiji (1970-2013)
  - Samoa (1970-2013)
  - Solomon Islands (1974-2013)
  - Tonga (1981-2013)
  - Vanuatu (1981-2013)
  - World (1970-2013) — fiscal series from WEO, WDI and IMF staff reports noted where applicable
- Abbreviations:
  - EM-DAT: Centre for Research on the Epidemiology of Disasters, The International Disaster Database.
  - WDI: World Bank, World Development Indicators.
  - WEO: IMF, World Economic Outlook.

### Determinants of real revenue (Appendix B)
- Methodology:
  - Separate dynamic panel regressions for groups: small states, Pacific island small states, LICs, emerging markets, resource-rich small states, and non-resource-rich small states.
  - Dependent variable: real revenue.
  - Explanatory variables: GDP (and its lag), weighted terms of trade (and its lag), intensity of natural disasters (lagged), lagged real revenues, fishing license fees.
  - Panel regressions, 1990-2013 using the generalized method of moments (GMM) instrumenting with lagged explanatory variables.
  - Asterisks indicate p-values: *** p<0.01, ** p<0.05, * p<0.1.
- Key findings:
  - Revenue shows strong procyclicality, especially in small states that are net commodity importers.
  - Combined coefficients higher than 1 on real GDP growth and lagged GDP growth imply revenue procyclicality.
  - For small states, the sum of the coefficients on real GDP growth (current period and one-period-lagged) — a proxy for cyclical components of revenues — is equal to 1.7.
  - After controlling for GDP, revenue depends on terms-of-trade shocks, especially in resource-rich small states.
  - Natural disasters heighten revenue volatility, especially in the Pacific islands.
  - Staff analysis: a natural disaster that affects 1 percent of the population causes a drop in real revenue of 0.4 percentage point in the Pacific islands.
- Table B.1. Determinants of Real Revenue — selected coefficient estimates (Year-on-year percent change):
  - Real GDP growth:
    - Small states: 1.093***
    - Pacific island small states: 1.672***
    - Low-income countries: 1.622***
    - Emerging markets: 1.41***
    - Resource-rich small states: 0.933***
    - Non-resource-rich small states: 1.249***
  - Real GDP growth (lagged):
    - Small states: 0.607*
    - Pacific island small states: 0.568
    - Low-income countries: 0.236
    - Emerging markets: -0.124
    - Resource-rich small states: 0.512
    - Non-resource-rich small states: 0.556*
  - Weighted terms of trade growth:
    - Small states: 0.390**
    - Pacific island small states: 0.659**
    - Low-income countries: 0.468***
    - Emerging markets: 0.821**
    - Resource-rich small states: 1.401**
    - Non-resource-rich small states: 0.120**
  - Weighted terms of trade growth (lagged):
    - Small states: 0.227
    - Pacific island small states: 0.352
    - Low-income countries: 0.130
    - Emerging markets: -0.180
    - Resource-rich small states: 0.260
    - Non-resource-rich small states: 0.136
  - Intensity of natural disasters (lagged):
    - Small states: -0.248**
    - Pacific island small states: -0.429***
    - Low-income countries: 0.039
    - Emerging markets: -0.189
    - Resource-rich small states: -0.294
    - Non-resource-rich small states: -0.239**
  - Real revenue growth (lagged):
    - Small states: -0.410
    - Pacific island small states: -0.375
    - Low-income countries: -0.181
    - Emerging markets: 0.024
    - Resource-rich small states: -0.237
    - Non-resource-rich small states: -0.545
  - Fishing license fees:
    - Small states: 0.206***
    - (Included explicitly for groups dependent on fishing license fees)
  - Constant terms (selected):
    - Small states: 0.009
    - Pacific island small states: -1.667
    - Low-income countries: -1.223
    - Emerging markets: -0.895
    - Resource-rich small states: 2.498
    - Non-resource-rich small states: -0.684
  - Sample and panel statistics:
    - Observations: 5919 (Small states), 2792 (Pacific island small states), 3074 (Low-income countries), 5100 (Emerging markets), 466 (Resource-rich small states) — as presented in the table layout
    - Number of countries: 33 (Small states), 6 (Pacific island small states), 49 (Low-income countries), 62 (Emerging markets), 7 (Resource-rich small states), 27 (Non-resource-rich small states)
  - Note: Combined coefficients higher than 1 on real GDP growth and lagged GDP growth imply revenue procyclicality.

