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Disaster Risk Financing in the Agricultural Sector Dr. Jerry Skees H.B. Price Professor, University of Kentucky President, GlobalAgRisk, Inc., USA WMO.

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Presentation on theme: "Disaster Risk Financing in the Agricultural Sector Dr. Jerry Skees H.B. Price Professor, University of Kentucky President, GlobalAgRisk, Inc., USA WMO."— Presentation transcript:

1 Disaster Risk Financing in the Agricultural Sector Dr. Jerry Skees H.B. Price Professor, University of Kentucky President, GlobalAgRisk, Inc., USA WMO Expert Advisory Group on Financial Risk Transfer (EAG-FRT I) Geneva, Switzerland December 2011

2 GlobalAgRisk, Inc. Activities Research and development Technical capacity building Educational outreach Supported by Multinational donors Governments Nongovernment organizations Bill and Melinda Gates Foundation, Ford Foundation, GIZ, UNDP World Bank, etc. Mission Improve access to market services for the poor through innovative approaches for transferring natural disaster risk Select Country Work Peru – El Niño/Flood Mongolia – Livestock Vietnam – Flood/Drought Mali – Drought Morocco – Drought Mexico – Drought Romania – Drought Ethiopia – Drought Indonesia -- Earthquakes

3 3 Weather Index Insurance Conceptually similar to weather derivatives. Contingent claims based on an underlying weather index. Important consumer protection issues: needs to be insurance Sold as insurance products. Insurance regulation requires that buyers must have an insurable interest. Works only in areas that are highly exposed to clearly identifiable and measurable spatially-covariate weather perils. Two general categories: Household products. Risk aggregator products. Risk aggregators are lenders, input suppliers, output processors, transporters, etc. whose financial exposure to catastrophic weather events spans a larger region.

4 4 Weather Index Insurance: Challenges Basis risk - a mismatch between payment and loss Reduces value as a guarantee (loan; a form of collateral) High initial and ongoing weather data demands High startup costs: Pilot projects funded by donors in lower income countries. Tailored to specific perils in a specific place Many pilots: Are they Scalable? Sustainable?

5 5 Two Sources of Basis Risk For any given magnitude of the underlying index (e.g., rainfall measured at a particular weather station) there is a conditional probability distribution for the policyholder s exposure to the same variable (e.g., rainfall measured at the policyholder s location). Index Cause of Loss For any given magnitude of the underlying weather variable measured at the policyholder s location, there is a conditional probability distribution of loss. Cause of Loss Loss

6 6 Challenges: Measuring Basis Risk In empirical applications, basis risk is typically measured as the covariance (or correlation) between the index and realized losses using all available data. Is this really the correct measure? We hypothesize that: The correlation between the weather event and losses is greater the more extreme the weather event. The spatial correlation of the weather variable increases.

7 7 Challenges: Indexes and Individual Losses Very unlikely that data will be available to quantitatively determine risk exposure and relationships between potential indexes and realized losses. Must rely on available scientific understanding of these factors; and Qualitative data collected from local sources using carefully structured interviews or focus groups. While time consuming, this process is critical for product design.

8 8 Challenges: Pricing and Payment Triggers Sufficient quantitative data of an appropriate spatial specificity are required to price the insurance. Is the target market a household or a risk aggregator? How much is sufficient? It depends on the temporal presentation of the risk. Is the probability distribution stationary? Is the probability distribution homoskedastic? Do either of the above exhibit multi-year cycles? Understanding the tail of the distribution is critical.

9 9 Sahelian Rainfall

10 10 Sahelian Rainfall

11 Challenges: Data Sources Hydro-Meteorological stations Alternative Data Sources Spatial interpolation of available weather station data. Satellite-based Normalized Difference Vegetation Index (NDVI). Satellite-based measures of rainfall. Satellite-based synthetic aperture radar (SAR) maps contours of geospatial environments (e.g., flooding). Reanalysis data. A class of data products that combines and calibrates observations from many sources weather stations, satellites, weather balloons, etc. Plant growth simulation data

12 12 Challenges: Weather Stations Density of weather stations is very sparse in many lower income countries – especially in Africa. Outside of South Africa, the only publicly available daily weather station data for many African countries is from major airports. More stations exist that report less frequently but density is still sparse – especially in rural areas.

