Scaling Crop Weather Index Insurance

Lessons from India

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Basis Risk: The primary hurdle

Imperfect relationship between index & targeted loss

Distance from the settlement metrological station

Differing scales of risk faced by insurer & farmers

Time averaged meteorological indices makes farmersresponsible for relating them to production losses 2

3

Exogenous Meteorological Indices

Not adjusted to farms yields (cumulative rainfall)

Extremely easy to design

Carries high basis risk

Yield Tailored Meteorological Indices

Designed using regression of yield on weather pattern

Maximum average risk reduction.

More than one weather parameter can be used

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0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 2 4 6 8 10 12 14 16 18

wat

er

usg

ae

week after emergence ->

Av corn water use based on maximum daily air temp.

50° - 59°

70° -79°

90° -99°

Water Stress based loss models are the good aids for their ease of use.

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Risk assessment factors at a development stage

Vulnerability to a particular weather peril

Prevalence of the said weather peril

Microclimate factors

Corn Soil Moisture Stress Criteria

Growth Stage Period Vulnerability

Emergence – Onset of tassels

40 days Can withstand up to 60% soil water depletion

Tassel onset - Blister kernel stage

40 – 80 days Root zone not more than 50% water deficient

Post Blister kernel stage > 80 days Can withstand 60% water depletion with out yield reduction

Growth stage Evapotranspiration(inches per day)

Percent yield loss per day of stress (min-ave-max)

Seedling to 4 leaf 0.06 ---

4 leaf to 8 leaf 0.1 ---

8 leaf to 12 leaf 0.18 ---

12 leaf to 16 leaf 0.21 2.1 - 3.0 - 3.7

16 leaf to tasseling 0.33 2.5 - 3.2 - 4.0

Pollination 0.33 3.0 - 6.8 - 8.0

Blister 0.33 3.0 - 4.2 - 6.0

Milk 0.26 3.0 - 4.2 - 5.8

Dough 0.26 3.0 - 4.0 - 5.0

Dent 0.26 2.5 - 3.0 - 4.0

Maturity 0.23 0

Estimated corn evapotranspiration & yield loss per stress day during various stages of growth.

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The index is triggered when a pre defined weatherevent occurs which is potentially damaging.

A good index should capture all the damage points.

Should account for the demanded protection by farmers

The triggers for a product will depend on the indextype. Eg. In a water deficit index distribution ofrainfall is more important consideration than thetotal volume

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DeficientRainfall

Reduction in rainfall

Kharif grain production fall

1982 -14% -12%

1987 -19% -7%

2002 -19% -19%

2009 -23% -16%

Premium= Expected Loss + Risk Margin + Admin Charges

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Expected Loss is the average payout for the product in anyyear

Risk Margin is to compensate the insurer for taking the risk ofunexpected payouts

Administrative charges like marketing cost, taxes, reinsurance& brokerage charges.

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Pricing using Burn Analysis

Straightforward & simple. Good for 1st look.

Few assumptions, hence lesser error points

Low level of accuracy

Pricing using Index Modeling

More complex.

A distribution is fitted to underlying weather indices

To be preferred when long data series is available.

Modeling error can lead to greater level of inaccuracies

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Risk Margin: Dependencies

Trends in weather pattern

Missing historical weather data

Unprecedented weather events

Current pricing methodologies uses VaR of thecontract to determine the risk margin.

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“Actual” Premium depends on many factors

Actuarial premium

Economic profile of the target farmers

Marketing Cost

Marketing cost comprise almost 10-15% of premium in retail sales.

Willingness to purchase weather insurance

Deductibles

Insurance schemes associated with contract farming operations have lower rates.

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The promise of Weather Insurance

Fast claim settlement

Independently variable loss data

Hassle free claim settlement process

Roadblocks to efficient claim handling

Settlement Data issues

Administrative inefficiency

Very few bank account holders amongst the farmers

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Settlement Data Issues

Failure of primary weather stations

Inadequate indemnity when compared to actual loss

Primary St.

Secondary St.

