Index insurance: structure, models, and dataIndex insurance: structure, models, and data

Daniel Osgood (IRI)deo@iri.columbia.edu

Material contributed by:

Miguel Carriquiry, Ashok Mishra, Nicole Peterson, Megan McLaurin

The International Research Institutefor Climate and Society

Examples from groundnut in Malawi

Contract Structure

• Rainfall summed over 10 day periods (dekads)

• Dekadal maximum ‘cap’

• Sowing rainfall condition – Starts contract clock

– Or triggers ‘failed sowing’ payout

• Season split into phases

• Payouts each phase – From capped dekadal rainfall total over phase

Phase sum payout function

Payout = (1 – (Rainfall Sum – Exit) / (Upper trigger – Exit)) Max Payout

Phase Payout function 2006

0

25

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75

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0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Kwacha

Mv

ula

Ra

infa

ll (

mm

)

Nicole Peterson, CRED

Insurance Contract developed with Farmers

Contract parameters

• Sowing– Sowing window beginning, end

– Sowing trigger

– Failed sowing payout

• Phases– Number of phases

– Beginning, end of each phase

– Upper trigger, exit

– Maximum payout per phase

• Maximum total payout

New obligations with index products

• Traditional insurance--Triggered on loss– Pricing and financing on losses

– If payments not closely linked to losses• Provider and client both face consequences

– Adjuster is responsible for agreement

– Insurance providers experienced assessing losses

• Index insurance--Triggered on index– Insurer pricing and financing built on index

– If there is an error linking payments to losses• Only client faces consequences

– Contract must emulate adjustor

– Much more client interaction

Crops and Climate

• Crop models – Summarize the biological drought vulnerability

of crops during a season

• Well selected crop– Adapted for little vulnerability during the dry

spells in local climate

• Drought stress:– Combination of biology and local climate

Insurance contracts must address this balance

Financial features of insurance

• Deductible, payout frequency: – Insurance only protects against the largest losses– Insurance pays out rarely

• Insurance must target losses that are important in client’s risk management

– Client may prefer protection against 100 year loss, or 5 year loss

– Client may prefer protection against late season losses because sowing problems might be better addressed through practice changes

• Price constraints– Insurance must be affordable– Risk coverage must be most cost effective option

These features must be addressed in design

Water Stress Information

• Multiple information sources– WRSI

– Process based crop models (eg DSSAT)

– Historical regional yield

– Farmer and expert feedback

– Field trials

Each has strengths, limitations for design

WRSI

• Powerful tool for ‘water stress accounting’ – Well known– Assumptions intuitive – Results are accounting of

• Rainfall• With storage, loss assumptions

• Not best for direct yield simulation– Its developers at FAO use related statistical

techniques instead of model outputs for yields

• In contract design useful – Weigh relative water stresses due to crop genetics

and climate– Platform for communication of crop features in design– Starting point for contract parameters– Statistically link local climate to crop vulnerabilities

WRSI Issues

• Key parameter assumptions– Timing of growth stages is assumed– Relative vulnerability over season is assumed

• Limited capabilities—`Simple but honest’– Often inaccurate for small losses– Not accurate quantification of

• Risk faced by individual farmer

• Yield losses

– Excess water impacts not modeled– Crop failure is not modeled

Targets limited coverage to most important risk

Must verify using additional sources of info

Stress models

0.0

200.0

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600.0

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1000.0

1200.0

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Daily Yield Ave Ky

Daily Yield Ky(t)

DSSAT Yield

What is ‘truth’?

DSSAT and WRSI Simulated Yields and Historical Yields for Chitedze Groundnut Crop

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300

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1100

19

62

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64

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66

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00

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Year

Cro

p Y

ield

(k

g/h

a)

0.60

0.80

1.00

WR

SI c

rop

yie

ld (

at

1 s

ca

le)

DSSAT Crop Yield Historical yield WRSI

  Hist. Yields

  DSSAT 0.30

WRSI 0.35 0.52

Correlations

WRSI, DSSAT, Historical Yields

Insurance targets covariate risk

EPA Historical Groundnut Yields

0

200

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Year

Yie

ld (k

g/ha

)

CHILAZA

DEMELA

KAMBANIZITHE

MING'ONGO

MLOMBA

M'NGWANGWA

MPINGU

NTHONDO

SINYALA

UKWE

CHILAZA DEMELA KAMBANIZITHE MING'ONGO MLOMBA M'NGWANGWA MPINGU NTHONDO SINYALA UKWE

0.78 0.92 0.78 0.69 -0.52 0.69 0.74 0.89 0.81 0.89

Correlations with average yield:

Note: ~2-3 worst years most important for insurance

Questions for farmers and experts

• What are the best years and the worst drought years that you can remember?

– In which years did you have yield problems because of drought, and for each year, what was the reason for the problem

(eg dry sowing/weak start of rains or drought during the filling phase)?

• When do you typically plant?– When is the earliest that you have planted?– When is the latest that you have planted?

• What do you do if rains are insufficient for planting?

• For what growth phases is rainfall most important? – In what months?

• Do the historical payouts from this contract – Match the years you had reduced yields from drought?– Connect to the growth stage that your crops were in when

they were impacted?

Use of Water Stress Information Sources

• WRSI– Somewhat insensitive, direct product of assumptions– Good benchmark– Use as an accounting system for relative water stress, not a direct

simulation of yields

• Process based crop models (eg DSSAT)– Must be carefully calibrated– Data intensive– Representative of very specific situation– Good for identifying and understanding for losses missed by WRSI

• Historical regional yield– Not only water stress – Often low quality– Short time series– Different varieties, practices– Use to see if important historical losses are covered

• Field trials– Artificial production situation, very limited availability– Detailed and reliable specifics of crop/climate interaction

• Farmer and expert feedback– Qualitative, strategic– Use to tune and verify WRSI and model timing, gauge how well

coverage addresses important years for correct reasons – But remember it may be strategic, unreliable

Contract design?

• Different data sources--different information

• Because of moral hazard in traditional insurance:– Only naïve players show all of their cards

– We can only approximate client• risk preferences, productivity, self-insurance,

production details, microclimate, practices, consumption needs, hedging strategies, other sources of income, etc…

– Design is negotiation process

• Iterative statistical system for design• Strategic use of information