### VAR model and robustness (Appendix C)
- Stationarity and unit root tests (Table C.1. Unit Root Test):
  - Real GDP Growth:
    - Dickey-Fuller Test: 5/5 (fraction of series rejecting unit root)
    - Perron-Phillips Test: 5/5
    - Im-Pesaran-Shin Test: 0.0000 (probability of unit root)
  - Fiscal balance:
    - Dickey-Fuller Test: 4/5
    - Perron-Phillips Test: 4/5
    - Im-Pesaran-Shin Test: 0.0000
  - Tax revenue:
    - Dickey-Fuller Test: 1/5
    - Perron-Phillips Test: 1/5
    - Im-Pesaran-Shin Test: 0.2908
  - Expenditure:
    - Dickey-Fuller Test: 1/5
    - Perron-Phillips Test: 1/5
    - Im-Pesaran-Shin Test: 0.0578
  - ∆ Fiscal balance:
    - Dickey-Fuller Test: 5/5
    - Perron-Phillips Test: 5/5
    - Im-Pesaran-Shin Test: 0.0000
  - ∆ Tax revenue:
    - Dickey-Fuller Test: 5/5
    - Perron-Phillips Test: 5/5
    - Im-Pesaran-Shin Test: 0.0000
  - ∆ Expenditure:
    - Dickey-Fuller Test: 5/5
    - Perron-Phillips Test: 5/5
    - Im-Pesaran-Shin Test: 0.0000
- Interpretations:
  - Real GDP growth is stationary (Dickey-Fuller rejects unit root for all countries and IPS p-value equal to 0).
  - Fiscal variables appear stationary only in first difference; fiscal deficit appears stationary in levels for most countries.
  - First differences of fiscal variables are stationary across tests.
- VAR specification and robustness:
  - Lag structure selected at one lag to minimize parameters; estimations with two and three lags show analogous impulse responses.
  - Robustness checks include controlling for global shocks: world real GDP growth and variations in the price of oil.
  - Results do not differ materially after controlling for these global variables.
- Impulse response analyses (figures summarized):
  - Responses of Real GDP Growth, Fiscal Balance (percentage points of GDP), Tax Revenue (percentage points of GDP), and Government Expenditure (percentage points of GDP) to:
    - A natural disaster with intensity equivalent to 1 percent of the population affected.
    - A 1 percent of GDP damage shock.
  - Robustness to lag specification shown for 1 lag, 2 lags, and 3 lags; figures display mean responses and 95 percent confidence intervals over 0–10 years.

### ARDL model and long-run effects (Appendix D)
- Methodology note:
  - The ARDL methodology is valid regardless of whether regressors are exogenous or endogenous, and irrespective of whether underlying variables are I(1) or I(0).
- Appendix D. Table 1. Fixed Effects Estimates of the Long-Run Effects, Based on the ARDL Model:
  - Dependent variable: real GDP (in log)
  - Capital stock (in log): 0.344***
  - Population (in log): 0.628***
  - Damage and losses (in percent of GDP): -0.003**
  - F-statistics for cointegration: 4.851***
  - Observations: 225
  - Number of countries: 5
  - Notes:
    - The lag length was selected using Schwarz's Bayesian criterion.
    - Asterisks indicate p-values: *** p<0.01, ** p<0.05, * p<0.1.
    - Estimation period: 1970-2014.

*Source: APPENDIX A: DATA SOURCES (content provided).*

### APPENDIX E: SELECTED PROGRAMS AND FUNDS

### APPENDIX E: SELECTED PROGRAMS AND FUNDS

### Overview
- Appendix lists institutional programs and funds addressing climate change, disaster risk reduction, and recovery for Pacific Developing Member Countries and other developing countries.
- Entries include: Institution; Program / Fund; Purpose; Period; Amounts; and concise program descriptions.

