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Ethiopian Weather Insurance Experiment
1. ETHIOPIAN DEVELOPMENT
RESEARCH INSTITUTE
An experiment on the impact of weather
shocks and insurance on risky investment
Ruth Vargas Hill and Angelino Viceisza
1
2. Outline
1. Motivation
2. Summary of methodology and results
3. Background on study site
4. Experiment protocol
5. Empirical strategy
6. Results
7. Conclusions and further research
2
3. Motivation: impact of weather shocks
• Weather shocks have a significant impact on the Ethiopian economy and on
the welfare of rural households
• The expectation of shocks also reduces investment in high-return but high
risk activities:
• Households more susceptible to risk of low consumption are less likely to
apply fertilizer (Dercon and Christiaensen 2006)
• Encourages production of low-risk buy low return crops (e.g. enset)
• Likelihood of investing in non-farm activities is lower for those more
susceptible to risk (Rijkers et al 2008)
• Existing policy response:
• Food aid
• Safety nets (PSNP)
3
4. Motivation: new tools for insurance?
• Can we do more? In particular can we develop weather insurance to:
• Support efficient delivery of public safety nets (e.g. subsidizing
premiums)
• exist as a market in its own right (commercial insurance purchases)
• Innovations in weather-index insurance has encouraged new work on
developing weather insurance markets for small-scale farmers
• Traditional weather insurance: assessing loss is expensive when farms
are small-scale
• Weather-based index insurance: pays not by assessing loss in farmers
fields, but on information on the quality of the rain from the nearby
weather station
4
5. Motivation: new tools for insurance?
As elsewhere, a number of pilots in Ethiopia
• Pilot in Alaba (SNNPR) with Ethiopian Insurance Company in 2006
(World Bank)
• Development of LEAP (Livelihood Early Assessment Protection)
software to design drought indexes specifically for the local
Ethiopian context was initiated by WFP, World Bank and FAO
• Nyala index-insurance pilot for haricot beans in Boset Woreda
(SNNPR) in 2009 (WFP)
• Index-insurance pilot for teff in Tigray in 2009 (Oxfam)
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6. Motivation: limits to new tools
• Promising new product:
• Cheaper to administer, also no moral hazard or adverse selection
• But this comes at expense of:
• Basis risk (payout does not always coincide with loss)
• Difficult to understand (depends on unseen mm, not actual loss)
• Also still:
• Trust
• Liquidity and budget constraints: insurance is still costly
• Cost is worth it if insurance provision results in change in behavior that
increases average income of farmers
• This could be applying fertilizer, or growing more high-risk but
high-return crops
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7. Motivation: limits to new tools
• Promising new product:
• Cheaper to administer, also no moral hazard or adverse selection
• But this comes at expense of:
• Basis risk (payout does not always coincide with loss)
• Difficult to understand (depends on unseen mm, not actual loss)
• Also still:
• Trust
• Liquidity and budget constraints: insurance is still costly
• Cost is worth it if insurance provision results in change in behavior that
increases average income of farmers
• This could be applying fertilizer, or growing more high-risk but
high-return crops
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8. What we do: summary of methodology
• Randomized trials help test impact, but not enough take-up to allow to test
impact on behavior (Giné and Yang 2007, Cole et al 2009)
• Small scale experimental games can help to explore such impacts
• We conduct a experimental game in which farmers make decisions for
money in a context familiar to them (whether or not to buy fertilizer) to
test hypothesis that insurance provision would enable farmers to take
greater, yet more profitable risks
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9. What we do: summary of methodology
• The insurance provided was free of many problems of insurance:
• Like index insurance it was based on an independent measure of how good
the rains had been: no adverse selection and no moral hazard
• Unlike index insurance there was no basis risk: farmers were always paid
when they experienced low income
• Because it was played in a game there were also no problems with trust or
understanding
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10. What we find: summary of results
• Game results provide some evidence that insurance does encourage
greater risk taking
• more so for risk averse farmers who understood insurance
well, and were already inclined to purchase fertilizer
• Changes in wealth and farmers experience of fertilizer purchases and
weather in previous rounds were also important determinants of
fertilizer demand
• Provision of real insurance will be the real test
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11. Study site
• Danicho Mukhere kebele (Silte zone) in southern Ethiopia: around
2000 households in 8 villages:
• some in the highlands growing enset, barley, potatoes, cabbage
• some in the mid-lands growing enset, wheat and chat
• mid-land villages have better market and road access
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13. Study site
• The average head is 43 years (median), male, has farming as main
occupation and little education (median: one year of education)
• Nearly all farmers experienced drought in the last 10 years
• farmers lose 75% of crop when rain fails (subjective estimate)
• expected probability of rain failure in coming season: 0.25
• Good mechanisms to deal with illness and death (iddirs, gifts and
transfers between family and friends)
• But little insurance against drought: weather shocks result in loss of
assets (72%) and reduced consumption (76%) much more than other
shocks
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14. Experiment protocol
• Framed game that farmers could relate to their day-to-day decisions:
• How much fertilizer will you buy when the return to fertilizer
depends on the uncertainty of the weather?
