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Dr. Jerry Skees HB Price Professor, U of KY

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Presentation on theme: "Dr. Jerry Skees HB Price Professor, U of KY"— Presentation transcript:

1 Drawing from Lessons Learned on Index Insurance to Consider Financing Famine Relief Efforts
Dr. Jerry Skees HB Price Professor, U of KY President, GlobalAgRisk, Inc

2 Defining the Problem Famine & Hunger are complex social problems created by numerous interrelated factors Low incomes Bad governments Chronic vs Transitory Crop failures (weather driven) High prices due to local shortages

3 Defining the Problem The solutions today can help with:
Crop failures (weather driven) High prices due to local shortages Transitory shortages Less clear that they can help with: Low incomes Bad governments Chronic shortages

4 The most common response
Emerging food aid has limitations Storage problem Transport problem Dependency problem Timing problem Bad government problem Political issue among the developed countries

5 Proposition: Getting cash to market participants before a transitory food shortage problem emerges is a superior food assistance program Cash is fungible it can be used for any food stuffs to mitigate the problem

6 Searching for Solutions that get cash into the country
“Food Insecurity in the Least Developed Countries and the International Response” By Michael Trueblood and Shahla Shapouri This paper compares 3 alternatives Grain options Revolving import compensation fund Import insurance Conclusion: All would cost significantly less $300-$600 million per year vs $2.9 Billion

7 ‘Insurance’ based solutions from Trueblood and Shapouri
All involve protecting the cost of imports at some level Each is focused on the price side We consider insurance that protects the supply side

8 Insuring crop/pasture failures
Issues: Traditional crop insurance is a failure Crop failures represent correlated losses; in a classic sense they are not insurable Financial innovations are creating new opportunities Technological innovations enhance those opportunities

9 Traditional Crop Insurance
A failure Moral hazard/ adverse selection / high monitoring and administrative cost No successful crop insurance in the world when one measures the total cost of the program versus the transfers Have we targeted the wrong level?

10 Cost Always Exceed Premiums

11 Consider Drought Insurance
A frequent event (1 in 5 / 1 in 7) with high correlated losses.. Everyone can have a wreck at the same time Loss function has a thick tail to the right with frequent-heavy losses much more likely than with earthquakes Classically NOT an insurable risk! Cause of loss is not easy to verify as a combination of events can cause a crop loss

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13 Financial and Technological Innovations Pave the Way
Financial Innovations Weather markets Index based insurance Catastrophe bonds Blending capital markets with reinsurance markets Technological Innovations Satellites are measuring weather Satellites images on vegetative cover Ground level real-time weather Computer models to give early warning (LEWS)

14 Recent Market Innovations for Catastrophes
Catastrophic Bonds in the equity markets Catastrophic Insurance options on the CBOT Crop Insurance Yield contracts on CBOT Over the Counter index trades Temperature Contracts on the CME Weather markets and agriculture

15 Catastrophic Bonds Debt instrument or Equity instrument?
Those at risk have a contingent claim on the Bond if the catastrophe occurs You give me your capital .. I give you a high rate of return unless the catastrophe hits… then I either reduce your return or take your capital Since over $10 Billion in deals Fund managers like CAT Bonds as they are not correlated to other equity markets

16 Area Yield Insurance Essentially, an option on county yield.
Indemnity does not depend on farm-level yield! No moral hazard. No adverse selection. Low transactions costs. Geographic basis risk!

17 Area Yield Insurance Need: Don’t need: County yield history.
Independent party to measure county yield for insured crop year. Don’t need: Farm yield history. Farm yield for insured crop year. Compliance officers. Loss adjusters to measure farm-level losses.

18 US Group Risk Plan Payments are strictly based on estimates of county yields

19 Need historic data to develop the PDF

20 Paying on Index Contracts
Expected county yield =100 Payment is based on percentage below a trigger yield EX: Payments begin yields of 90 or less Actual yield = 70 Percentage = (90-70)/90 = 22.2% Payment = Liability Selected x .222% Premium= Premium rate x Liability

21 Changing the Loss Function

22 Alternatives to Area Yields
Is there an objective index that is highly positively correlated with area yields and farm yields? Weather variables: Rainfall. Temperatures. Satellite images

23 Romanian Summer Rainfall

24 Romanian Rain (drought or excess rain)
Strike for drought at 100 mm or below Strike for excess rain 100 mm or above Simple contract We will pay for every 1 mm of rainfall below 100 mm. You decide where to stop payment and the maximum level of insurance value

25 Premium Premium rates are driven by the PDF and actuarial procedures for loading rates Premium payment = Liability x Premium rate Question: How does one determine how much liability to purchase? What is at risk?

