Daniel Kull Global Facility for Disaster Reduction and Recovery (GFDRR) The World Bank

1. Socioeconomic Benefit Analysis: Applications in World Bank Operations Daniel Kull Global Facility for Disaster Reduction and Recovery (GFDRR) The World Bank Geneva, 8 April, 2013 Meeting of the WMO Forum: Social and Economic Applications and Benefits of Weather, Climate and Water Services

2. Modernization of NHMSs • NMHS capacity often not adequate and considerably degraded during the last 15-25 years. • Since 1990s World Bank invests in modernization of NHMSs, currently scaling-up system-wide investments . • International support is still significantly below priority needs – high World Bank client demand. • International support in NMHS modernization in developing countries so far largely unsuccessful due to: • Lack of government understanding of NMHS’s value and commitment to maintain NMHS operations. • Poor project design (reliance on unsustainable solutions, inadequate attention to capacity building, limited scope of investment, etc.). • Inadequate coordination among donors. • Technical complexity and small size of the projects.

3. Key Principles for Modernizing NMHSs • Modernization of NMHSs in developing countries is a high value investment, providing a positive return to the national economy, while improving public safety and security. • The financing and scope of modernization must be sufficient to be transformative. • Clear legal and regulatory frameworks for providing weather, climate, and water services – which articulate the roles and responsibilities of the NMHSs – increase effectiveness. • Large-scale modernization programs should typically include three components: • Institutional strengthening, capacity building, and implementation support • Modernization of observation infrastructure and forecasting • Enhancement of the service delivery system • Modernization of NMHSs should be considered within the wider regional and global context. • The World Bank and development partners have a vital role in strengthening NMHSs. Source: World Bank (2013). "Weather, Climate and Water Hazards and Climate Resilience: Effective Preparedness through National Meteorological and Hydrological Services“ (in press).

4. Justifying and Leveraging Investment • Better estimates of the socioeconomic costs and benefits of NHMSs needed for financing approvals. • Better communication of such results needed. • Concerned NHMSs and governments directly requesting support for such analysis.

5. Development Context • Risk reduction benefits = avoided or reduced potential damages. • Enhanced productivity of weather/climate-sensitive sectors. • Weather extremes are stochastic events, so most benefits are probabilistic. • Need to account for changes due to current and future climate, socioeconomics, land-use and other trends. • Socioeconomic vulnerability and productivity are multidimensional concepts encompassing a large number of factors. • Challenges: • Lack of data. • Lack of expertise. • High resource demands. • Uncertainties in future conditions. • Exposure of people, assets and environment difficult to quantify.

6. Specific NHMS Challenges • Lack of established techniques of economic assessment understandable to NMHS staff. • Lack of in-house economic expertise. • Lack of baseline economic data, particularly data on losses from weather events. • Insufficient priority attached to economic assessment by some NMHS management. • Poor interactions with clients/beneficiaries. • Lack of resources for studies.

7. Summary of Utilized Approaches World Bank projects use a combination of: • Sector-specific assessments • Customized sociological surveys • Simplified benchmarking • Probabilistic assessment of avoided damage and loss

8. Sector-specific Assessment • Data collection and/or surveys of experts: • Level of damages and losses from hazardous weather events and adverse weather conditions; • Estimated changes in preventable damage and losses due to a more accurate and timely information/forecasts. • Evaluate marginal effects from modernization for each sector and the integral effect for the economy: • Dependence on weather conditions and hazards, amount and quality of information used, and current efficacy of information uses. • Potential demand for information types and presentation formats, accuracy and timeliness of each element/event forecast, requirements for optimal performance, recommendations and proposals on service improvement and customization. Source: World Bank (2008). Weather and Climate Services in Europe and Central Asia: A Regional Review. World Bank Working Paper No. 151., Washington, D.C., pp. 70.

9. Customized Sociological Survey • Captures household benefits. • Based on contingent valuation approach. • Customized for national context. • Willingness-to-pay for: • Detailed weather forecast for the next month • NHMSs out of own pocket • Insurance against weather-related disasters • Limited by partial inconsistency with economic models of rational choice and unavoidable biases. Source: World Bank (2008). Weather and Climate Services in Europe and Central Asia: A Regional Review. World Bank Working Paper No. 151., Washington, D.C., pp. 70.

10. Simplified Benchmarking Stage 1 • Parameters set to average values, scaled to national GDP. • Average annual losses from adverse and dangerous weather conditions: 0.45% of GDP; range of annual losses: 0.1 to 1.0 % of GDP. • Average annual level of preventable weather losses: 40% of total losses • Weather sensitive sectors of the economy: 50% of GDP • Share of agriculture: 15% of GDP • Meteorological vulnerability: “average”. • Status of hydrometeorological service provision: “satisfactory” Stage 2 • Benchmarks adjusted following rapid assessments of national context. • Adjusted benchmarks used to assess the marginal efficiency of the existing NMHS and of modernized services. Source: World Bank (2008). Weather and Climate Services in Europe and Central Asia: A Regional Review. World Bank Working Paper No. 151., Washington, D.C., pp. 70.

11. Example: Europe and Central Asia Source: World Bank (2008). Weather and Climate Services in Europe and Central Asia: A Regional Review. World Bank Working Paper No. 151., Washington, D.C., pp. 70.

12. Probabilistic Avoided Damage and Loss • Probabilistic risk assessment • Assumptions on potential damage and loss reduction due to modernized NHMS services Source: G20 - Mexico & World Bank: Improving the Assessment of Disaster Risks to Strengthen Financial Resilience

13. Loss-frequency curve • Also called “Exceedance probability curve” represents the relationship between disaster frequency and severity. • Area under the loss-frequency curve = Average Annual Losses (AAL) • For this example: AAL = $12’900/year • Eliminate losses up to $25’000, then AAL = $4’200/year

14. Yemen example Combined with economic productivity increases based on benchmarking approach

15. Pragmatic Approach Based on: Kull, D., Mechler, R. and Hochrainer, S. (2013). “Probabilistic Cost-Benefit Analysis of Disaster Risk Management in a Development Context.” Disasters, doi: 10.1111/disa.12002.

16. Overcoming Challenges: Transparency Consistent approaches/frameworks to identify and communicate: • Analysis methods. • Key data, their reliability and sources. • Externalities and how these are incorporated. • Assumptions and the basis on which they are made. • Sensitivity analysis and their implications for the results. Building the basis for evaluating the validity of the results: • Issues such as willingness to take action, warning reliability and potential costs of taking inappropriate action. • Distributional aspects. • Conservative analysis comparing the lowest potential benefits with the highest potential costs instills greater confidence. Source: Moench, M. and The Risk to Resilience Study Team (2008). Understanding the Costs and Benefits of Disaster Risk Reduction under Changing Climatic Conditions, From Risk to Resilience Working Paper No. 9, eds. Moench, M., Caspari, E. & A. Pokhrel, ISET and ProVention, Kathmandu, 38 pp.

17. Looking Ahead • Closer collaboration between World Bank, WMO & members, and other partners on NMHS modernization, including socioeconomic benefit analysis (SEB). • Joint development of guidance on SEB for weather, climate and water services for World Bank staff and clients, also in support of WMO commitments. • More robust investigation and global database on baselines for benchmarking. • Technical support and capacity development for NHMSs to perform SEB also to strengthen user engagement. • Joint advocacy to raise profile and recognition of NHMSs’ added value with government, public, media, etc.