Applications to Agriculture and Other Sectors : The Role of Weather Index Insurance

1. Applications to Agriculture and Other Sectors: The Role of Weather Index Insurance Dr. Jerry Skees H.B. Price Professor, University of Kentucky, and President, GlobalAgRisk, Inc. US CLIVAR/NCAR ASP Researcher Colloquium Statistical Assessment of Extreme Weather Phenomena under Climate Change NCAR Foothills Lab, Boulder, Colorado, USA June 13-17, 2011 Special Thanks to GlobalAgRisk Team!

2. GlobalAgRisk, Inc. • Mission • Improve access to financial services and the value chain for the rural poor through innovative approaches for transferring weather risk • Select Country Work • Peru – El Niño/Flood • Mongolia – Livestock • Vietnam – Flood/Drought • Indonesia – Earthquakes • Mali – Drought • Morocco – Drought • Mexico – Drought • Romania – Drought • Ethiopia – Drought • Activities • Research and development tied to University of Kentucky research program • Technical capacity building • Educational outreach • Supported by • Multinational donors • Governments • Nongovernment organizations

3. Current Support for GlobalAgRisk, Inc. • Bill and Melinda Gates Foundation (Peru) • Ford Foundation (Vietnam and Indonesia) • Gov’t of Mongolia via Swiss Trust Fund • UNDP (Peru) • GiZ (Peru) • Risk Management Agency of USDA • Actuary and Underwriting Reviews

4. State of Knowledge Reports from GlobalAgRisk Supported by Bill and Melinda Gates Foundation Innovation in Catastrophic Weather Insurance to Improve the Livelihoods of Rural Households • March 2010 • “Data Requirements for the Design of Weather Index Insurance.” • March 2011 • “Market Development for Weather Index Insurance Key Considerations for Sustainability and Scale Up.” • Under Revision: • “Legal Considerations for Index Insurance” • Forthcoming a book

5. Somebody Always Pays for Catastrophic Risk Who? How? Society needs to understand the cost of natural disaster risk Someone always pays Need incentives for proper risk management and mitigation The poor pay through direct losses and long-term economic impacts Financial institutions restrict services as they learn that the correlated losses of many of their borrowers and savers create significant banking problems Governments — Disaster relief and recovery expenses, infrastructure investments, subsidized agricultural insurance Donors forgive debt and divert funds for recovery

6. Designing Sustainable and Scalable Weather Index Insurance Programs Is Challenging • Products must be developed in context • Costly (technical support, capacity building, R&D) • Not easily replicable • Basis risk • Tradeoff between transaction costs and basis risk • Limited or no data to develop products • High delivery costs • Small transactions/Small market volume • Nascent legal and regulatory systems

7. Lessons LearnedRisk Assessment • Understanding vulnerability necessitates examining a specific context: • The risk, geography, demographics, risk management strategies, government policies, etc. • Risk assessment requires a systems approach • Proper risk assessment benefits decision makers and can guide policy decisions • Cognitive failure – decision makers often underestimate extreme risk of natural disasters

8. AdaptationMultilateral Organizations Call for Insurance • Decision makers suggest insurance can play an important role in adaptation to climate change • Article 4.8 of the United Nations Framework Convention on Climate Change (UNFCCC) • Article 3.14 of the Kyoto Protocol • The Hyogo Framework • Agricultural insurance, in particular, gains increased attention because agriculture is the primary livelihood of households, especially the poor, in developing countries

9. Insurance and Adaptation • Insurers assess specific risks • Insurers price the risk – a critical aspect of risk assessment • Insurance can encourage adaptation through pricing – if insurance costs that much, maybe I should do something different! • Access and price of insurance can be directly linked to adaptation strategies • Fire insurance: extinguishers in the house? • Proximity to a fire hydrant • Improved building codes • Insurance reduces variability in wealth/revenues

10. Pricing the Risk“Fairness” and Perverse Incentives • If insurance is not priced to reflect differences in relative risk (or if more subsidies are paid to higher risk areas), perverse incentives are likely to follow – if you pay people to take risk, they will take more risk • The result will be more risk exposure rather than less • Higher risk activities • Higher risk areas • Less investment in risk reduction

11. In the U.S. Subsidies Have Been Used to Lower Loss Ratios

12. Increased Risk-Taking • As a result of purchasing insurance, farmers tend to engage in riskier behavior (plant higher risk crops, monoculture, less irrigation, etc.) • Difficult for insurer to monitor these changes in behavior • Increased risk-taking increases loss ratios • High loss ratios lead to higher premium rates • Higher premium rates reduce insurance purchasing • Higher premium subsidies are then needed to maintain or increase insurance purchasing