13 13 Challenges: Weather Stations Minimum cost of an automated rain gauge with data logger and remote access capability – approximately US$ 2,000. Does not include cost of shipping, installation, power source, remote access mechanism (e.g., mobile phone or satellite connection), or any security measures (e.g., fencing). Also not included are routine maintenance costs which can be quite prohibitive. Mali had 85 different weather stations in operation between 1951 and Ten are currently in operation.

14 14 Challenges: Alternative Data Sources Satellite-based measures of weather variables still have significant errors when compared to ground- based measures. Many alternative sources tend to understate outliers. Insufficient spatial and/or temporal specificity (possible exception is NDVI). Limited time series of data and challenges with calibrating observations across evolving technologies. Will potential buyers purchase an insurance product based on satellite data?

15 15 Future of Alternative Data Sources Satellite-based data sources are rapidly improving. They are currently used by reinsurers (often in the form of reanalysis data) to supplement short time series of weather station data or to cross-check questionable weather station data. Commercial firms are already investing resources in developing and improving these alternative data sources much as the catastrophe bond market stimulated the development of private-sector firms that provide earthquake and hurricane modeling services.

16 16 Two Implications Widespread scale-up will likely require use of alternative data systems. Initial focus should be on products that require less spatially specific data – risk aggregator products rather than household products.

17 17 Current State of Weather Index Insurance A. Most weather index insurance pilots have been designed to protect against reduced yields for a particular crop. B. Increasingly, weather index insurance pilots have been designed to cover more moderate (rather than catastrophic) losses. Insurance bundled with loans. Concern about long-term demand if buyers don t receive an indemnity. C. Focus is on household products rather than risk aggregator products. D. Limited contribution to climate resiliency and adaptation. Regional climate forecast insurance

18 18 (A) Index Insurance is for Consequential Losses: Livelihoods But extreme weather events have impacts that extend far beyond yield losses for a single crop. Increased irrigation cost or quality losses. Increased disease and pest pressure. Impacts on non-agricultural enterprises. Loss of off-farm income opportunities. Higher prices for food and other necessities. Assets destroyed or liquidated. Need to change the focus from yield losses for a single crop to the multiple consequential losses caused by extreme events.

19 19 (A) Index Insurance is for Consequential Losses: Data Constraints Focusing on consequential losses reduces quantitative data requirements. Quantitative measures of the in-sample correlation between the index and yield of a specific crop are less important. Qualitative data on consequential losses caused by extreme events become more important.

20 20 (A) Index Insurance is for Consequential Losses: Basis Risk Basis risk for consequential losses is likely less than basis risk for yield losses on a single crop. For example, Berg and Schmitz (2008) demonstrate that weather index insurance for a specific crop is a less effective risk management tool for households with a diversified portfolio. Many smallholder households in lower income countries have diversified portfolios of both agricultural and nonagricultural enterprises.

21 21 (B) Index Insurance is for Catastrophic Losses: Cost Moderate loss insurance is prohibitively costly. The most efficient use of insurance is to protect against extreme catastrophic events which can threaten long- term wealth positions. When, instead, weather index insurance is designed to protect against more moderate losses, it raises the price of insurance compared to a catastrophic policy. As a result buyers purchase less sum insured and are less well protected when a catastrophe occurs. Savings and borrowing are more economically efficient mechanism for transferring moderate losses.

22 22 (B) Index Insurance is for Catastrophic Losses: Basis Risk Spatial correlation of the weather variable may increase with the severity of the event. Evidence: spatial correlation of June Iowa county rainfalls is higher in the years of drought than in years of normal or above normal rainfall (Miranda and Liu, 2010) These results support our conclusions that writing index insurance for catastrophic events would likely reduce the basis risk problem (GlobalAgRisk, 2010b).

