the favorable impact of index-based livestock insurance (ibli): results among ethiopian and kenyan...

The Favorable Impact of Index-Based Livestock Insurance (IBLI): Results

among Ethiopian and Kenyan Pastoralists

Christopher B. Barrett, Cornell University

Seminar at USAID Headquarters, Washington, DCFebruary 10, 2015

Motivation: Poverty Traps And Catastrophic Risk

There is strong evidence of poverty traps in the arid and semi-arid lands (ASAL) of northern Kenya and southern Ethiopia. These put a premium on risk mgmt.

Catastrophic herd loss risk due to major droughts identified as the major cause of these dynamics.

Source: Lybbert et al. (2004 EJ) on Boran pastoralists in s. Ethiopia. See also Barrett et al. (2006 JDS) among n. Kenyan pastoralists, Santos & Barrett (2011 JDE) on s. Ethiopian Boran.

Motivation: Increased Risk From Climate Change

Pastoralist systems adapted to climate regime. But resilient to a shift in climate? Many models predict increased rainfall variability (i.e., increased risk of drought).

Herd dynamics differ b/n good and poor rainfall states, and so change with drought (<250 mm/ year) risk.

In southern Ethiopia, doubling drought risk would lead to system collapse in expectation in the absence of any change to prevailing herd dynamics.

Source: Barrett and Santos (EcolEcon 2014)

Motivation: Standard Responses to Drought

Standard responses to major drought shocks:1) Post-drought restocking2) Food aidKey Problems: - Slow- Expensive (in part, because it’s slow)- Targeting challenges- Food aid can reinforce sedentarization/foster dependency- Core issue: If transfers go only to the poor who are already in

the poverty trap, the numbers of poor will grow as shocks knock others below the poverty trap threshold. In the long-run, the ex ante poor worse off as others join their ranks and compete for scarce social assistance resources. (see Barrett, Carter & Ikegami 2012 for more general theory/illustrations)

Alternative Responses: Insurance?

Commercially sustainable insurance can:• Prevent downward slide of vulnerable populations• Crowd-in investment and accumulation by the poor• Induce financial deepening by crowding-in credit • Let us focus humanitarian resources on the needy

But can insurance be sustainably offered in the ASAL?

Conventional (individual) insurance unlikely to work, especially in small scale pastoral/agro-pastoral sector:

• Very high transactions costs, esp. w/little financial intermediation among pastoralists

• Moral hazard/adverse selection

The Potential of Index Insurance

Index insurance is a variation on traditional insurance:- Do not insure individual losses.

- Instead insure some “index” measure that is strongly correlated with individual losses.

(Examples: rainfall, remotely sensed vegetation index, area average yield, area average herd mortality loss).

- Index needs to be:- objectively verifiable- available at low cost in real time- not manipulable by either party to the contract

The Potential of Index Insurance

Index insurance can obviate the problems that make individual insurance unprofitable for small, remote clients:

- No transactions costs of measuring individual losses- Preserves effort incentives (no moral hazard) as no single individual can influence index- Adverse selection does not matter as payouts do not depend on the riskiness of those who buy the insurance

Index insurance can, in principle, be used to create a timely, financially sustainable, self-targeting safety net to protect pastoralists against catastrophic drought shocks.

Could also accelerate herd recovery, altering herd dynamics and averting system collapse if drought frequency increases. Perhaps crowd in value-adding investments, too!

The Major Challenges of Index Insurance

1. High quality data (reliable, timely, non-manipulable, long-term) to design/price product and to determine payouts

2. Minimize uncovered basis risk through product design. Is it insurance or a lottery ticket? All turns on basis risk!!!

3. Innovation incentives for insurers/reinsurers to design and market a new product and global market to support it

4. Establish informed effective demand, especially among a clientele with little experience with any insurance, much less a complex index-based insurance product

5. Low cost delivery mechanism for making insurance available for numerous small and medium scale producers

(Jensen, Barrett &2014)

Index-Based Livestock Insurance: Design

The signal: Normalized Difference Vegetation Index (NDVI) collected by satellite

Response function: regress historic livestock mortality onto transforms of historic cumulative standardized NDVI (Czndvi) data. In Borana, just NDVI. Designed to minimize household-level basis risk.

