Drawing from Lessons Learned on Index Insurance to Consider Financing Famine Relief Efforts Dr....

Drawing from Lessons Learned on Index Insurance to Consider Financing Famine Relief Efforts

Dr. Jerry SkeesHB Price Professor, U of KYPresident, GlobalAgRisk, Inc

jskees@qx.net www.GlobalAgRisk.com

Defining the Problem

• Famine & Hunger are complex social problems created by numerous interrelated factors

Low incomesBad governmentsChronic vs TransitoryCrop failures (weather driven)High prices due to local shortages

Defining the Problem

• The solutions today can help with:Crop failures (weather driven)High prices due to local shortagesTransitory shortages

Less clear that they can help with:Low incomesBad governmentsChronic shortages

The most common response

• Emerging food aid has limitationsStorage problemTransport problemDependency problemTiming problemBad government problemPolitical issue among the developed

countries

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

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

1) Grain options

2) Revolving import compensation fund

3) Import insurance

Conclusion: All would cost significantly less

$300-$600 million per year vs $2.9 Billion

‘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

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

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?

Cost Always Exceed Premiums

Country Time Period (I + A) / P Brazil 1975-81 4.57 Costa Rica 1970-89 2.80 Japan 1947-77 2.60 1985-89 4.56 Mexico 1980-89 3.65 Philippines 1981-89 5.74 USA 1980-89 2.42 1999 3.67

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

Hypothetical Loss Functions for Different Risks

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 2 3 4 5 6 7

MPCI

Private Hail

Loss Ratio (Indemnity /

Financial and Technological Innovations Pave the Way

Financial Innovations• Weather markets• Index based insurance• Catastrophe bonds• Blending capital markets with reinsurance

marketsTechnological Innovations• Satellites are measuring weather• Satellites images on vegetative cover• Ground level real-time weather• Computer models to give early warning (LEWS)

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

Catastrophic BondsDebt 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 1994..over $10 Billion in deals• Fund managers like CAT Bonds as they

are not correlated to other equity markets

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!

Area Yield Insurance

• 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.

US Group Risk Plan

• Payments are strictly based on estimates of county yields

0

20

40

60

80

100

120

140

160

1960 1965 1970 1975 1980 1985 1990 1995 2000

Need historic data to develop the PDF

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00 180.00 200.00

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

Changing the Loss FunctionBlue is Loss Function for Unsupported MPCI at 60%

Black is Loss Function for Residual MPCI Product at 60%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 20000000 40000000 60000000 80000000 100000000 120000000

Cum

ulat

ive

Prob

abili

t

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

Romanian Summer Rainfall

0 100 200 300 400 500 600

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

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?

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

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

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.

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

Mexico

• Same infrastructure can be used

1) To sell direct to farmers

2) To reinsure the crop insurance program

3) Sold to collective groups (Fondos)

4) Used for natural disaster relief (Funden)

Wider Press Chapter

Can Financial Markets be Tapped to Help Poor People Cope with Weather Risks?

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

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)

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

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

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?

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

Global Livestock CRSP - LEWS

Dr. Jerry Stuth, PI

• 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

Biophysical Models, Technologies and Spatial Analysis Tools Currently Used in the LEWS Project Process

Systems can focus on local

problemsGrid of

12 x 12 km

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)

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

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

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

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?

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)

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