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Admin. Inefficiencies in claim handling

3rd party observers take time to release verified data

Data is processed at multiple levels

Admin issues (claim verification, cheque printing)

Cheque distribution to individual farmers

Obstacles to Acceptability

Complex models can be considered opaque by the farmers

The farmers may view the process to be vulnerable tomanipulation

Farmers perceive premium amount lost in case of no payout.

Needed a balanced trade off between basis riskand farmer’s ease of understanding.

Obstacles to a Sellable product

Poorly trained ground staff often unable to explain theproduct to farmers

Limited numbers of “Trusted” Point of Sales.

Below par post sales service.

Needed a balanced trade off between basis riskand communication challenges. 17

Handling Product Complexity

Split product according to multiple weather variable for ease

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Lesson Learned from India

Handling Trust Issues

Local agro-input dealers, cooperatives, NGOs as POS

Handling Service Quality Issues

Daily weather forecast, actual data & agro-advisory messages

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GIS in weather index insurance

Vulnerability map of a region

Can help cut down location specific basis risk

Can help in claim settlement process

Rainfall & topography to calculate runoff

Usage in loss calculation

To model evapotranspiration for arid regions

Flood Inundation

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TERMSHEET FOR WEATHER INDEX INSURANCE FOR LAC

Peril Type 1 Damage due to high fluctuation in Max temperature

Peril Period 1 Feb 1 to Feb 28

Varying Temp Index (VTI) Cumulative value of deviation in daily temperature over the Peril period

VTI Strike 35

Notional 2%

VTI Index ( HDD - Strike ) * Notional

Peril Type 2 Damage due to high temperature

Peril Period 2 Mar 16 to Apr 15

High temp Index (HTI) HDD of Max temp above 37* C

Loss rate in % HDD

1st 15 days 2nd 15 days

15 4% 2%

25 8% 4%

Loss Cap 70% 30%

Index VTI + HTI

Index Threshold 30

Notional (Rs./unit) 20

Max Sum Insured 900

Premium (incl Service Tax) 125

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TERMSHEET FOR WEATHER INDEX INSURANCE FOR SALT PRODUCTION

Risk Index Excess Rainfall

Wet Spell Strike 2 consecutive days of rainfall greater than 15 mm

Wet Spell Exit 3 consecutive dry days

Policy PeriodPhase 1 Phase 2

10 Jan – 15 Mar 16 Mar – 31 May

Index_1 (Production losses due to wet spell)

Loss index in %

15mm < Rain ≤ 50mm 3% 3%

50mm < Rain ≤ 100mm 8% 8%

100 mm < Rain 15% 25%

Index_2 (consequential losses for each rainy day)

Extra loss for each rainy day (Eloss) 0.40% 0.80%

Index_2 = Wet Days Count X Eloss

TLI { Index_1 + Index_2 - 25% } Notional (amount paid for each point of TLI)

0 < TLI ≤ 25 50

25 < TLI 105

Payout = TLI X Notional

Sum Insured = Rs. 6500 Premium = Rs. 800

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Excess Rainfall Index (ERI)

Stages Initial Phase Dev Phase Mid Phase End Phase

Dates 15 Jul – 13 Aug 14 Aug – 17 Sep 18 Sep – 22 Oct 23 Oct – 21 Nov

Excess Rainfall Index

ERS 1 150 150 125 100

ERS 2 25 25 15 15

ERC 1 Cumulative rainfall of two consecutive days – ERS1

ERC 2If ERC_1 > 0, sum of rainfall of subsequent days till rainfall < ERS2 for two consecutive days

Payoff ∑ {(ERC 1 + ERC 2) PHASE X Notional PHASE }

Maximum payoff 2000

TERMSHEET FOR WEATHER INDEX INSURANCE, COTTON (AP)

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Water Deficit Index (WDI)

Stages Initial Phase Dev Phase Mid Phase End Phase

Precipitation Weight 1.5 1.5 0.5 NA

Index Strike 400

Notional (Rs.) 4

Payoff formula Max [0, (Index Strike – Actual Index) X Notional]

Max Payoff 2000

Loss Calculation

Deductible Rs. 200

Max S.I. 4000

Total Payoff Max (4000, payoff for ERI+ payoff for WDI – Deductible)

Premium 400

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