13. U.S. Agricultural Policy Has Also Favored More Risk Areas • If you pay people to take on more risk; they will take on more risk! • Program yields for commodity payments are based on planted acre yields (not harvested acre yields); favored areas with large abandoned acres • Ad hoc disaster payments (long history) • Highly subsidized crop insurance • Premium subsidizes are a percent of premium: Once again favoring high risk areas

14. Premium Subsidy per Dollar of Liability2002

15. Loss Ratios Vary Across the Country1981–2002

16. Change in Crop Share

17. Farmer Knowledge and Decision Processes • Literature shows • Farmers optimize • Farmers adapt • Farmers are good Bayesians • Farmers know central tendency on yields • Cognitive failure sets in for catastrophic events • Challenge for adaptation • Communicating information about climate change in a fashion that is useful for decision makers

18. Lessons LearnedPricing the Risk • Pricing risk is an important aspect of insurance • Insurance can encourage households to reduce risks • Government policies can motivate households to take more risk at government’s expense • Agricultural insurance is extremely complex • Risk classification is difficult • Risk varies by commodity • Risk varies by region • Risk varies by producer • Difficult to monitor farmer behavior • Administrative cost is very high

19. Weather Index Insurance: Gaining Popularity in Developing Countries • Payouts are based on a measurement of an event correlated with losses • Weather event (e.g., rainfall, temperature, or river levels associated with yields) • Insurance pays when index crosses a certain threshold • Payouts do not require inspection of client losses • Eliminates most information asymmetry problems • Transfers extreme risk to reinsurers

20. 4000 0 500 1000 2000 3000 Example of an Excess Rainfall Insurance Product • Extreme rainfall in India – payments would occur anytime rainfall exceeds 2000 mm • Household might buy US$100 in liability

21. Payout Structure for Example Excess Rainfall Contract • $1 for every 10 mm excess of 2000 mm • $100 limit at 3000 mm

22. Pricing Index Insurance Price of Index Insurance = Pure Risk + CAT and Ambiguity Loads + Risk Financing + Delivery + Education/Marketing + R&D + Farm level/Asymmetric Information Problems • Adverse Selection • Moral Hazard • Loss Adjustment

23. Index Insurance Creates Flexibility for Different Users • Households • India: Drought coverage to groundnut farmers • Malawi: Cooperative links insurance to farmer loans for high-yield seed varieties • Mongolia: Index-based livestock insurance • Firms (e.g., Banks/input suppliers) • Increase access to credit and other services • Vietnam and Peru: Possible use of index insurance for protecting credit risk • Governments/Donors • Mexico: State governments provide quick drought relief to farmers in need using a drought index

24. Sahel: Shifts in the Central Tendency Sahel data 1900 – 2007 SahelSemi-arid region below the Sahara • Dynamic climate largely due to oceanic oscillations • Unlike the Sahel, climate change may lead to more permanent changes

25. Sahel: Hypothetical Insurance Product Insurer entry point at 1962, 1990, and 2007 to develop a drought insurance Insurer would re-center data arounda current forecast of the central tendency

26. Sahel: Rainfall Distribution Using Data from 1900 to 1961 (Reference Point – 1962) • Insurance contract for wheat farmers • Indemnities when rain is below 425 mm • Central Tendency: 510 mm • Payout Threshold: 425 mm • Pure Risk: 2% Pure risk will likely result in insurance affordable to households

27. Pure risk would make insurance unaffordable for households Sahel: Rainfall Distribution Using Data from 1962 to 1989 (Reference Point – 1990) • Central tendency is below payout threshold • Insurance is inappropriate in this setting • Central Tendency: 328 mm • Payout Threshold: 425 mm • Pure Risk: 44%

28. Sahel: Rainfall Distribution Using Data from 1990 to 2006 (Reference Point – 2007) • Increases in rainfall reduced the weather risk • Insurance would be affordable depending on other costs (Delivery, ambiguity loading, etc.) • Central Tendency: 456 mm • Payout Threshold: 425 mm • Pure Risk: 6% Pure risk may result in affordable insurance for households

