At the Workshop on Innovations in Index Insurance to Promote Agricultural and Livestock Development in Ethiopia, held December 3rd, 2015 in Addis Ababa, Craig McIntosh of UC San Diego, presented on challenges and pitfalls of implementing rainfall insurance

1. The Impact of Rainfall Index
Insurance in Amhara, Ethiopia
Shukri Ahmed, FAO
Craig McIntosh, UC San Diego
Alexander Sarris, University of Athens

2. 2
The promise of weather index insurance:
 From the perspective of Townsend (1994), the risk that
farming communities cannot manage themselves is
covariate, primary source of this is weather.
 Weather is an outcome that allows insurance contracts
to be written with no moral hazard (Gine & Yang 2009).
 Consequently, WII appears to be an effective way to
protect farmers against unavoidable risks.
 Interlinking credit with insurance may enhance the
willingness of farmers to borrow to invest in inputs,
generating a first-order expansion in productivity (Carter
et al. 2015).

3. 3
The pitfalls of weather index insurance:
 Despite this promise, WII has struggled to generate
demand at market prices (Cole et al. 2012).
 Highly risk averse farmers may dislike possibility of
‘contract non-performance’ (Clarke 2011).
 Ambiguity aversion may depress demand for complex
products with unknown probability distributions (Bryan
2010, Carter & Elabed 2015, McIntosh et al. 2015).

4. 4
Our project had two key purposes:
1. To attempt to ‘interlink’ index insurance with credit for
smallholder Ethiopian farmers.
2. To work entirely with private-sector providers of
insurance (Nyala) and credit (Dashen) to see if a market-
driven approach to WII using initial subsidies could
generate durable, sustainable demand at market prices.
Question of this project: can the right combination of
individual price subsidies and interlinking with credit
unlock demand for a private market product?

5. 5
Ethiopia may be an ideal environment for weather
index insurance because:
1. Vast majority of agriculture is rain-fed.
2. Rainfall variability is among the highest in the world.
3. Risk has been demonstrated to be a major factor
constraining farmers away from using the optimal level
of inputs (Dercon and Christiaensen, 2011).
4. The presence of strong intermediary institutions such as
village cooperatives and cooperative unions provide
structure to offer insurance.
5. Presence of strong private-sector insurance company,
Nyala.

6. 6
However, Ethiopia is also a very challenging
environment for a private-sector intervention:
1. Very strong state provides entire input and output chain
for cooperative farming sector.
2. Weak history of private sector involvement in
agriculture.
3. Three decade history of major food relief efforts to
famine-struck areas.
4. Large government safety-net program (PSNP) may
serve as a substitute for private-sector insurance (Duru
2015).
Raises the question: is it possible for the state to be too
credible at providing disaster relief, thereby undermining
private-sector demand for insurance?

7. 7
Ethiopian Project on Interlinking Insurance &
Credit for Agriculture (EPIICA):
Project is a collaboration between researchers and:
Nyala Insurance Company (largest insurer in country)
Dashen Bank (largest private-sector bank in country)
Ethiopian Economics Association (fieldwork/analysis).
Purpose of project is:
to test impact of rainfall insurance in one of the most
drought-exposed farming populations in world.
to understand the extent to which interlinking credit and
insurance (rainfall-contingent loans) can unlock demand for
inputs in smallholder agriculture

8. 8
Standalone Insurance:
 Sold through primary (village-level) cooperatives to
members at time of purchasing inputs.
 Framed as input insurance, meaning that it would cover
cost of inputs if rain fails.
 Payoffs with trigger/exit for each of three crop phases,
optimized separately for maize, sorghum, teff, and wheat
for each insured station.
 Only households in villages whose center is less than
15km from an insured station offered insurance.

