Michael Carter discusses key learnings from index insurance projects for smallholder farmers. Three main points: 1) Studies in Ghana, Mali, and Kenya found that index insurance increased investment and reduced reliance on costly coping strategies during droughts, showing it can have real development impacts by reducing risk. 2) However, basis risk from poorly correlated indexes undermines trust and impacts. New solutions using satellite data and dual-scale contracts have shown promise in reducing basis risk. 3) Behavioral experiments revealed farmers' aversion to ambiguity and compound risk reduces demand, while willingness to pay increased with contracts framing the premium as forgivable in bad years. Better understanding farmer behavior can improve uptake and impact.

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 &
I4 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

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?
M.R. Carter Index Insurance Impacts & Design

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
M.R. Carter Index Insurance Impacts & Design

4. Logic of Index Insurance
Comparison of Index versus Conventional Insurance in Ecuador
M.R. Carter Index Insurance Impacts & Design

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
M.R. Carter Index Insurance Impacts & Design

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.
M.R. Carter Index Insurance Impacts & Design

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
M.R. Carter Index Insurance Impacts & Design

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. 19702.
M.R. Carter Index Insurance Impacts & Design

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
M.R. Carter Index Insurance Impacts & Design

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:
M.R. Carter Index Insurance Impacts & Design

11. Insurance or Lottery Ticket?
Source: Clarke et al. “Weather based Crop Insurance in India,” World Bank Research
Working Paper 5985.
M.R. Carter Index Insurance Impacts & Design

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
M.R. Carter Index Insurance Impacts & Design

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)
M.R. Carter Index Insurance Impacts & Design

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
M.R. Carter Index Insurance Impacts & Design

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:
M.R. Carter Index Insurance Impacts & Design

16. Contract Design Innovations
Equivalently priced single & dual-scale contracts (Mali)
M.R. Carter Index Insurance Impacts & Design

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
M.R. Carter Index Insurance Impacts & Design

18. Index Insurance as an Ambiguous, Compound lottery
M.R. Carter Index Insurance Impacts & Design

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:
M.R. Carter Index Insurance Impacts & Design

20. 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(%)
FractionofPopulationthatWouldPurchaseContract(%)
Assuming Expected Utility Theory
Assuming Compound−Risk Aversion
M.R. Carter Index Insurance Impacts & Design

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
M.R. Carter Index Insurance Impacts & Design

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:
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%
M.R. Carter Index Insurance Impacts & Design

23. Field Experiments in Burkina
M.R. Carter Index Insurance Impacts & Design

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) 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
M.R. Carter Index Insurance Impacts & Design

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
M.R. Carter Index Insurance Impacts & Design