UKRAINIAN AGRICULTURAL WEATHER

RISK MANAGEMENT

WORLD BANK COMMODITY RISK MANAGEMENT GROUP

Ulrich HessJoanna Syroka PhD

January 20 2004

UKRAINIAN AGRICULTURAL WEATHER

RISK MANAGEMENT

WORLD BANK COMMODITY RISK MANAGEMENT GROUP

IFC PEP Ukraine

Ulrich HessJoanna Syroka PhD

January 22 2004

Weather Index Insurance for Agriculture

COMMODITY RISK MANAGEMENT GROUPThe World Bank

13th October 2006

William J. Dick

Overview of the Commodity Risk Management Group (CRMG), the World Bank

Index-based Weather Insurance

How to develop a Weather Insurance program?

Extending the index concept to flood insurance

OUTLINE

CRMG Overview

CRMG facilitates….

Market-based Risk Transfer Products Weather index-based insurance Price risk management contracts

New Applications Disaster risk financing Extension to new hazards

Access to risk capital Access to global reinsurance markets

Knowledge Transfer and Education Technical assistance in projects Publications and training workshops

Existing Transactions India, Ethiopia, Malawi, Ukraine.…

Feasibility Study Pilot Design Pilot Implementation

CRMG global activities

2001

2002

2003

2004

2005

2006

New Model: Index-based Weather Insurance

Motivation

Traditional crop insurance Multi-Peril Crop Insurance (MPCI)

• Yield-based insurance is not sustainable Named peril Crop Insurance

• Damage-based insurance is viable for selected localised perils

Main Problems Loss adjustment and farm level data Moral hazard Adverse selection due to asymmetric information High monitoring and administrative costs Often heavily subsidised Operationally difficult for small farmer agriculture

Experience with public crop insurance

Condition for sustainability:

(A+I)/P < 1 Where:

A = average administrative cost

I = average indemnities paid

P = average premiums paid

Country Period (A+I)/P

Brazil 75-81 4.57

Costa Rica

70-89 2.80

Japan 85-89 2.60

Mexico 80-89 3.65

Philippines

81-89 5.74

USA 80-89 2.42

Source: Hazell

Financial performance of crop insurance

Index insurance

ChallengeDesign an alternative, efficient and cost-effective crop failure insurance program that facilitates risk transfer and is feasible for small farmers in low-income countries.

What are index insurance contracts ?

An index insurance contract indemnifies based on the value of an “index”- not on losses measured in the field

An index is a variable that is highly correlated with losses and that cannot be influenced by the insured

Example indices: rainfall, temperature, regional yield, river levels

Index insurance contracts overcome most of the supply side problems of traditional insurance contracts

Main characteristics of an index

Observable and easily measured Objective Transparent Independently verifiable Able to be reported in a timely manner Stable and sustainable over time

Weather indexes can form the basis of an insurance contract that protects farmers from

weather risk

Payout per Hectare for Maize Drought Protection, Lilongwe Region

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0 10 20 30 40 50 60 70

Maize Rainfall Index

Pa

yo

ut

(MK

W p

er

hc

t)Payout structure: drought protection

Financial payout - increment

per mmMaximum Payout

Trigger Rainfall Level

Long-Term Average Rainfall

Deficit Rainfall Index (mm.)

EXAMPLE OF PAYOUT STRUCTURE

Pay

out

(uni

t pe

r ha

)

Index insurance: Advantages and challenges

Advantages Challenges

Less moral hazard and adverse selection Basis Risk

Timely payout Sustainability of the index

Lower administrative costs Precise actuarial modeling

Standardized and transparent structure Education

Availability and negotiability Market Size

Reinsurance acceptability Forecast

Versatility Micro climates

How can index instruments be used ?Micro level Weather-indexed insurance for smallholder

farmers, intermediated through institutions with rural outreach Ex. India, Nicaragua, Malawi, Ukraine

Meso level Weather-indexed portfolio hedge for rural financial institutions that lend to poor farmers Ex. India

Macro level Weather insurance or weather-indexed contingent credit line for governments or international organizations that provide safety nets for the poor Ex. Ethiopia, Malawi

The global market Deals transacted:

Argentina I – Weather insured seed credit Argentina II – Dairy yield protection against low rainfall South Africa – Apple co-operative freeze cover India – Approximately 250,000 insured against poor monsoon Mexico – Crop insurance portfolio reinsurance through

weather derivative structure Canada (Ontario) - Forage insurance with weather indexation Canada (Alberta) - Heat index insurance for maize Ukraine – Winter wheat protection against weather risks Malawi – Weather insurance pilot for groundnut farmers Ethiopia – WFP Drought Insurance

Under preparation: Morocco – Wheat yield protection against drought Zambia – Maize yield protection against drought Nicaragua – Bank-intermediated weather insurance for

groundnut farmers Thailand – Bank-intermediated weather insurance

How to develop a weather insurance program ?

Developing a pilot program

I. Identify significant farmer exposure to weather

II. Quantify the impact of adverse weather on their revenues

III. Structure a contract that pays out when adverse weather occurs

IV. Execute contract (with insurers and a delivery channel)

V. Secure international reinsurance

High Probability, Low Consequence Risks Vs.

Low Probability, High Consequence Risks

High probability

Low Consequence

Reduced yields

The producers generally perceive this as their risk

Normal weatherLow probability

High Consequence

Extremely low yields

Low probability

High Consequence

Extremely low yields

Extreme weather events (excess rainfall or flood)

Extreme weather events (droughts)

The cropping calendar

*Maize yields are particularly sensitive to rainfall during the tasseling stage and the yield

formation stage – rainfall during the latter phase determines the size of the maize grain

Diagram taken from the FAO’s maize water requirement report*

Sowing and establishment

period is also critical crop survival

• A rainfall index is normally split into 3 or more crop growth phases

• Objective: maximise the correlation between index and loss of crop yield

Maize Rainfall Index - example

Phase 1 Phase 2 Phase 3

Seedling Emergence to

Knee High

Vegetative Physiological Maturity

Days 30 21 30

Trigger (mm.) 35 50 60

Limit (mm.) 15 20 30

Tick size (Baht/mm./rai)

42 21 21

Sum Insured (Baht/rai)

1,200 1,600 1,700

Index v. maize yield example

Distribution and risk transfer

Bank-intermediated weather insurance contracts to farmers

Insurance Company/ Syndicate

Global Reinsurance Companies

Reinsurance treaty

National

International

Farmers

Weather insurance contractsWeather insurance contracts

Agricultural Bank

Contractual relationship Contractual relationship (risk transfer, services, operations etc.)(risk transfer, services, operations etc.)

Weather index insurance - summary

The product is simple and weather measurements can be understood by farmers

Basis risk can be reduced by increasing the density of low cost weather stations

Low cost of distribution and loss adjustment Less specialist knowledge needed to

underwrite the product The product is suited for catastrophe hazards The product is highly flexible and can multiply

in the insurance market Reinsurers are interested to accept the risk

Extending the concept to flood insurance

The flood risk in Asia

Flood insurance concept

Design a flood index which can proxy losses caused to crop

Rice is the strategic crop most exposed to flood Flood impact is dependent on variety, time of

occurrence, depth, speed and duration of flood water

Harness technology to support insurance underwriting and operations

2 key components for index design phase Flood modelling (FM) Agro meteorological modelling (AMM)

2 key components for operational phase Geographical information system Earth Observation (EO)

Pasak

River

LA4

LA2

LA3

LA1

LA5

“High Risk” Pricing Zone“Medium Risk” Pricing Zone

“Low Risk” Pricing Zone

Summary: Combining the Technology Components

FM + AMM Design a flood index that proxies crop loss

FM+EO+GIS

Define flood risk zones and pricing the contract

EO+ GIS Loss adjustment for payout determination according to the index

FM: flood modelling. AMM: Agro-meteorological modelling. EO: Earth observation. GIS: Geographical Information System.

Remote sensing can measure flooded areas

Flood assessment based on SAR - Bangladesh 07/2004

River gauges

Flood map

Challenges in indexing flood risk

Types of flood risk River inundation flood Flash flood Typhoon induced flood Coastal surge floodChallenges Zoning for insurance purposes Defining “macro” or “micro” level insurance products Pricing flood risk Influence of flood management practices on risk Avoidance of anti-selection Simplifying the product CRMG is still in the research phase Thailand, Vietnam and Bangladesh