• When you have insurance how much will this change?
• Farmers were given money
• From 2 to 16 Birr
• They then played the following game to represent production and
consumption
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15. Experiment protocol
Farmer decides how much
fertilizer to buy (0, 1 or 2
bags) and pays =
Rainfall is drawn
= 10 Birr + 2 Birr for each
Farmer receives income
based on rain and fertilizer = 5 Birr + 0 Birr for each
Farmer pays 8 Birr for
consumption
REPEATED 4 TIMES
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16. Experiment protocol
• The return to fertilizer was varied (2 Birr of additional yield value for each
bag or 1.25 Birr of additional yield value for each bag)
• The probability of bad rain was varied (1 in 4, or 1 in 5)
• To assess how insurance affects risk taking we offered insurance in the last
2 rounds of randomly selected sessions
• Either at the actuarially fair price or free
• Insurance paid 3 Birr if the weather was bad
• Players were requested to buy insurance to increase the power of
the test of the impact of insurance on behavior
• Compare changes in fertilizer purchases between those were offered
insurance and those that were not
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17. Experiment protocol
Farmer decides how much
fertilizer to buy (0, 1 or 2
bags) and pays =
Rainfall is drawn
= 10 Birr + 2 Birr for each
Farmer receives income
based on rain and fertilizer = 5 Birr + 0 Birr for each
Farmer pays 8 Birr for
consumption
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18. Experiment protocol
Farmer decides how much
fertilizer to buy (0, 1 or 2
bags) and buys insurance
=
INSURANCE
Rainfall is drawn
Farmer receives income = 10 Birr + 2 Birr for each
based on rain and fertilizer
and insurance = 5 Birr + 0 Birr for each
+ 3 Birr insurance payout
Farmer pays 8 Birr for
consumption
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19. Experiment implementation
• 241 household heads randomly selected from four villages
participated in the game and undertook a household survey:
• On average experiments lasted 2.5 hours and paid 27 Birr
• Conducted in library in school at centre of village: large room
allowed privacy…
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21. Empirical strategy
• Random selection of individuals should result in no differences
between individual characteristics with and without insurance
• compare changes in fertilizer purchase before (rounds 1 and
2) and after insurance (rounds 3 and 4) between insurance
and no insurance sessions
• Fixed effect regression:
Δ fit = Δ Iit+ Δ ui
where Δ fit is change in fertilizer, Iit is an indicator function
denoting whether insurance was provided, ui is a time
constant individual effect (i= individual, t=round of game)
• Few differences in individual characteristics
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22. Empirical strategy
• But, randomization of both weather and insurance across 44 rounds
resulted in some important differences in round characteristics…
• In sessions in which insurance was provided
• Bad weather was less likely ex-poste: changes in weather
and wealth were not orthogonal to provision of insurance
• Initial fertilizer purchases were higher
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24. Empirical strategy
• After round 2 choice: sessions being provided with insurance
universally experienced good weather (ex-poste)
• Wealth was much higher for individuals with insurance when
insurance was provided
• Change in wealth was much higher
• Perceived probability of bad weather may also have been
less
• After round 3 choice: sessions without insurance universally
experienced bad weather
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25. Empirical strategy
• Control for commensurate changes by adding Δ wealth and weather in
fixed effects regression
• Use nearest neighbor matching estimator (Abadie et al 2004) to compare
like observations
• Is their sufficient overlap (Imbens 2004)?
– If outliers in control observations (round 3): artificially precise
estimates – caution in interpreting results
– If outliers in treatment observations (round 4): biased estimates –
omit treatment observations which experienced bad weather
– Fixed effects estimation should be ok given multiple observations for
each individual allows a more accurate estimate of behavioral
response to good and bad weather
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26. Empirical strategy
• Can we assume common time effects across each group, even after
controlling for weather?