### Asian Development Bank (ADB)
- Pacific Climate Change Program (PCCP)
  - Purpose: Climate Change
  - Period: 2011-14
  - Amounts: US$240 million
  - Description: Integrated program addressing mitigation and adaptation; focuses on climate and disaster proofing of the investment portfolio, scaling up renewable energy. Out of ADB’s own funds, approximately US$172 million funded adaptation and mitigation costs during 2011-14. ADB mobilized around US$68 million from global funds: Least Developed Country Fund (LDCF) and the Climate Investment Fund's Pilot Program for Climate Resilience (PPCR).
- Asia Pacific Disaster Response Fund (APDRF)
  - Purpose: Natural disasters
  - Amounts: US$3 million per event
  - Description: Incremental grant resources for quick-disbursing grants to restore life-saving services and augment aid. Since 2011, ADB approved 8 APDRF projects in the Pacific.
- Disaster Response Facility (DRF)
  - Purpose: Natural Disasters
  - Period/Amounts: Since 2011, US$26 million
  - Description: Supports emergency assistance, restoration, rehabilitation and reconstruction via grants or loans depending on country status. ADF-only country can get up to 100 percent of the country’s annual Performance Based Allocation (PBA) or US$3 million per disaster, whichever is higher; a blend country can receive up to 3 percent of its annual PBA. Since 2011, ADB helped Pacific countries access US$26 million from the DRF through 3 projects (Solomon Islands, Tonga, and Samoa).

### European Union (and partners)
- ACP-EU Building Safety and Resilience in the Pacific
  - Purpose: Disaster Risk Reduction & Climate Change Adaptation
  - Period: 2013-2018
  - Amounts: €20 million
  - Description: Strengthen capacity of PICs to address risks posed by natural hazards and maximise synergies between DRR and CCA.
- ACP-EU Natural Disaster Risk Reduction Programme (ACP-EU NDRR)
  - Purpose: Disaster Risk Reduction & Climate Change Adaptation
  - Period: 2011-20
  - Amounts: Total: €80 million; Indicative Pacific: €13.7 million
  - Description: Prevention, mitigation and preparedness; mainstreaming DRR; risk identification and assessment; early warning systems; risk transfer; integration of DRR into recovery.
- Intra-ACP Global Climate Change Alliance (GCCA)
  - Purpose: Climate Change Adaptation / Mitigation
  - Period: 2012-16
  - Amounts: €37 million; Indicative Pacific: €8.0 million
  - Description: Integrated strategies, mainstreaming climate change in national development planning and budgeting, institutional and capacity strengthening, fostering dialogue and exchange of experiences.
- Enhancing the Capacity of Pacific Island Countries to Address the Impacts of Climate Change on Migration (EU/UNESCAP/ILO/UNDP)
  - Purpose: Climate change and migration
  - Period: End in 2016
  - Amounts: €2.1 million
  - Description: Capacity building on climate change induced migration financed through the European Initiative for Democracy and Human Rights (EIDHR).
- Global Index Insurance Facility (GIIF)
  - Purpose: Disaster Risk Reduction
  - Period: 2008-16
  - Amounts: €24.5 million (all ACP countries)
  - Description: Expand use of index/parametric insurance as a risk management tool to mitigate weather/CAT risks in ACP countries.
- GCCA South Pacific
  - Purpose: Mainstreaming Adaptation
  - Period: 2011-15
  - Amounts: €10 million
  - Description: Supports Secretariat of the Pacific Community and Secretariat of the Pacific Regional Environment Programme across development, poverty reduction, coastal zone management, health, infrastructure and water and sanitation.
- GCCA Papua New Guinea
  - Purpose: REDD – Forest
  - Period: 2013-17
  - Amounts: €8.6 million
  - Description: Contributes to implementation of the national REDD readiness Plan (FAO UN-REDD).
- GCCA Samoa
  - Purpose: Mainstreaming Adaptation / disaster risk reduction
  - Period: 2012-15
  - Amounts: €3.0 million
  - Description: Contributes to implementation of the national Water for Life sector Plan - Water and sanitation.
- GCCA Salomon Islands
  - Purpose: Mainstreaming Adaptation
  - Period: 2011-14
  - Amounts: €2.8 million
  - Description: Supports development and poverty reduction; contributes to NAPA priorities and National Disaster Risk Management Plan.
- GCCA Timor Leste
  - Purpose: Mainstreaming Adaptation
  - Period: 2013-18
  - Amounts: €4.0 million
  - Description: Supports development, forests, agriculture and natural resource management.
- GCCA Vanuatu
  - Purpose: Mainstreaming Adaptation / disaster risk reduction
  - Period: 2010-14
  - Amounts: €3.2 million
  - Description: Supports development, agriculture, natural resource management and water and sanitation; contributes to measures identified in the NAPA.
- Pro-Resilience Action
  - Purpose: Building resilience in response to food crises
  - Period: 2014-20
  - Amounts: Indicative €65 million/year (worldwide)
  - Description: Supports poor and food insecure to react to crises and strengthen resilience. Worldwide action accessible to Pacific SIDS in case of food crises; program can be activated if a natural disaster has impacts in terms of food and nutrition security.
- Adapting to Climate Change and Sustainable Energy (ACSE)
  - Purpose: Climate Change
  - Period: 2014-2019
  - Amounts: €35.5 million
  - Description: Will help 15 PICs adapt and enhance energy security. Objectives: (1) create/strengthen national technical expertise on climate change adaptation and sustainable energy, (2) improve cost-effective and efficient energy systems to reduce fossil fuel dependency, (3) improve communities' adaptive capacity. Also aims to enhance sustainable livelihoods via government institutional support and community empowerment (agriculture, coastal fishery, salt-resistant plant varieties, secure water supply, improved access to energy).