29. Insurers expecting climate change greatly increase ambiguity and catastrophe loads Climate Change Increases Ambiguity and Catastrophe Loads • Misestimating the central tendency is very costly Green distribution Insurer forecast in 1962 Blue distribution Forecasted loss experience based on 1990 Reference

30. Adaptation, Policy Interventions, and Climate Change • Households must adapt or experience increasing farm losses • Change farming practices • Invest in infrastructure • Transition out of farming • Insurance, by itself, is not a means of adaptation • Policy interventions should assess the opportunity costs when supporting insurance programs – other adaptation investments may be more important • Insurance can be used to facilitate adaptation (e.g., linking insurance, credit, and improved seed varieties in Malawi)

31. Premium Subsidies Will likely Slow Adaptation • Significant caution is needed for insurance premium subsidies • If climate change shifts the central tendency, premium subsidies could slow adaptation • Households account for subsidies when comparing profits from farming and other activities • These subsidies can encourage farmers to take more risk and delay adapting to climate change

33. How to Improve Access to Catastrophic Weather Insurance? Our Experience Suggests . . . Index insurance is best suited for catastrophic and consequential losses Index insurance that addresses weather risk of firms that serve the poor (risk aggregators) presents a feasible avenue for market growth; build a sustainable market first and then move to micro products Household products must find innovative delivery mechanisms to improve product affordability and offer value to clients (insurance-linked products) Solutions that involve public-private partnerships must clearly delineate the role for markets and the role for government Understanding cognitive failure for extreme risk can help Risk layering – Putting catastrophic insurance into a broader conceptual framework

34. Risk Aggregator Products Are Less Costly to Develop and Implement than Household Products • Risk aggregator products face lower basis risk • Risk aggregators effectively diversify much of the idiosyncratic risks born by their clients • Data constraints are less binding for risk aggregator products • It is more cost effective for the insurer to establish a partnership with a risk aggregator than to market and distribute products to small holders • Risk aggregators are more likely to understand hedging and basis risk

35. El Niño Insurance for FloodInnovation in Northern Peru

36. Piura and other areas in the NorthSeverely 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

37. Contract is Written Using NOAA Data • Nino estimates are derived from satellite data, observations of buoys and readings of the temperature on the surface and at deeper levels. • The data are publicly available monthly from NOAA (The U.S. National Oceanic and Atmospheric Administration) • http://www.cdc.noaa.gov/Correlation/nina1.data

38. Strong El Niño in 1982-83 and 1997-98 2 extreme events in the last 32 years

39. 37 31

40. Correlation Matrix for top 10 percentile El Niños

41. Example of a Payout from the 1997 Event • Nino 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

42. 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 • Strengthen the resiliency of the financial institution • Financial institution can be ready to lend when the community needs capital the most – post disaster

43. Risk Aggregator StrategyNatural Disaster Effects on Banking • Loan portfolio — Systemic repayment problems for borrowers, problems can remain for years • Deposits — Depositors withdraw funds • Costs increase — Costs of funds (e.g., Interbank loans), administrative costs • Resulting problems • Liquidity • Profitability • Capital Adequacy • Lending institutions have many ways of managing these risks (e.g., Provisions, restructuring loans, etc.)

44. 1997–1998 El Niño Spike and Recovery • 10% Spike • 3.5-Year Recovery P R O B L E M L O A N S With this event every 1 in 15 years, 300 basis points must be added

45. Default Risk Significantly Affects Interest Rates! Example (10% default risk) r = 10% p = 90% Example (No default risk) r = 10% p = 100% π – Expected profits p – Exogenous probability of non-default i – Interest rate r – Lender’s opportunity costs L –Amount of funds loaned El Nino may add 300 basis points to interest rates 45

46. Historical Pattern of Agricultural Lending in Piura1994–2006 Lenders say they have“fixed the problem” by not making loans when they see El Niño coming El Niño

47. El Niño Reduces Capital Adequacy and the Ability to Leverageand Make Profits

48. Comparison of Sum Insureds using Monte Carlo % is the sum insured corresponding to a percent of credit portfolio e.g., Sum insured = 5% of credit portfolio

49. Risk Assessment Includes Evaluating Current Risk Management Strategies • Potential strategies for managing these risks and their costs • Liquidity  Hold higher portion of assets in cash • Effect — Reduces investment in productive assets • Profitability  Avoid exposed regions and sectors • Effect — Limits growth opportunities, especially for untapped markets • Capital adequacy  Leverage a lower amount of equity to provide a “cushion” for the risk • Effect — Limits growth

50. 50