Explaining Index Based Livestock Insurance in Kenya through Games:

Describing Our Approach and Analyzing Game Play.

John McPeak,215 Eggers Hall,

Department of Public Administration and International Affairs, Maxwell School of Citizenship and Public Affairs,

Syracuse University.(315) 443-6146

jomcpeak@maxwell.syr.edu

IBLI – Index Based Livestock Insurance• This research was made possible by support provided in part by

the US Agency for International Development (USAID) Agreement No. EDH00-06-0003-00 awarded to the Assets and Market Access Collaborative Research Support Program (AMA CRSP).

• This research was conducted in collaboration with Andrew Mude, Munenobu Ikegami, Sommarat Chantarat, Chris Barrett, and Michael Carter and other individuals at the International Livestock Research Institute, Cornell University, University of Wisconsin-Madison, The Australian National University, and the University of California-Davis.

• All views, interpretations, recommendations, and conclusions expressed in this paper and presentation are those of the author and not necessarily those of the supporting or collaborating institutions.

TIMELINE• ALRMP gathering data from late 90’s to present in Arid Districts of Kenya.• PARIMA, a GL-CRSP funded project, active in Northern Kenya / Southern

Ethiopia 1997-2008.• Quarterly panel data on 330 households from 2000 to 2002.• DFID launches HSNP in 2007; cash transfers to reduce poverty• IBLI begins in 2007 to develop index based livestock insurance• 2008 , Index Construction, WTP study, first round of extension games

played.• 2009 game revision, baseline survey written and run, and second round

of games played in 2009-10• 2010 First round of insurance purchase in February-March, repeat

survey in October to December• 2011 Second round of insurance purchase in February-March repeat

survey in the field now. Some problems with the insurance partner led to lower sales than expected.

The Basic Idea• is Income for household i at time t• is a vector of productive assets for hh i , time t• is the rate of return on these productive assets, possibly as a

function of asset levels.• is the household and period specific shock to the return on assets.• are household specific but time invariant income flows• is household and time specific transitory income• is household and time specific measurement error.

– From Barrett et al. (2006) JDS paper.

• Transfers such as Ui could raise income, impact future asset stocks, influence the rate of return to existing assets

• Assets could be subject to stochastic shocks, say , with theta + gamma defined as shocks over the interval [0,1] from some stochastic distribution.

Research Design

• IBLI is asset protection, reduce impact of shocks to A.

• HNSP is cash transfer, works like U.• Sites with IBLI and HSNP• Sites with only IBLI or HSNP• Sites with neither• Full comparison is ahead

Top photo by John McPeak,Bottom photo by Sharon Osterloh

Google Earth Map of IBLI sites.Top photo by Chris Barrett, Bottom photo by John McPeak

NDVI

Normal Year (May 2007) Drought Year (May 2009)

From Chantarat and Mude 2011

Annual Deviation of NDVI 1999-2006

1999 2000 2001 2002 2003 2004 2005 2006

-40%

-30%

-20%

-10%

0%

10%

20%

30%

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50%

Dirib GumboKargiLogologoNorth HorrAVERAGE

What Is the Index Part?

• Figure 3: zNDVI for North Horr and Dirib Gumbo 1990 to 2008 by season

1990 1 3

1991 1 3

1992 1 3

1993 1 3

1994 1 3

1995 1 3

1996 1 3

1997 1 3

1998 1 3

1999 1 3

2000 1 3

2001 1 3

2002 1 3

2003 1 3

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2008 1

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Dirib Gumbo

North horr

Why Index Insurance for Livestock?

• Figure 1: Share of Total Income from Different Sources, PARIMA data, Kenya sites, 2000-2002

40%

11%4%

16%

13%

8%

6%4%

Milk

Livestock Sale

Livestock Slaughter

Salary or Wage

Food Aid Value

Trade or Business

Crop Value

Net gifts

Why Index Based Livestock Insurance?

Figure 2: Mean Herd Size and Income per Person per Day, PARIMA data, 2000-2002

Jun-00Jul-0

0

Aug-00

Sep-00

Oct-00

Nov-00

Dec-00Jan

-01

Feb-01

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$0.00

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$0.70

Kenya Household Herd TLU (LHS)Kenya Income $ per person per day(RHS)

Nutrition as well as income impact

Mude et al., Food Policy, 2009

What is the Index Predicting?

Jun-00 Sep-00 Dec-00 Mar-01 Jun-01 Sep-01 Dec-01 Mar-02 Jun-020

100

200

300

400

500

600

700

800

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1000

otherDrank bad watertoo much rain/ too coldold agekilled to save motheraccidentpredatordiseasedrought/lack of pasture/starvation/emaciation

Total number of animal deaths from the Kenya sample of 180 households per round and reason cited for the death.