26 Index-Based Insurance Products
Example: Farmer purchases an insurance policy that will pay an indemnity if cumulative precipitation measured at a given location is below a specified level over a period of time. Indemnities are not based on farmer’s yield; they are paid on an independent source of information

27 Index-Based Insurance Products
Advantages: No moral hazard. No adverse selection. Low administrative costs (no individual farm loss adjustments). Easy to understand. Protects against correlated risk

28 Weather Index Insurance
Need: Reliable historical weather data for a given weather station. Secure and objective source of current weather measurements. Don’t need: Farm yield history. Farm yield for insured crop year. Compliance officers. Loss adjusters to measure farm-level losses.

29 Potential Applications
Weather index insurance can be: Sold to households at risk Sold to importers Sold to governments for disaster aid Sold to groups of households Sold to agribusinesses Used for commercial risk and for emergency assistance

30 Mexico Same infrastructure can be used To sell direct to farmers
To reinsure the crop insurance program Sold to collective groups (Fondos) Used for natural disaster relief (Funden) Wider Press Chapter Can Financial Markets be Tapped to Help Poor People Cope with Weather Risks?

31 Mexico Case Study December 2001, Agroasemex was the first emerging economy ever to use weather derivatives to reinsure the Mexican crop insurance program Motivation: Obtain a price for the upper layer of reinsurance (the biggest risk) was lower than other alternatives in the market Much more activity in Mexico now to use weather measures for disaster payments and insurance

32 Countries Argentina (use area yield for disaster pay)
Morocco (rainfall insurance this fall) Mexico (first reinsurance with weather) Canada (Alberta & Ontario use rain) Mongolia (to use mortality rate of animals) Ukraine (progress toward using rain) Romania (recommended area yield)

33 Linking Rainfall Insurance and Water Markets
Rain feeds the system of reservoirs Rainfall insurance sold to the Irrigation Authority (IA) offers new opportunities IA could sell quota rights to water with 3 characteristics: 1) ownership and right to use water; 2) right to lease water; and 3) a guarantee that replaces lost water with insurance payments

34 Linking Rainfall Insurance and Water Markets
How does a quota with these characteristics change the political economy of water markets? IA sells these quotas to obtain the capital needed to make infrastructure improvements Burden is on IA to ‘fix things’ and make certain that they can deliver water to quota owners The IA reinsures the indemnity payments with the rainfall insurance

35 Moving to a Proposal for Famine
Use the early warning systems to index emerging problems and offer index insurance Issues Who will pay premium? Who should purchase? How might such a system be implemented?

36 Livestock Early Warning System for East Africa.
Global Livestock CRSP - LEWS Livestock Early Warning System for East Africa. …..blending monitoring/modeling and spatial technologies to improve food security of pastoral communities in East Africa Lead Institution:Texas A&M University System Dr. Jerry Stuth, PI

37 Biophysical Models, Technologies and Spatial Analysis Tools Currently Used in the LEWS Project Process PHYGROW - hydrologic based, spatially explicit multiple-species plant growth/hydrology/animal grazing model. NUTBAL - nutritional balance analyzer used to assess nutrient requirements, nutrient intake, milk production, and performance in cattle, sheep, goats and horses with least cost mediation solutions.. Near Infrared reflectance spectroscopy of (NIRS) – Allows fecal profiling of livestock to determine the quality the forage recently consumed prior to defecating. Spatial Characterization and GIS tools - GPS units, ACT, ArcView, GS+ Satellite Imagery - NOAA RFE weather and EROS NDVI data

38 Systems can focus on local problems Grid of 12 x 12 km

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40 Using Early Warning Systems for Insurance Contracts
These systems index the deviations from normal These systems give early warning (up to 90 days) Insurance model could be indexed with deviations from normal and the early warning information Insurance would likely have layered payment structure (1 early payment with another payment should certain excess conditions be meet)

41 Who Could Purchase? Governments
NGOs who want resources when there is a serious problem Importers within the country (remember.. If they are concerned about price increases they should purchase more liability) Microfinance entities within the country for local problems Villages / households

42 Who will pay? Some level of payment could come from the G-8 (for the worst catastrophes) Some level should come from the end-users Some payments could be in the form of food stamps Some payments could come from NGOs Charity Catastrophe Bonds

43 Who will supply these index contracts?
A consortium of international reinsurers Investment banks via famine CAT Bonds Charity CAT Bonds Keeping some market base is important! Relative risk pricing.. Proper design of contracts Pooling of global risk to make undiversifiable risk diversifiable

44 Is this doable? Yes.. I have visited with some key market makers: there is an interest Developing such a system helps them spread global risk / helps them with an enhanced social image What is needed?

45 Rules for Successful Indexes
Easy to understand Replication Frequency of Publication Representative of True Economic Value Break-down of alternative hedging (Drs. Richard Sandor and Joseph Cole)

46 Benefits Gets cash to important stakeholders in the developing country: BEFORE the problem gets too serious Paves the way for more risk management instruments by providing the important infrastructure for a variety of commercial and social risk problems Enhances the opportunity to spread global risk


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