Index Insurance for Small-holder Agriculture:

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1 Index Insurance for Small-holder Agriculture: What We Have Learned about Impacts & Contract Design Michael R Carter Department of Agricultural & Resource Economics BASIS Assets & Market Access Research Program & I 4 Index Insurance Innovation Initiative University of California, Davis AFD-FERDI Workshop, Paris June 24, 2014

2 Promise of Index Insurance Know that risk is directly costly Makes Households Poor when it leads them to adopt less risky activities, but lower returning activities Keeps Households Poor when it leads them accumulate unproductive buffer assets Deepens capital constraints also as Cuts off self-finance by holding of unproductive savings Discourages external finance (credit), especially when risk is correlated Reducing risk via insurance should address all these problems Note also that when twined with technology adoption, insurance becomes relatively inexpensive But can insurance work for smallholder agriculture?

3 Logic of Index Insurance Conventional insurance (based on individual loss adjustment) has a dismal record Costly to verify losses for smallholders Moral hazard if do not/cannot reliably verify losses Adverse selection Index insurance does not pay based on (verified) individual losses, but instead based on a cheap to measure index that is correlated with individual losses (e.g., average yields in a zone, or rainfall) Cuts costs Eliminates moral hazard & adverse selection Example from recent study of individual versus (shadow) index insurance in Ecuador

4 Logic of Index Insurance Comparison of Index versus Conventional Insurance in Ecuador

5 Challenge of Index Insurance Ecuador study relies on government collected yield data available at high density at a national level Also based on relatively homogenous, coastal agriculture, not highland areas But, more generally, can an index be found that is strongly correlated with individual losses? If not, index insurance is a lottery ticket, not an insurance and would not be expected to reduce the costs of risk & have real development impacts Review what have we learned about whether & when index insurance can fulfill its promise Development impacts of index insurance The challenge of basis risk & sustainable impact Designing better index insurance contracts Technological solutions Institutional solutions Behavioral insights

6 Impact of Index Insurance Maize producers in Ghana invest more when insured Randomly offered some farmers insurance at variable prices Other farmers offered a capital grant for purchasing inputs Found that farmers offered insurance: Expand area cultivated by 15% Increase input use by 40% Capital grants by themselves have little impact Source: Karlan et al. (2014). Agricultural Decisions after Relaxing Risk & Credit Constraints, Quarterly J of Econ.

7 Impact of Index Insurance Cotton producers in Mali invest more when insured Designed a dual-scale index contract with radically lower basis risk Insurance offered to a random subset of villages find that in insured villages: Area planted to cotton increases by 15% Input use increases by 10-20% Currently scaling up in Burkina Faso Source: Elabed & Carter (2014) Ex-ante Impacts of Agricultural Insurance: Evidence from a Field Experiment in Mali, Working Paper

8 Impact of Index Insurance Kenyan pastoralists reduce dependence on costly coping strategies Drought insurance for pastoralists in Kenya launched in 2010 Index based on satellite measure of forage availability (NDVI) Insurance had payout in October 2011 after a prolonged drought that sparked 30-40% livestock mortality October 2011 survey asked insured and uninsured households how they had been coping with the drought prior to the payout/survey & how they anticipated coping after the payout/survey Source: Janzen & Carter (2014) After the Drought: The Impact of Microinsurance on Consumption Smoothing and Asset Protection, NBER Working Paper No

9 Impact of Index Insurance Kenyan pastoralists reduce dependence on costly coping strategies Striking results consistent with poverty trap theory: Initially better off households (expected to be consumption smoothers) show: Before Payout: No impact on consumption reduction nor on asset sales prior to payout After Payout: 65 %-point reduction in asset sales after payout Initially worse off households (expected to be asset smoothers) show: Before Payout: 30 %-point reduction in meals reduced prior to payout; No impact on asset sales After Payout: 43 %-point reduction in meals reduced after payout; No impact on asset sales

10 Challenge of Contract Design & Sustainable Impact These kinds of development impacts can only be sustained if contracts work well, paying farmers when losses occur Failure of index insurance contracts to pay when farmers have genuine losses is damaging at multiple levels Hurts farmers Hurts lenders Destroys trust & confidence in insurance (& insurance company reputation) Sometimes spectacular failure of rainfall-based contracts reflects: Poor correlation between rainfall and average farmer outcomes Low correlation between individual farmer outcome & average because of index scale An example from an analysis of rainfall contracts in India:

11 Insurance or Lottery Ticket? Source: Clarke et al. Weather based Crop Insurance in India, World Bank Research Working Paper 5985.