Creating informed Demand: The Index Insurance Game

• Random Draw Initial Herd Size• Pay ‘consumption’

– Generates Bifurcating Asset Dynamics• Buy insurance• Draw idiosyncratic outcome• Draw covariate outcome• Calculate herd size change• Pay out on insurance policies if applicable• Move to new round• Each round is a rainy season dry season six month period

Creating informed Demand: The Index Insurance Game

2008 All Sites

Covariate value -30% -20% 0% 10% 20%Probability of value 6% 6% 13% 44% 31%Idiosyncratic value -10% 0% 10%

Probability of value 33% 33% 33%

2009 Upper Marsabit

Covariate value 40% (-25%) 30% (-15%) 20% (-5%) 10% (5%) 0% (15%)

Probability of value 10% 10% 10% 10% 60%Idiosyncratic value -15% 0% 15% 30% Probability of value 30% 50% 10% 10%

2009 Lower Marsabit

Covariate value 30% (-15%) 20% (-5%) 10% (5%) 0% (15%)

Probability of value 15% 15% 10% 60% Idiosyncratic value -15% 0% 15% 30% Probability of value 25% 50% 12.50% 12.50%

Mean Net Change (Threshold)

Variance Net Change Price per month per TLU Pricing in Game

2008 All Sites

7.5% (6.7 TLU)

0.019 17 KSH 100 KSH covers 6 months

2009 Upper Marsabit

10.0% (5 TLU)

0.020 63 KSH 750 KSH covers 12 months

2009 Lower Marsabit

9.0% (5.6 TLU)

0.022 63 KSH 750 KSH covers 12 months

2008 Game Play, Karare Kenya

GameWeb for 2009

http://blip.tv/ilri/development-of-the-world-s-first-insurance-for-african-pastoralist-herders-3776231Start at about 6:45

How Did People Play the Game

• The 2008 data had been analyzed before, and is presented in an Agricultural Finance Review (2010) article.

• The 2008 and 2009 were combined to be presented here.• Specification based on 2008 findings• Issues: 2009 cost more, could only be bought in even

rounds, and had a different probability distribution.• Control by using price per TLU per month.

– Trying to use Expected Herd growth also does not work as too much covariation with site dummy and game play parameters defined by site.

Bifurcating Asset Dynamics

R0 R1 R2 R3 R4 R5 R6 R7 R8 R9 R100.0

5.0

10.0

15.0

20.0

25.0

S6_2008S8_2008S10_2008S8_2009S12_2009S15_2009

Round of the Game

TLU

Game PlayPercent of Game Herd TLU Insured Number of Game Herd TLU insured

β SE β SE meansRound 0 TLU 0.008 0.00312 ** 0.076 0.03438 ** 9.426

Round 0.009 0.01087 0.069 0.1198 5.675Round squared/100 -0.031 0.08908 -0.001 0.98202 0.394Lag covariate shock -0.133 0.06357 ** -1.54 0.70075 ** 0.054

Lag idiosyncratic shock -0.298 0.08832 *** -3.559 0.97363 *** 0.004Lag % net TLU change 0.055 0.04174 0.952 0.46017 ** 0.079

Round TLU -0.013 0.0033 *** 0.707 0.03639 *** 9.588Round TLU /1000 -0.057 0.09267 -11.277 1.02165 *** 0.117

Price/month Insurance -0.014 0.00324 *** -0.11 0.03577 *** 3.277Dirib Gombo 0.758 0.03599 *** -0.037 0.39671 0.195

Kargi 0.735 0.03582 *** -0.243 0.39487 0.190Karare 0.736 0.03671 *** -0.237 0.40466 0.165

Logologo 0.676 0.03654 *** -0.76 0.4028 * 0.167North Horr 0.746 0.03703 *** 0.173 0.40822 0.108

Kalacha 0.804 0.04604 *** 0.738 0.50754 0.030Turbi 0.84 0.04738 *** 1.023 0.5223 * 0.027Ilaut 0.82 0.04692 *** 1.012 0.51722 * 0.025

Loiyangalani 0.71 0.04275 *** -0.471 0.47124 0.061South Horr 0.746 0.04554 *** -0.32 0.50206 0.031

R2 0.88 0.85 F 1111.4 857.6 N 2865 2865

Predicted TLU Insured (y-axis) as function of game herd size (x-axis)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 350

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Start 8Start 12Start 15

Implied Price Elasticity of Demand

• Implied price elasticity of demand moving from 16.7 KSH per month to 62.5 KSH per month– %ΔQ=-8%, %ΔP=73%, ε=-0.073

• Chantarat finds much more elastic results, more inelastic for poorer, more elastic for wealthier in the WTP study.