9. 9
Interlinked Insurance:
 Cooperative Unions (collectives of village-level
cooperatives) are used as credit intermediaries.
 Each CU signs single loan contract with Dashen, is
made beneficiary of Nyala insurance policy.
 Pushes the CUs into new role, asking them to take
collateralized loans with collective assets.
 Premium must be paid up front for either product.
 Can only get the interlinked loan if insurance purchased,
but can choose standalone product also in interlinked
arm.

10. 10
Problems in the Interlinked Arm:
 Cooperative Unions reluctant to take on risk of loans,
particularly as government has typically provided credit
to their members.
 Heavy state involvement in credit sector, negative real
interest rates.
 Unpredictable role of government in smallholder input
financing: ‘the game of chicken’.
 Bureaucratic delays in screening of collateral, account
opening, etc.
Interlinked credit could not be executed in either the first or
the second year’s sales, only in the third.
Interlinked arm is standalone from an impact perspective.

11. 11
Research design, intended and actual:
Original sample:
120 kebeles: 40 control, 40 standalone, 40 interlinked.
However, not all turn out to be deficit-rainfall threatened.
Drought-threatened sample:
84 kebeles: 27 control, 29 standalone, 28 interlinked
However, Swiss Re refuses all but 7 stations.
Drought-threatened insurable sample ‘Experimental’:
49 kebeles: 15 control, 17 standalone, 17 interlinked
‘Experimental’ sample: 15 control vs. 34 treatment

12. 12
Survey Design:
We ran a four round panel survey.
Two baseline surveys prior to implementation.
One survey in each of the years following the first two sales windows.
The household survey sampled 20 households per village:
18 households that were randomly sampled members of the
cooperatives.
2 households that were randomly sampled from the non-cooperative
members in the village.
Our analysis uses only the cooperative members, since they were the
only ones with easy access to purchase insurance and inputs.

13. 13
Organization of the Panel Analysis:
The study features four rounds of household surveys, and two rounds
of insurance sales for which we have post-sales outcome data:
SURVEY TIMING: SALES WINDOW TIMING:
2011:
Jan – Mar: R1 Survey
2012:
Jan – Mar: R2 Survey
July-Aug: S1 sales, standalone only
2013:
Jan – Mar: R3 Survey
Apr: S1 sales payouts.
May-Jul: S2 sales, standalone only
2014:
Jan – Mar: R4 Survey
Apr: S2 sales payouts.
Apr-Jun: S3 sales, interlinked only

14. 14
The Individual-level Voucher Experiment:
In order to improve power of the village-level experiment:
We randomized the provision of insurance purchase
vouchers at the individual level.
 In the first two sales years, these vouchers enabled farmers to acquire
up to that amount of insurance for free.
 The large majority of insurance coverage issued in the project comes
from these vouchers rather than from private demand.
 That means that the study is largely measuring the impact of providing
small amounts of insurance cover for free.
 Quantity of coverage ~ directly randomized at individual level.

15. 15
Uptake across three years:
Year 1 sales window:
offered subsidies only to the study sample; uptake among those offered
subsidies was 34%
Uptake rate <.5% among the broader population not offered subsidies.
Year 2 sales window:
subsidy experiment in whole membership of coop, with vouchers of 0, $6, and
$12, more than 5,000 contracts written by Aug 2013.
Uptake rate in subsidized sample ~ 41%
Uptake rate in unsubsidized sample ~ .5%.
Year 3 sales window:
vouchers changed to cover a given fraction of purchase (no free lunch); sales
almost completely shut down.
Strong interlinked sales to a single cooperative, Feres Wega.