Indemnity payments: In Kenya, predicted livestock mortality >15% according to:

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb

Period of NDVI observations forconstructing LRLD mortality index

Predicted LRLD mortality is announced.Indemnity payment is made if IBLI is triggered

LRLD season coverage SRSD season coverage

1 year contract coverage

Sale periodFor LRLD

Sale periodFor SRSD

Predicted SRSD mortality is announced.Indemnity payment is made if IBLI is triggered

Period of NDVI observationsFor constructing SRSDmortality index

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb

Period of NDVI observations forconstructing LRLD mortality index

Predicted LRLD mortality is announced.Indemnity payment is made if IBLI is triggered

LRLD season coverage SRSD season coverage

1 year contract coverage

Sale periodFor LRLD

Sale periodFor SRSD

Predicted SRSD mortality is announced.Indemnity payment is made if IBLI is triggered

Period of NDVI observationsFor constructing SRSDmortality index

Temporal Structure of IBLI contract:12 month contract sold during 2-month sales windows just prior to usual start of seasonal rains. Payouts March 1 and/or October 1.

Chantarat et al. JRI 2013


Index-Based Livestock Insurance: Implementation

Commercial underwriters: In Kenya: UAP, APA, Takaful. In Ethiopia: OIC

International reinsurers: Swiss Re, Africa Re

Lots of implementation challenges

IBLI team developed extension/ financial education programs to (randomly) inform prospective buyers.

IBLI team (randomly) distributeddiscount coupons to induce uptake andto establish price elasticity of demand.

Payouts in Kenya in Oct 2011, Mar 2012Payouts in Ethiopia in Nov 2014


IBLI Pilots in Ethiopia and Kenya

IBLI products (surveys) launched in Marsabit, Kenya in Jan 2010 (Oct 2009) and in Borana, Ethiopia, in Aug 2012 (Mar 2012).

Kenya sampling overlaid with HSNP coverage as research design.

(Jensen, Barrett & Mude 2014)

IBLI: Significant Basis Risk Remains

Covariate risk is important but household losses vary a lot …

Jensen, Barrett & Mude 2014

Notes: The left figure illustrates the covariate (average) loss rate in each season. The right figure illustrates the distribution of losses within each seasons. The boxes depict the interquartile range, the upper and lower adjacent values are either 3/2 the interquartile range or the value furthest from the median. The remaining observations fall outside the adjacent values.

and the index does not perfectly track covariate losses.

Notes: Covariate loss-index observations are seasonal division average mortality paired with the index value for that division-season. Fitted lines and confidence intervals are generated by regressing livestock mortality rates on the index.

- IBLI hhs still hold most risk: 62-77% of total risk exposure remains- Most basis risk is idiosyncratic and random, not targetable or correctable.- Significant spatial variation in covariate share – geographically target IBLI?

(Jensen, Barrett &

IBLI: A Highly Imperfect Product

Because of basis risk, esp. false negatives, IBLI cannot stochastically dominate no insurance.


Survival rate w/o insurance (L) and net of prem/indemnity payments w/IBLI (R).Note: - small probability of negative survival rates! - increased dispersion of outcomes due to false payments>losses

Histograms of livestock survival rate and net livestock survival rate with full insurance. Tally to the left of zero, between zero and one, and to the right of one are in green.

IBLI Uptake Significant … But So Is Disadoption

In HH surveys , in Borana (Ethiopia)/Marsabit (Kenya):- 54/44% ever purchased IBLI within first 4 sales periods- But repurchase rates low: 18-68%/16-27%- High rates of disadoption : 26/41% within 2 years

IBLI Uptake Is Also Predictable …

Capacity to predict uptake patterns is reasonably strong:

Unconditional observed / predicted (Cond. FE) likelihood of buying IBLI

Observed / predicted (Cond. FE) level of purchases (|buying IBLI)

Key determinants of IBLI uptake

General uptake findings — robust across specifications and surveys

Price: Responsive to premium rate (price inelastic). Price elasticity grows w/design risk. Design Risk: Design error reduces uptake; greater effect at higher premium rates.Idiosyncratic Risk: Hh understanding of IBLI increases effect of idiosyncratic riskUnderstanding: Extension/marketing improves accuracy of IBLI knowledge but no

independent effect of improved understanding on uptake.Herd size: Likelihood of uptake increasing in HH herd sizeLiquidity: IBLI purchase increasing w/HSNP participationIntertemporal Adverse Selection: HHs buy less when expecting good conditions.Spatial Adverse Selection: Divisions with relatively more covariate risk see higher uptake and level of coverage increases with variation in division average losses.Gender: no gender diff in uptake. Women more sensitive to risk of new product.