### United Nations and Funds
- Adaptation Fund (AF)
  - Purpose: Established to finance concrete adaptation projects and programs in developing country Parties to the Kyoto Protocol
  - Established: 2001
  - Description: Financed from the share of proceeds on CDM project activities and other sources. Share of proceeds amounts to 2 percent of certified emission reductions (CERs) issued for a CDM project activity. Supervised and managed by the Adaptation Fund Board (AFB) composed of 16 members and 16 alternates meeting at least twice a year.

### UNDP
- UNDP investment in DRR & Recovery
  - Purpose: Disaster Risk Reduction and Recovery
  - Period: 2005-14
  - Amounts: US$1.7 billion
  - Description: Since the launch of the Hyogo Framework for Action (HFA) in 2005, UNDP invested just under US$1.7 billion in 163 countries to build capacity to prevent, prepare for and recover from disasters. In 2014 specifically: invested US$252 million on disaster prevention and risk management (supporting development of 98 new DRR and adaptation plans/frameworks or multi-stakeholder coordination mechanisms in 23 countries; new early warning systems in 17 countries; new disaster response and recovery plans in 14 countries). In 2014, invested US$281 million on Early recovery and rapid return to sustainable development pathways.
- UNDP investment in Climate Change
  - Purpose: Climate Change Adaptation and Mitigation
  - Period: 2004-14
  - Amounts: US$1.4 billion
  - Description: Over the past five years, UNDP’s climate change portfolio in support of over 140 countries totaled US$1.4 billion: 48 percent Adaptation, 40 percent Mitigation, 12 percent Cross cutting programmes (including UN REDD and Climate Finance). Geographical coverage: 32 percent in Asia Pacific, 31 percent in Africa, 17 percent in Eastern Europe and CIS, 12 percent in Latin America and Caribbean, 8 percent in Arab States. In 2014, results included channeling over US$200 million to support more than 100 countries in implementing adaptation and mitigation initiatives.