16. 16
The Individual-level Voucher Experiment:
 Only 21% of farmers put any of their own money into purchase;
most took the voucher and purchased only that much coverage.
 This is an experiment in giving away insurance coverage.
 Quantity of coverage ~ directly randomized at individual level.
050100
ValueofInputsInsured
0 20 40 60
Subsidy Voucher Amount, US$
All Treatments Kebeles with Any Uptake
All, fitted Uptake, fitted
Circle size proportional to number of observations at each subsidy amount
Sum Insured by Subsidy Voucher

17. 17
Credit Constraints & Interlinked Demand:
Uptake depending of type of Credit Constraint faced by the household:
For villages with any sales and for individuals who received vouchers, the Interlinked
product appears particularly appropriate for the 17% of individuals who are ‘Risk
Constrained’ (Bouchet et al. 2011).
Otherwise, no evidence of stronger overall demand for the Interlinked product, even for
those who report being Price or Quantity constrained in access to credit.
0 .2 .4 .6 0 .2 .4 .6
Standalone
Interlinked
Standalone
Interlinked
Standalone
Interlinked
Standalone
Interlinked
None Price
Quantity Risk
mean of Insurance_Demand
Graphs by cred_cons

18. 18
Balance in the Experimental Sample:
Despite reduced sample size, balance decent across villages and
excellent in individual voucher experiments.
Preliminary results: please do not circulate.
Balance Test using Outcomes in Rounds 1 and 2, all treatment terms:
Uses Chemical
Fertilizer
Fertilizer used
per Hectare
(KG)
Uses
Improved
Seeds
Number of
Parcels
Cultivated
Uses
Agricultural
Credit
Interlinked Treatment 0.246* 43.09 150.7 0.391 0.0335
(0.143) (29.100) (90.830) (0.352) (0.054)
Standalone Treatment 0.182 30.25 116 0.199 0.0303
(0.155) (30.640) (79.670) (0.375) (0.060)
Voucher S1 -0.0657 -6.359 -72.53 0.113 -0.0298
(0.088) (17.660) (53.550) (0.331) (0.043)
Voucher Amount S1 0.00012 0.0164 0.253 -0.000814 -0.00000726
(0.000) (0.039) (0.161) (0.001) (0.000)
Voucher S2 0.0373 0.31 -111.4 0.254 0.00134
(0.092) (17.540) (74.480) (0.252) (0.041)
Voucher Amount S2 -0.000416 -0.0684 0.252 -0.000482 -0.0000728
(0.000) (0.045) (0.237) (0.001) (0.000)
Baseline Outcome in Control 0.450*** 58.17*** 78.46*** 3.388*** 0.157***
(0.104) (20.610) (21.510) (0.216) (0.041)
Observations 818 809 1,636 818 1,636
R-squared 0.028 0.019 0.014 0.012 0.001

19. 19
Using the Experimental Design to analyze impact:
The core regression specifications take the form:
where i is individual (household), k is kebele, t is survey
wave (1-4), is kebele-level treatment status and is
individual-level voucher status, randomized per round.
• Most outcomes are not observed in R2, so only the
round dummies 3 and 4 are identified.

20. 20
Panel Impacts on Fertilizer Use
Very little uptake outside of voucher group within the study sample.
No indication that provision of small amounts of free insurance
leads to an improvement in fertilizer use.
Preliminary results: please do not circulate.
Panel Impact on Fertilizers:
Covered by
Insurance
Sum Insured
Uses Chemical
Fertilizer
Number of
Plots on which
Chemical Ferts
Used
Urea Used per
Hectare
DAP Used per
Hectare
Total Fertilizer
Used per
Hectare
Household Received Voucher this season 0.331*** 125.8 0.0161 -0.144 -0.902 3.853 3.688
(0.047) (92.000) (0.038) (0.093) (4.472) (4.808) (8.309)
Amount of Household Voucher this season 0.000218 1.887*** -0.000105 0.000382 0.00741 -0.00938 -0.00483
(0.000) (0.442) (0.000) (0.000) (0.016) (0.017) (0.029)
Round 3 -0.0262** -36.09* 0.201*** 0.460*** 7.549*** 4.310** 11.57***
(0.012) (20.680) (0.024) (0.056) (2.061) (2.093) (3.782)
Round 4 0.0253*** 14.15 0.155*** 0.342*** 9.051*** 8.047*** 16.92***
(0.009) (18.710) (0.022) (0.048) (2.023) (2.082) (3.742)
Constant 0.000263 0.486 0.551*** 1.196*** 34.27*** 40.69*** 75.72***
(0.005) (10.820) (0.011) (0.023) (1.041) (1.032) (1.927)
Observations 2,571 2,571 2,571 2,571 2,428 2,428 2,428
R-squared 0.272 0.198 0.078 0.07 0.02 0.016 0.021
Number of quest_id 882 882 882 882 876 876 876