Bageant 2014; Jensen, Mude & Barrett 2014; Takahashi et al. 2014

IBLI’s Impacts: Herd mortality risk

Proportion of households that are better off with IBLI than without (Simulated utility analysis)

Proportion of households for whom IBLI improves their position with respect to each statistic


Statistic Proportion Loaded &

UnsubsidizedSubsidized

Mean 0.232 1.000Variance 0.359 0.359Skewness 0.817 0.817Semi-Variance 0.374 0.609

IBLI’s Impacts: Livestock productivity/income

HSNP Participation:• Improves the likelihood that target type (poor, old, many dependents) households remain mobile, an important characteristic for pastoralists

IBLI coverage:• Increases investments in maintaining livestock through vet expenditures

• Increases total and per TLU income from milk.

Note: TLU veterinary expenditures are pos/sign related to milk productivity


HSNP IBLI

Dependent Variable

Cumulative Past Participation

Current Client

Cumulative Past Coverage

Current Coverage (TLU)

Production strategies:

Herd Size 0.0583 -2.531 -5.634*** -0.270 (0.470) (2.963) (1.970) (0.693) [4.693] [3.543] Veterinary Expenditures (KSH)

48.22 261.6 584.8* -46.21(57.48) (363.9) (324.7) (127.2)

[13.78] [15.17] Household is Partially or Fully Mobile

0.0376** 0.251*** -0.0669 0.0386(0.0172) (0.0686) (0.111) (0.0481)

[17.76] [14.86]Production outcomes:

Milk income (KSH) 193.6 307.0 1,688* 840.6* (275.6) (1,039) (970.0) (473.6) [11.94] [11.46] Milk income per TLU (KSH)

63.65*** -97.35 423.5*** 63.81(20.14) (79.24) (118.1) (47.23)

[14.72] [13.05]A complete list of covariates, coefficient estimates, and model statistics can be found in Jensen, Mude & Barrett (2014). Clustered and robust standard errors in parentheses. Model F-stat in brackets. *** p<0.01, ** p<0.05, * p<0.1.

IBLI’s Impacts vs. HSNP: Normalized by cost


VOI Mean VOI in Final PeriodHSNP 0.86HSNPC 3.76IBLI 0.60IBLIC 1.26Values are calculated for the subset of clients in each program.

Average values (final survey round, clients)HSNP IBLI

Total Program Cost/Participant

47,600(2.7BN/57,811 HH)

43,200(99MM/3,297 contracts*1.44 contract/HH)

Marginal Cost of an Additional Participant

31,700 (13.9 transfers)

1,580 (3.25 TLUs)

Average cumulative cost/client by the final round (KSH)

Income from Milk Income per AE MUAC Cost structure Impact Impact/

KSHImpact Impact/

KSH1Impact Impact/

KSH2


992 0.021 394 0.083 1.097 0.0222,631 0.067 263 0.070 0.337 0.026


992 0.031 394 0.124 1.097 0.0332,631 1.667 263 1.666 0.337 0.623

All in real 2009 Kenya Shillings. Impacts are estimated using the average client value and costs provided below, and parameter estimates in the previous two slides. 1Results are multiplied by 10. 2Results are multiplied by 1,000.

Income from Milk Income per AE MUAC Cost structure Impact Impact/

KSHImpact Impact/

KSH1Impact Impact/

KSH2


992 0.021 394 0.083 1.097 0.0222,631 0.067 263 0.070 0.337 0.026


992 0.031 394 0.124 1.097 0.0332,631 1.667 263 1.666 0.337 0.623

All in real 2009 Kenya Shillings. Impacts are estimated using the average client value and costs provided below, and parameter estimates in the previous two slides. 1Results are multiplied by 10. 2Results are multiplied by 1,000.

IBLI’s Impacts vs. HSNP: Normalized by cost


VOI Mean VOI in Final PeriodHSNP 0.86HSNPC 3.76IBLI 0.60IBLIC 1.26Values are calculated for the subset of clients in each program.

Average values (final survey round, clients)HSNP IBLI


47,600(2.7BN/57,811 HH)

43,200(99MM/3,297 contracts*1.44 contract/HH)


31,700 (13.9 transfers)

1,580 (3.25 TLUs)

Average cumulative cost/client by the final round (KSH)

Janzen & Carter 2013 NBER

IBLI’s Impacts: Less adverse post-drought coping

Marsabit HHs received IBLI indemnity payments in October 2011, near end of major drought. Survey HHs with IBLI coverage report much better expected behaviors/outcomes than the uninsured:- 36% reduction in likelihood of distress livestock sales, especially

(64%) among modestly better-off HHs (>8.4 TLU)- 25% reduction in likelihood of reducing meals as a coping

strategy, especially (43%) among those with small or no herds

IBLI appears to provide a flexible safety net, reducing reliance on the most adverse behaviors undertaken by different groups.