• Higher initial fertilizer purchases for those offered insurance
• Limited fertilizer purchases: those already purchasing 2 bags could
not increase even if exposure to risk reduced
• Time trend of increasing purchases (perhaps because of good
weather and wealth increases): confounding effect of insurance in
encouraging fertilizer purchases
• Opposite effect to Eissa and Liebman (1996) in which higher labor
market participation in control caused overestimation of treatment
effect (Blundell and Costa-Dias 2002)
• Add dummy for those at corner solution in analysis
• Perform matching where matching is identical on initial fertilizer purchases
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27. Results
Before doing this we first present the unconditional changes in
fertilizer purchases between those who were insured and those who
were not:
Difference in bags of fertilizer
purchased in rounds... 1 and 3 2 and 3 1 and 4 2 and 4
Insurance 0.154* 0.157** -0.152* -0.149**
(0.083) (0.073) (0.084) (0.064)
Constant -0.0746 -0.157** 0.231*** 0.149***
(0.069) (0.064) (0.058) (0.054)
Observations 248 248 248 248
Adjusted R2 0.009 0.013 0.009 0.016
*** p<0.01, ** p<0.05, * p<0.1
Robust standard errors in parentheses
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28. Results
Before controlling for differences in changes in weather and wealth we
also present the unconditional matched results using nearest neighbor
matching and matching exactly on initial fertilizer purchases (omitting
treatment outliers in last 2 columns)
Difference in bags of fertilizer
purchased in rounds... 1 and 3 2 and 3 1 and 4 2 and 4
Nearest neighbor matching 0.273** -0.061 -0.059 -0.027
(0.113) (0.074) (0.077) (0.061)
*** p<0.01, ** p<0.05, * p<0.1
Standard errors in parentheses
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29. Results
Conditional changes in fertilizer purchases between those who were
insured and those who were not:
Δ insurance 0.090 (0.069) 0.115* (0.060)
Δ wealth -0.192*** (0.038) -0.018 (0.031)
Square of Δ wealth 0.016* (0.008) 0.035*** (0.007)
Good weather in previous round 1.858*** (0.26)
Good weather and no fertilizer 1.27*** (0.143)
Bad weather and no fertilizer 0.445* (0.266)
Bad weather and fertilizer -0.568*** (0.215)
Dummy for max fertilizer -1.435*** (0.0901) -1.375*** (0.0769)
Constant -0.453** (0.1800) 0.565*** ( 0.1410)
Observations 744 744
Number of id 248 248
Adjusted R2 0.3151 0.3601
*** p<0.01, ** p<0.05, * p<0.1, Robust standard errors in parentheses
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30. Results
• Some small impact: under insurance fertilizer purchases were 0.115
higher => average return realized by farmers increased by 5.75%
• Are there groups of people for whom the provision of insurance has
more of an impact?
Impact of insurance High Low
for people with
Risk aversion 0.125** (0.0571) 0.114 (0.0728)
Understanding 0.124** (0.0622) 0.080 (0.0747)
C.V. of fertilizer return 0.052 (0.070) 0.174*** (0.066)
• Insurance also has a higher impact for those who had previously
bought fertilizer: 0.125** versus 0.116
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31. Conclusion and further work
• Conducted a small-scale framed field experiment to explore the impact of
insurance
• Some evidence that insurance may slightly increase investment in higher
return riskier activities. Impact higher for those who:
• Understood insurance better
• Were more risk averse
• Faced a lower C.V. of return
• Were inclined to purchase fertilizer in reality
• Purchases strongly dependent on weather in previous round:
• Partially from changes in wealth, partially from changes in perceptions
of cost and benefits => implications for how we understand peoples
perceptions of weather and returns to technologies
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32. Conclusions and further work
• Promising new product:
• Cheaper to administer, also no moral hazard or adverse selection
• But this comes at expense of:
• Basis risk, difficult to understand (depends on unseen mm, not
actual loss)
• Also still:
• Trust
• Liquidity and budget constraints: insurance is still costly
• Cost is worth it if insurance provision results in change in behavior that
increases average income of farmers
This study provides some evidence insurance increases investment in risky inputs
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33. Conclusions and further work
• How to limit basis risk? Very important
– Invest in automated weather stations reduces basis risk for farmers (even if
insurance difficult to price)
– Combine index with assessment of loss: Nyala’s contracts with seed farmers,
research by Daniel Clarke (but moral hazard must not creep back in), future work
with Nyala and WFP
• How to improve understanding and trust? Index products can be complicated
– Does offering simple contracts help? (IFPRI: research on very simple contracts
insuring rainfall directly not weighted rainfall)
– Work with those used to thinking about insurance—e.g. iddir leaders—who can
buy on behalf of others? (Ongoing work by Oxford and IFPRI)
• How to address liquidity constraints?
– Offering insurance products through iddirs, which have some liquidity
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