### World Bank Group and Related Facilities
- Global Facility for Disaster Reduction and Recovery (GFDRR)
  - Purpose: Reduce vulnerability to natural disasters and climate change
  - Established: 2006
  - Project sizing: For projects sized US$100 thousand-US$1 million
  - Description: Partnership of 35 countries and six international organizations to mainstream DRR and CCA in country development strategies, supporting country-led implementation of the Hyogo Framework for Action (HFA).
- Immediate Response Mechanism (IRM) (IDA)
  - Purpose: Natural disasters
  - Established: Dec 2011
  - Amounts: 5 percent of undisbursed IDA project balances or SDR$5 million.
  - Description: Allows IDA countries rapid access to up to 5 percent of undisbursed IDA investment project balances following a crisis (natural disasters and economic shocks). Small states and countries with small undisbursed project balances may access up to $5 million. Complements longer-term emergency response tools, such as the Crisis Response Window.
- Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI)
  - Purpose: Natural disasters
  - Period: Nov 2012 to Nov 2014
  - Amounts: US$45 million in aggregate coverage
  - Description: Participating countries select per-peril coverage and option of coverage attaching at loss levels of a severity of recurrence of 1 in 10, 15 or 20 years (or less frequent). Five PICs participated in initial 2012-2013 pilot, which had an aggregate limit of US$45 million and an annual expected loss of US$1 million. Scheme covers the Marshall Islands, Samoa, Solomon Islands, Tonga, Vanuatu, and the Cook Islands.
- Climate Investment Funds (CIF) (World Bank Group trustee)
  - Purpose: Climate Change
  - Established: 2008
  - Amounts and Windows:
    - US$5.5 billion Clean Technology Fund (CTF)
    - US$639 million Forest Investment Program (FIP)
    - US$1.3 billion Pilot Program for Climate Resilience (PPCR)
    - $551 million Scaling Up Renewable Energy in Low Income Countries Program (SREP)
  - Description: Provides funding to 48 developing and middle income countries from contributor countries with co-funding sought from private sector; fosters programmatic, MDB-supported investment planning and implementation.
- IDA Climate and Disaster Resilience
  - Purpose: Climate and Disaster Resilience
  - Period: 2011-15
  - Amounts: US$150 million
  - Description: IDA-17 Replenishment requires Country Partnership Frameworks to incorporate climate and disaster risk considerations, and for all IDA operations to be screened for short- and long-term climate change and disaster risks, integrating resilience measures as appropriate (concessional credits and IDA grants).
- IBRD Climate and Disaster Resilience
  - Purpose: Climate and Disaster Resilience
  - Period: 2011-15
  - Amounts: US$15 million
  - Description: IBRD promotes sustainable development in middle-income countries and creditworthy poorer countries via loans, guarantees, risk management products, and advisory services.

### Bilateral and Other Donors
- DFAT (Australia)
  - Purpose: Climate Change
  - Amounts: Total funding for 2010/11 and 2012/13 was AU$599 million
  - Description: Support to developing countries to adapt, reduce carbon emissions and pursue cleaner development. Focus on LDCs and small island developing states; includes reducing emissions from deforestation, piloting low emission development pathways, and engagement in international development and environment forums.
- Japan — Assistance Package for Pacific Island Countries at PALM7
  - Purpose: Climate Change
  - Period: 2015-17
  - Amounts: US$450 million
  - Description: Assistance to Pacific island countries battling rising sea levels and natural calamities. Focus areas: disaster risk reduction, climate change, environment, people-to-people exchanges, sustainable development, maritime issues and fisheries, trade, investment and tourism.

### Key quantitative figures (selected)
- PCCP total amount: US$240 million; ADB own funds for adaptation/mitigation during 2011-14: approximately US$172 million; mobilized global funds: around US$68 million.
- ADB APDRF: US$3 million per event; 8 APDRF projects in the Pacific since 2011.
- ADB DRF: Since 2011, US$26 million accessed via 3 projects.
- EU ACP-EU NDRR: Total €80 million; Indicative Pacific allocation €13.7 million.
- EU GCCA funding: Overall €37 million (2012-16); Indicative Pacific €8.0 million.
- UNDP DRR & Recovery: 2005-14 US$1.7 billion; 2014 disaster prevention and risk management: US$252 million; 2014 early recovery: US$281 million.
- UNDP Climate Change portfolio: 2004-14 US$1.4 billion (48 percent Adaptation, 40 percent Mitigation, 12 percent Cross cutting); regional shares: 32 percent Asia Pacific, 31 percent Africa, 17 percent Eastern Europe and CIS, 12 percent Latin America and Caribbean, 8 percent Arab States. 2014 channeling: over US$200 million.
- PCRAFI aggregate coverage: US$45 million; pilot annual expected loss: US$1 million.
- CIF windows: CTF US$5.5 billion; FIP US$639 million; PPCR US$1.3 billion; SREP $551 million.
- IDA IRM access: 5 percent of undisbursed IDA project balances or SDR$5 million (small states up to $5 million).
- DFAT total funding (selected years): AU$599 million.
- Japan PALM7 package: US$450 million (2015-17).
- EU Pro-Resilience Action indicative funding: €65 million/year (worldwide).

*Source: APPENDIX E: SELECTED PROGRAMS AND FUNDS (excerpt).*

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