Index Insurance for Small-holder Agriculture:

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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

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Index Insurance Innovation Initiative University of California, Davis

http://basis.ucdavis.edu .

AFD-FERDI Workshop, Paris

June 24, 2014

M.R. Carter Index Insurance Impacts & Design

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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?

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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

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Logic of Index Insurance

Comparison of Index versus Conventional Insurance in Ecuador

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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

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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.

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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

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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. 19702.

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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

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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:

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Insurance or Lottery Ticket?

Source: Clarke et al. “Weather based Crop Insurance in India,” World Bank Research Working Paper 5985.

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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

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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)

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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

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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:

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Contract Design Innovations

Equivalently priced single & dual-scale contracts (Mali)

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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

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Index Insurance as an Ambiguous, Compound lottery

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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:

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Predicted Impact of Compound Risk Aversion on Index Insurance Demand

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80

Index Insurance Uptake as a Function of FNP

Probability of False Negative(%)

Fraction of Population that Would Purchase Contract (%)

Assuming Expected Utility Theory Assuming Compound−Risk Aversion

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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

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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:

Standard framing (certain premium)

Novel framing (premium forgiveness in bad years)

Found that:

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%

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Field Experiments in Burkina

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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) 0.09 0.01 0.9 0.5

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

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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