Index insurance: structure, models, and data

1. Index 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

2. Examples from groundnut in Malawi

3. 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

4. Phase sum payout function Payout = (1 – (Rainfall Sum – Exit) / (Upper trigger – Exit)) Max Payout

5. Insurance Contract developed with Farmers Nicole Peterson, CRED

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. Stress models What is ‘truth’?

14. WRSI, DSSAT, Historical Yields Correlations

15. Insurance targets covariate risk Correlations with average yield: Note: ~2-3 worst years most important for insurance

16. 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?

19. 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

20. 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