23 23 (B) Index Insurance is for Catastrophic Losses: Demand What about buyers losing interest if they don t receive frequent indemnities? This concern is certainly supported by some psychology of risk literature. People do purchase some forms of catastrophic insurance – life insurance and accident insurance are the fastest growing forms of microinsurance. Framing matters. Index insurance should be framed as protecting long-term wealth positions from the multiple consequential losses of extreme weather events.

24 24 (C) Risk Aggregator Products have Fewer Data Constraints For the time being, risk aggregator products may be the only feasible means of extending weather index insurance products into many regions of the world. In many areas, weather station density is not sufficient to support household products. Risk aggregator products require less spatially specific data so alternative data sources are more likely to be feasible for risk aggregator products than for household products. Risk aggregators are more likely to understand hedging and basis risk (lower capacity building needs).

25 (D) Linking Insurance and Risk Adaptation Encourage risk management and appropriate adaptation. Smooth cash flow between disaster and non-disaster years. Targeted, early payments. Insurance payouts can be used to finance adaptation investments (e.g., infrastructure, livelihoods transitions). Insurance is only one tool to address climate change Insurance can protect against weather extremes, but adaptation is necessary to adjust to changing climate trends. Challenge: Which Insurance products can provide the greatest opportunity for adjusting to changing climate?

26 (D) Weather vs. Climate The difference between weather and climate is a measure of time. Weather is what conditions of the atmosphere are over a short period of time, and climate is how the atmosphere "behaves" over relatively long periods of time. In addition to long-term climate change, there are shorter term climate variations. This so-called climate variability can be represented by periodic or intermittent changes related to El Niño, La Niña, volcanic eruptions, … Source: NOAA Definitions

27 (D) Regional Climate Change At least three drivers to consider: Teleconnections (e.g., El Nino Southern Oscillation, N. Atlantic Oscillations, India Ocean Oscillations, Arctic Oscillations). Regional land use practices (1930s dust bowl in the U.S.; prolonged drought across the Sahel in the 1960s to 1980s; Northwest China; other locations?). Build up of green house gases (longer term).

28 28 Sahel Semi-arid region below the Sahara Sahel data 1900 – 2007 Courtesy of IRI at Columbia University Dynamic climate largely due to oceanic oscillations Unlike the Sahel, climate change may lead to more permanent changes

29 (D) Climate Change Insurance Products? Generally weather insurance products involve localized conditions and are for only one year. Modeling climate drivers (e.g. teleconnections) to create forecast insurance may be the first regional climate insurance. At the current time, regional climate forecast insurance for 2 to 3 years may be as close as we can get to creating climate change insurance.

30 (D) Regional Climate Forecast Insurance Regional climate insurance can be used to help build resiliency. Most emerging economies have no regional climate insurance products. Due to an improved understanding of how teleconnections drive regional climate events, the science of forecasting extreme regional climate events is improving. GlobalAgRisk has developed the financial architecture in Peru for regional Climate Forecast Insurance – Extreme El Nino Insurance Product.

31 31 Source: lications/vg/africa/page/ 3105.aspx Teleconnection: El Niño and Sothern Oscilation

32 Piura and Other Areas in Northern Peru Severely Affected by 1998 El Niño Extreme rains (Jan – Apr 1998) 40x normal rainfall Severe floods 41x normal river volume Widespread losses Many disrupted markets Agricultural production, 1/3 Public infrastructure losses Cash-flow, debt-repayment problems Health problems Total losses in Piura estimated at USD 200 Million 32

33 Contract Is Written Using NOAA Data El Niño estimates derived from Satellite data, observations of buoys, and readings of the temperature on the surface and at deeper levels Data are publicly available monthly from NOAA (The U.S. National Oceanic and Atmospheric Administration) 33

34 34 Two extreme events in the last 32 years Strong El Niño in 1982–83 and 1997–98


36 The Nino Index is Negatively Correlated with Intensity of Atlantic storms and Hurricanes Copyright by GlobalAgRisk, Inc.