21. 21
Instrumenting for sum insured w/ Voucher Amount
Study provides a very large degree of experimental intensive margin
variation in the sum insured, yet . . .
No evidence that increasing the voucher amount (and hence sum insured)
leads to changes in the use of fertilizers.
Preliminary results: please do not circulate.
Impact of Sum Insured, Instrumenting for Sum Insured with Voucher Amount
Uses Chemical
Fertilizer
Number of
Plots on which
Chemical Ferts
Used
Urea Used per
Hectare
DAP Used per
Hectare
Total Fertilizer
Used per
Hectare
Sum Insured (instrumented w voucher amt) -0.0000339 -0.0000342 0.00226 0.000481 0.00238
(0.000) (0.000) (0.004) (0.004) (0.007)
R3 0.210*** 0.459*** 7.609*** 5.148** 12.59***
(0.022) (0.050) (2.279) (2.257) (4.126)
R4 0.164*** 0.330*** 8.865*** 8.921*** 17.77***
(0.020) (0.047) (2.126) (2.106) (3.850)
Constant 0.559*** 1.215*** 35.03*** 41.40*** 77.23***
(0.012) (0.029) (1.286) (1.274) (2.329)
Observations 2,454 2,454 2,323 2,323 2,323
Number of Observations 818 818 813 813 813

22. 22
Do past payouts drive future uptake?
 Contrary to others
in literature, we
find that ‘payouts’
are actually a
negative predictor
of uptake.
 First year payouts
were late, may
have depressed
demand for
insurance in
second season
for those who
were supposed to
be paid.
The Impact of Receiving a Payout on Sales in the Subsequent Season.
Purchased Insurance in Sales
Season 2
Received Payout in Sales season 1 -0.104*
(0.057)
Would have received payout if bought in S1 0.0807
(0.060)
Any voucher S1 0.0271
(0.045)
Voucher amount S1 -0.000096
(0.000)
Any voucher S2 0.402***
(0.082)
Voucher S2 0.000102
(0.000)
Constant -0.0105
(0.010)
Observations 818
R-squared 0.301

23. 23
Impacts: Seeds
Slight improvement in value of seeds, but this is local seeds not improved.
Preliminary results: please do not circulate.
Panel Impact on Seeds:
VARIABLES
Uses Any
Improved
Seeds
Value of Local
Seeds Used
Value of
Improved
Seeds Used
Household Received Voucher this season -0.019 262.5* -13.39
(0.042) (142.300) (55.520)
Amount of Household Voucher this season 0.0001 -0.594 -0.0104
(0.000) (0.431) (0.200)
Round 3 0.00973 -560.7*** 56.44**
(0.023) (104.800) (26.450)
Round 4 -0.00135 -442.4*** 39.49
(0.021) (104.600) (26.600)
Constant 0.370*** 1,072*** 161.0***
(0.010) (48.160) (13.100)
Observations 2,571 2,571 2,571
R-squared 0.001 0.032 0.003
Number of quest_id 882 882 882