IBLI’s Impacts: Household subjective well-being

Borana survey HHs report overall life satisfaction. In principle, insurance helps risk averse people even when it doesn’t pay out. But an imperfect product with commercial loadings might not.

There had been no payout in Ethiopia (pre-11/14). So use subjective well-being measures to assess welfare gains even w/o indemnities.

To deal with potential heterogeneity problems associated with SWB (attitudinal measures), we correct our SWB measures using hypothetical vignettes, using current best practice, and verify with alternative measures to ensure robustness of findings.

Hirfrot , Barrett, Lentz and Taddesse. 2014



Use randomized treatments to instrument for IBLI and then estimate:

There are at least two ways IBLI can influence SWB:1) Non-monetary (psychological) benefits or costs

• Insurance may give peace of mind about adverse outcomes (• Insurance could increase stress if basis risk is high (• Buyer’s remorse:

2) Monetary benefits or costs – effect on net income/wealth• Since premium payment reduces net income/wealth, indemnity

payment increases it, net indemnity payments will influence SWB.• This effect is captured by .



Ordered logit regression estimates (extensive margin) Model Model ModelDependent variable: Vignette corrected SWB

(1) (2) (3)

Predicted IBLI uptake 0.825*** 0.746*** 0.894*** (0.288) (0.235) (0.269)Predicted lapsed IBLI -0.453*** -0.463*** -0.469*** (0.157) (0.166) (0.170)Number of TLU owned 0.015** 0.013* 0.013* (0.007) (0.007) (0.007)Asset index 0.287*** 0.243*** (0.066) (0.067)Annual income (‘000 Birr) 0.003 0.003 (0.005) (0.005)Controls No No YesReera fixed effect No No YesObservations 1,530 1,530 1,530Number of groups (households) 550 550 550Clustered standard errors in Parenthesis; *** p<0.01, ** p<0.05, * p<0.1

Controls : Household head gender, age, age squared, schooling, household size, and household composition.

•IBLI has +++ effect on SWB

•Sig. buyer’s remorse effect

•But + peace of mind effect >> - buyer’s remorse effect



Ordered logit regression estimates (intensive margin) Model Model ModelDependent variable: Vignette corrected SWB

(1) (2) (3)

Predicted TLUs insured 0.126*** 0.131*** 0.136*** (0.035) (0.033) (0.033)Predicted lapsed TLUs -0.073*** -0.071*** -0.074*** (0.022) (0.023) (0.023)Number of TLU owned 0.015** 0.012* 0.013* (0.007) (0.007) (0.007)Asset index 0.331*** 0.295*** (0.066) (0.066)Annual income (‘000 Birr) 0.003 0.003 (0.005) (0.005)Controls No No YesReera fixed effect No No YesObservations 1,530 1,530 1,530Number of groups (households) 550 550 550Clustered standard errors in Parenthesis; *** p<0.01, ** p<0.05, * p<0.1

Controls : Household head gender, age, age squared, schooling, household size, and household composition.

•IBLI has +++ effect on SWB

•Sig. buyer’s remorse effect

•But + peace of mind effect >> - buyer’s remorse effect


• IBLI has a positive, statistically significant effect on HH well-being, even after premium payment and w/o any indemnity payments • IBLI coverage for 3 TLU moves a HH 1 step up the SWB scale• Insuring 15 TLU (roughly baseline sample mean) shifts HH

from lowest to highest SWB category• Ex post of contract, purchasers exhibit some buyer’s remorse in

the absence of indemnity payments.• But the positive effect of IBLI coverage is significantly higher

than the negative effect of buyer’s remorse.

Even with prospective buyer’s remorse, IBLI purchase improves subjective well-being.


Although IBLI offers incomplete coverage against herd loss and will not help all people, uptake is solid and IBLI has clear favorable impacts on purchasers.

IBLI offers a promising option for addressing poverty traps that arise from catastrophic drought risk … and impacts/$ > cash transfers

Thank you for your time, interest and comments!For more information visit www.ilri.org/ibli/

the favorable impact of index-based livestock insurance (ibli): results among ethiopian and kenyan...

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