37 Example of a Payout from the 1997 Event Niño Region 1.2 (Nov–Dec) Temperature = 26.28°C Minimum Payment = 5% The insured selects the Sum Insured Sum Insured = 10,000,000 Soles 1998 Payment = 76% x 10,000,000 = 760,000 Soles 37

38 Primary Goal Improve Access to and Terms of Loans Capacity building with Financial institutions Peruvian banking regulator Peruvian credit rating agencies Sources of social capital flows into Peruvian institutions Case to be made 1) Strengthen resiliency of the financial institution 2) Financial institution can be ready to lend when the community needs capital the most Ex post After the disaster 38

39 ENSO effects in atmospheric conditions is significant– its impact is not anecdotal but physically established January-March weather anomalies and atmospheric circulation during El Nino and La Nina phases (D) Teleconnections and Regional Climate Forecasts

40 Precipitation and Temperature Anomalies associated with El Nino Teleconnections and Regional Climate Forecasts

41 Precipitation and Temperature Anomalies associated with La Nina (D) Teleconnections and Regional Climate Forecasts

42 What about IOD, NAO, PDO…etc? What is their signature? What about the combination of these indices and the feedback mechanisms? ENSO and IOD have positive feedback over Eastern Africa. IOD – Indian Ocean Dipole NAO – North Atlantic Oscillation PDO – Pacific Decadal Oscillation (D) Teleconnections and Regional Climate Forecasts

43 (D) How can Regional Climate Forecast Insurance be Climate Change Insurance? Getting cash before the impending disaster can motivate decision makers to take action to reduce the losses Most decision makers dont use forecast information in an efficient manner (prospect theory: regret) Taking action based on impending disasters will result in improved adaptation that builds resiliency As climate change occurs, if the frequency and the severity of the forecasted events increases, the rising price of the insurance will increase the dynamics of these adaptive management practices over time and should result in stronger systems to cope with climate change

44 44 Research Questions Does the spatial covariance of specific weather events change depending on the severity of the event? Does the covariance between specific weather events and realized losses change depending on the severity of the weather event? Does the covariance in losses across different livelihood activities change depending on the severity of the weather event?

45 45 Research Questions Does index insurance designed to protect against various consequential losses have lower basis risk than index insurance designed to protect against yield losses for a specific crop? (Will likely depend on the weather peril and local context.) Will potential buyers purchase index insurance based on novel (e.g., satellite-based) data sources? Will they purchase index insurance that protects against only catastrophic losses?

46 46 Research Questions In what regions can estimates of oceanic anomalies such as ENSO be used as forecasts for regional climate risk transfer? ENSO for regions other than Peru? Gulf of Guinea SST? North Atlantic Oscillation?

47 47 Conclusion Important research questions remain, we believe that: Wide spread scale-up will likely require alternative data sources. Index insurance should target consequential losses (not just crop yield shortfalls). Index insurance should target catastrophic losses. For the near future, risk aggregator products are likely the only feasible means of extending weather index insurance products into many regions of the world. There is potential to use climate forecast insurance to improve resiliency and adaptation. Developing regional climate forecast insurance will take time. The science of regional climate forecasting is improving and will enable more widespread application.

48 Sources Collier, B., B.J. Barnett, and J.R. Skees. State of Knowledge Report – Data produced by GlobalAgRisk, Inc. for the Bill and Melinda Gates Foundation. Available soon at Barnett, B.J., C.B. Barrett, and J.R. Skees Poverty Traps and Index-Based Risk Transfer Products. World Development 36: Collier, B., J.R. Skees, and B.J. Barnett Weather Index Insurance and Climate Change: Opportunities and Challenges in Lower Income Countries. Geneva Papers on Risk and Insurance Issues and Practice 34:401–

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