12 Solutions to the Problem of Basis Risk 3 classes of solutions: Better predictors of average outcomes (area yield or satellite instead of rainfall) Institutional solutions (redistribution within groups at scale of index) Dual-scale index & audit rules In addition, behavioral economics insights suggest further solutions, and willingness to pay for them

13 Contract Design Innovations Remote sensing: Evapotranspiration & other biomass growth measures Several efforts to employ satellite measures that reflect vegetative growth IBLI rangeland contracts in Northern Kenya and Southern Ethiopia PlaNet Guarantee working with evapotranspiration measures in Senegal & Burkina Strength is that it is very high resolution (e.g., 250 m x 250 m pixels) Still need more exploration about relationship between grain yields & biomass (skepticism from precision ag experts in California) New projects by IFAD/WFP (Senegal) & I4 (in cooperation with the SI systems engineering company in Tanzania)

14 Contract Design Innovations Audit rules & gap insurance Dual scale contract implemented in Ethiopia under IFPRI/I4 project Primary index is rainfall based Farmers can appeal for crop cut if fell index inaccurate See how trust evolves over time

15 Contract Design Innovations Dual-scale Contracts Reducing scale of an insurance index has two impacts: Reduces uninsured basis risk; but, Increases moral hazard So why not Dual-scale contract? One scale close to the farmer to reduce basis risk (reduces false negative & false positives) One at higher scale as an audit rule to check moral hazard (only pay if higher scale says likely that payout could have occurred naturally) First effort in Mali showed results could be large:

16 Contract Design Innovations Equivalently priced single & dual-scale contracts (Mali)

17 Contract Design Innovations Insights from behavioral economics Behavioral economics places individuals in controlled experiments & observes how people behave, say, in the face of risk Find that people do NOT consistently behave in ways economics traditionally predicts Calls into question models used to design insurance and predict its uptake Look at two areas where behavioral economics has been used to understand contract design: Ambiguity aversion & insurance demand in Mali Certainty preference & willingness to pay for insurance in Burkina Faso

18 Index Insurance as an Ambiguous, Compound lottery

19 Aversion to Ambiguity & Compound Lotteries Long-standing evidence (Ellsberg paradox) that people are averse to ambiguity & act much more conservatively in its presence Similar empirical evidence of a similar reaction to compound lotteries Psychologically: Complexity If people cannot reduce the lottery, then final probabilities seem unknown > akin to ambiguity Halvey (2007) shows in an experiment a link between ambiguity aversion and compound risk attitudes Implemented field experiments in Mali to measure risk & compound risk aversion 60% of the sample is compound risk averse, implying demand for index insurance becomes hyper-sensitive to basis risk:

20 Predicted Impact of Compound Risk Aversion on Index Insurance Demand Fraction of Population that Would Purchase Contract (%) Index Insurance Uptake as a Function of FNP Assuming Expected Utility Theory Assuming Compound Risk Aversion Probability of False Negative(%)

21 Certain vs. Uncertain Utility Andreoni & Sprenger propose a simple way to account for commonly observed behavioral paradoxes Suggest that people strongly overvalue a sure thing more than an almost sure thing (99.9% probability) If this overvaluation of outcomes that are certain is correct, implies that individuals undervalue insurance because the bad thing (the premium) is certain and hence overvalued relative to the good thing (payments), which are uncertain and undervalued Note that overvaluation is above and beyond what would be expected based on standard risk aversion Consistent with farmer complaints in the field about paying premium in bad years

22 Field Experiment in Burkina Faso Working with 577 farmer participants in the area where we are working with Allianz, HannoverRe, EcoBank, Sofitex and PlaNet Guarantee to offer area yield insurance for cotton farmers, played two incentivized behavioral games: Measured risk aversion over uncertain outcomes (α) and extent of certainty preference (β) Measured willingness to pay for insurance under two randomly offered alternative, actuarially equivalent contract framings: Found that: Standard framing (certain premium) Novel framing (premium forgiveness in bad years) One-third of farmers exhibit certainty preference Average willingness to pay is 10% higher under novel framing Certainty preference farmers value the alternative framing by 25%

23 Field Experiments in Burkina

24 Willingness to Pay for insurance All Certainty Others Expected Preference Utility Average WTP (both frames) 15,771 15,208 15,573 16,492 Frame A WTP 15,051 13,526 15,631 15,989 Frame B WTP 16,493 17,397 15,521 16,950 T-test (p-value) Regression analysis (controlling for covariates, clustering standard errors, etc.) confirms these findings that Frame B has a large & significant impact on demand for the 30% of the population that exhibits a strong preference for certainty Suggests that a simple re-framing of insurance can matter a lot

25 In Conclusion Know that risk is costly to poor rural households Exciting new evidence that insurance can make a difference However, cannot place rain gauges everywhere & think that we have a viable basis for insurance Still work to be done to craft technological and institutional solutions that will yield contracts that will work & that will appeal to people given how we behave in the face of risk

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