Response to Lagged Game Variables

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%

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%

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%

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%

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%

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CovariateIdiosyncaticNet change

Shock or net % herd size change

TLU INSURED

Game Play with Personal Characteristics (site dummy results omitted)

Percent of Game Herd TLU Insured Number of Game Herd TLU insured

β s.e. β s.e. Means

Round 0 TLU 0.007 0.00324 ** 0.06 0.03563 * 9.426Round 0.01 0.01091 0.067 0.1202 5.675

Round squared -0.043 0.08945 -0.027 0.98512 0.394Lag % net TLU change 0.066 0.04233 0.976 0.46623 ** 0.079

Lag covariate shock -0.136 0.06425 ** -1.506 0.7076 ** 0.054Lag idiosyncratic shock -0.272 0.08988 *** -3.28 0.98994 *** 0.004Beg. TLUs each round -0.016 0.00341 *** 0.688 0.03756 *** 9.588Beg. TLUs squar~1000 0.034 0.09591 -10.371 1.05636 *** 0.117

Price/month -0.003 0.00629 0.097 0.0693 3.277Head is male 0.058 0.01155 *** 0.544 0.1272 *** 0.728Age of head -0.003 0.00173 * -0.04 0.0191 ** 48.044

Head age squared/1000 0.025 0.01564 0.336 0.17227 * 2.548

Head attended school -0.027 0.01573 * -0.493 0.17323 *** 0.751

How many graduated HS 0.035 0.00964 *** 0.341 0.10616 *** 0.513Household size 0.001 0.00242 0.009 0.0267 6.023

Dependency ratio 0.04 0.02402 * 0.477 0.26453 * 0.467Herd size 0.002 0.00055 *** 0.025 0.00609 *** 19.167

Herd size sq/1000 -0.007 0.00473 -0.11 0.05213 ** 0.830% immature animals 0.029 0.01434 ** 0.541 0.15789 *** 0.223

R2 0.88 0.85 F 720.6 551.0 N 2759 2759

Logo

logo

Loinga

lani

Kargi

Karare

South Horr

North Horr

Dirib Gombo

Kalach

aTu

rbi

Iliaut

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Game HerdReal Herd

Evidence on Sales

October 2009: How many in a random sample of 924 people said they would buy insurance? 98%

First round of sales in early 2010 covering March 2010 to February 2011

October 2010: How many in a random sample of 723 people did buy insurance? 35%

Second round of sales in early 2011 covering March 2011 to February 2012

Currently working to manage a third round of contracts.

don't have

money t

o spend on in

sura

nce

did not underst

and insu

rance

well e

nough

waiting to se

e what h

appens to th

e people who bought t

he insu

rance

don't have

enough animals

did not have

an opportunity

to buy i

t

don't think i

nsura

nce w

ill help m

e

afraid of u

ncerta

nity in

insu

rance

don't tru

st any i

nsura

nce co

mpanies

discoura

ged by someone in

the co

mmunity

can re

ly on fa

milly and fr

iends

can re

ly on other p

ersons/s

ervice

(specif

y)

other (sp

ecify)

0%

5%

10%

15%

20%

25%

If not, why not from the 2010 sample?

March 2010_ February 2011

March 2011_February 2012

People buying contracts

1,979 638

TLU Insured 5,375 TLU 1,086 TLU

Value insured in USD

$1,193,080 $218,083

Insurance company received

$77,636 $13,641

Subsidy $31,039 $5,456

Herders Paid $46,597 $8,185

Subsidy as % of value insured

2.60% 2.50%

Herder paid as % of value insured

3.90% 3.75%

Insurance c. rec as % of value

6.50% 6.25%

TLU insured per person average

2.8 TLU 1.7 TLU

Premium per person average

$39.23 ($15.68 subsidy, $23.55 herder)

$21.38 ($8.55 subsidy, $12.83 herder)

Payout total $0 $19,947

Payout per person average

$0 $31

1 TLU (Tropical Livestock Unit)= 250 kg. liveweight =10 sheep or goats = 1 head of cattle = 0.7 camel.15% predicted mortality ‘trigger’.Sales in Early 2010, again in early 2011

Conclusions

• Really just getting going on this analysis so preliminary

• Chantarat’s WTP sample has more covariates that merit comparison with game play for 2008

• 2010 repeat has actual purchases, so we can look at both WTP and Game Play predictions in contrast to what happened.

Conclusions

• Major drought currently testing the product’s ability to insure against asset risk.

• Behavioral responses still to be identified:– More livestock with lower risk?– Higher sales from existing herd (higher offtake

rates) if need to self-insure increases herd size?– Collateral / credit constraint impact?