24. 24
Impacts: Other Inputs
Some evidence of an increased use of Input Credit when insured (point
estimate is large; 50% of baseline credit usage rate).
Preliminary results: please do not circulate.
Panel Impact on Other Inputs:
VARIABLES
Total Hectares
of Land
Farmed
Total Number
of Parcels
Cultivated
Used any
Input Credit
Used any
Chemical
Pesticides or
Herbicides
Used Hired
Labor
Household Received Voucher this season -0.189 -0.095 0.0743* 0.0256 -0.00134
(0.260) (0.117) (0.040) (0.049) (0.042)
Amount of Household Voucher this season 0.000815 -0.000214 -0.000195 0.000118 9.35E-06
(0.001) (0.000) (0.000) (0.000) (0.000)
Round 3 -0.484* -0.357*** 0.0821*** 0.130*** 0.0368*
(0.270) (0.068) (0.022) (0.025) (0.022)
Round 4 -0.516** -0.441*** -0.016 0.127*** 0.0728***
(0.245) (0.065) (0.019) (0.024) (0.019)
Constant 1.621*** 3.607*** 0.153*** 0.279*** 0.215***
(0.165) (0.035) (0.009) (0.012) (0.010)
Observations 2,571 2,571 2,571 2,571 2,571
R-squared 0.005 0.066 0.029 0.049 0.011
Number of quest_id 882 882 882 882 882

25. 25
Impacts: Yields
Large secular decrease in yields for major crops despite increase in fertilizer use.
Fertilizers compensating for declining soil fertility?
No improvements in yields from insurance.
Preliminary results: please do not circulate.
Panel Impact on Yields:
VARIABLES
Wheat Maize Teff Sorghum
Household Received Voucher this season 188.9 34.21 50.17 -169.8
(341.700) (139.400) (72.960) (177.700)
Amount of Household Voucher this season 0.413 0.258 -0.22 0.23
(0.911) (0.521) (0.249) (0.565)
Round 3 -803.1*** -300.3*** -140.9*** 84.07
(243.800) (88.770) (50.730) (93.790)
Round 4 -832.7*** -428.0*** -158.7*** 205.3**
(215.100) (77.360) (45.440) (83.560)
Constant 1,160*** 1,395*** 740.1*** 981.4***
(90.690) (40.320) (21.360) (34.460)
Observations 360 1,090 1,774 896
R-squared 0.184 0.058 0.027 0.016
Number of quest_id 220 497 751 423

26. 26
Conclusions: the successes of the projects.
 Succeeded in building collaborative relationship with
Nyala, Dashen.
 Fielded the Interlinked product in the third year.
 Worked with Dashen to acquire support for the
Interlinked product from USAID’s Development Credit
Authority.
 Issued a large number of insurance policies as a part of
the project:
 728 farmers in the first sales year
 Over 5,000 farmers in the second sales year
 254 Interlinked policies sold at full market price in most recent sales
year.

27. 27
Conclusions: however . . . .
 Projects results are disappointing on two levels:
1. Without a 100% subsidy rate, it appears that there was no viable
demand for rainfall index insurance in this case.
2. Even when free insurance was distributed, this appears to have had
no effect on the farming behavior of covered households.
 We have substantial individually-randomized variation in the extent of insurance
coverage, and no evidence that this generated meaningful changes in agricultural
behavior.
Did this occur because the product was not marketed correctly to the
field?
 88% of study households in the treatment area said that they had received
information about the product, 57% received a brochure describing product.
 70% said they understood the product ‘well’ or ‘partially’.
 However, only 2% of households correctly specified deficit rainfall at the
closest station as the event that triggers payouts, but most believed it was
actually an indemnity policy (which should have been more attractive).

28. 28
Conclusions: Moving forward
Based on results of summary of literature of Randomized Controlled
Trials from ATAI/JPAL:
No evidence from anywhere in the world that the current type of
weather index insurance products can move to scale at commercial
prices.
And yet, risk remains a dominant concern in agriculture!
So what are the promising areas moving forward?
1.Embrace subsidized Weather Index Insurance.
2.Shift focus to ag technology that protects farmers from risk.
3.Improve the design of insurance products (better indexes,
group insurance).
4.Pursue Meso-level insurance (government safety net programs,
insuring agricultural lenders).