Dynamic Effects of Index Based Livestock Insurance on Household Intertemporal

Behavior and Welfare

Munenobu Ikegami, Christopher B. Barrett, and Sommarat Chantarat

International Livestock Research Institute (ILRI), Cornell University, and The Australian National University (ANU)

www.ilri.org/ibli

At “Mobile Pastoralism, Index Insurance, Computational Sustainability and Policy Innovations for the Arid and Semi-arid Lands of East Africa”, ILRI, on June 10,

2015

0. Outline

1. Introduction– Literature– Central Question

2. Methodology

3. Modela. Autarky

b. under IBLI

4. Resultsa. Key Findings & policy implication based on model prediction

b. Compare model prediction with data• Next step

1. Introduction• Index-based insurance

– Reduce vulnerability of agricultural households– Increase their resilience and welfare

• The focus of previous/ealry studies – Reduce Income fluctuation = direct effects

• The focus of this study– Further positive effects:

• Reduce precautionary saving• Encourage investment in productive asset• How large are the indirect effects?

1. Introduction• Previous studies on further positive effects

– Ex-ante evaluation• Crop and technology adoption: De Nicola (2014)• Demand and impact of IBLI under poverty trap:

– Chantarat, Mude, Barrett, Turvey (2014)– Janzen, Carter, Ikegami (2015)

• Environmental feedback: Muller, Quaas, Frank, Baumgartner (2011)

– Reduced form ex-post evaluation of IBLI: • Carter and Janzen (2014)• Chebelyon, Lyons (2015)• Jensen, Barrett, Mude (2015)• Toth et al. (2014)

• This paper – focus on joint decisions on investment and insurance purchase– Structural ex-post evaluation

– Omit environmental feedback and poverty trap

1. Introduction• Does Index Based Livestock Insurance (IBLI)

induce pastoralists to increase their herd size?– Livestock as productive asset => increase their herd– Livestock as precautionary saving => decrease their herd– Does IBLI induce (further) over grazing and

environmental degradation?

• How much of their herd and under what conditions do pastoralists insure?– Is the current design of the insurance contract OK?

• How much would IBLI reduce pastoralists’ vulnerability and improve welfare in the long run? – How large are “the further” effects?

2. Methodology• Model

– household decisions on livestock investment and insurance purchase

– Stochastic structure of livestock accumulation

• Data– IBLI Marsabit Household Survey from 2009-2013

(Round 1-5)– Vegetation condition: Normalized Differenced Vegetation

Index (NDVI) from 2001 to 2013 (MODIS NDVI)

2. Methodology• Fit the model to data

– 1st Step: Estimate milk production and stochastic structure of livestock accumulation

– 2nd Step: Compute optimal decisions on livestock investment and insurance purchase

• Counter-factual / ex ante policy simulation• Compare data with model prediction (ex post impact

evaluation)

3.a. Model: Autarky (without IBLI)• Maximize utility over time

• Budget constraint

• Livestock accumulation

• Birth rate, idiosyncratic and covariate mortality rate

• Vegetation condition dynamics

3.b. Model: under IBLI:

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

Period of continuing observation of NDVIfor constructing LRLD mortality index

LRLD season coverage SRSD season coverage

1 year contract coverage

Sale periodFor SRSD

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

Period of NDVI observationsfor constructing SRSDmortality index

Prior observation of NDVI sincelast rain for LRLD season

Sale periodFor LRLD

Sale periodFor SRSD

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

Prior observation of NDVI since last rainfor SRSD season

Short Rain Short Dry Long Rain Long Dry Short Rain Short Dry

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

Period of continuing observation of NDVIfor constructing LRLD mortality index

LRLD season coverage SRSD season coverage

1 year contract coverage

Sale periodFor SRSD

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

Period of NDVI observationsfor constructing SRSDmortality index

Prior observation of NDVI sincelast rain for LRLD season

Sale periodFor LRLD

Sale periodFor SRSD

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

Prior observation of NDVI since last rainfor SRSD season

Short Rain Short Dry Long Rain Long Dry Short Rain Short Dry

Temporal coverage of current IBLI (1 year with 2 seasons)IBLI Contract Feature: Temporal Structure

3.b. Model: under/with IBLI• Budget constraint with insurance premium

– Insurance premium• for Upper Marsabit• for Lower Marsabit

• Livestock accumulation with indemnity payout

– Indemnity payouts – Index:

4. Results

a. Key Finding & Policy Implication based on model prediction

b. Compare model prediction with data

Key Findings & Policy Implications• Does IBLI induce pastoralists to increase their herd

size?– Livestock as productive asset => increase their herd– Livestock as precautionary saving => decrease their herd– Does IBLI induce (further) over grazing and

environmental degradation?

Net Livestock Investment in Upper Marsabit

-.2

-.1

0.1

.2i_

t

0 10 20 30k_t

autarky, CZNDVI_pos,t-1 = -15 autarky, CZNDVI_pos,t-1 = -5autarky, CZNDVI_pos,t-1 = 5 IBLI, CZNDVI_pos,t-1 = -15IBLI, CZNDVI_pos,t-1 = -5 IBLI, CZNDVI_pos,t-1 = 5IBLI, CZNDVI_pos,t-1 = 15

Numbers of obsedrvations under autarky and IBLI are 2692 and 4596 respectively.No observation with CZNDVI_pos_t-1 = 15 under autarky

investment by vegetation condition based on the model

Initial asset and last asset under autarky and IBLI by initial asset level and non-livestock income in Upper Marsabit

Key Findings & Policy Implications• Does IBLI induce pastoralists to increase their herd

size?– Livestock as productive asset => increase their herd– Livestock as precautionary saving => decrease their herd– Does IBLI induce (further) over grazing and

environmental degradation?– => Pastoralists would invest less in livestock in bad

seasons (in order to buy IBLI)– => but insurance/safety net effects let pastoralists

accumulate more livestock– => reduced risk exposure but might bring environmental

delegation and decreased productivity

IBLI purchase and non-livestock income

05

1015

k_til

de_t

,t+1

0 10 20 30k_t

data, ynl = 0 data, ynl = 10Kdata, ynl = 20K model, ynl = 0

model, ynl = 10K model, ynl = 20k

number of observation is 3681

insured livestock in bad season by non-livestock income

Key Findings & Policy Implications• How much would IBLI reduce pastoralists’

vulnerability and welfare in the long run? – IBLI is more beneficial for vulnerable households with

less non-livestock income as an alternative insurance mechanism

Key Findings & Policy Implications• How much of their herd and under what conditions

do pastoralists insure?– households may insure herd sizes larger than they own as

insurance against covariate income shocks more broadly – households may divest livestock in order to buy

insurance

IBLI purchase and forage condition

010

2030

k_til

de_t

,t+1

0 10 20 30k_t

data, CZNDVI_pos,t-1 = -15 data, CZNDVI_pos,t-1 = -5data, CZNDVI_pos,t-1 = 5 data, CZNDVI_pos,t-1 = 15model, CZNDVI_pos,t-1 = -15 model, CZNDVI_pos,t-1 = -5

model, CZNDVI_pos,t-1 = 5 model, CZNDVI_pos,t-1 = 15

number of observation is 5519.

insured livestock by vegetation condition

Results: IBLI purchase and forage condition

010

2030

k_til

de_t

_t+

1

0 10 20 30k_t

data, CZNDVI_pos,t-1 = -15 data, CZNDVI_pos,t-1 = -5data, CZNDVI_pos,t-1 = 5 data, CZNDVI_pos,t-1 = 15model, CZNDVI_pos,t-1 = -15 model, CZNDVI_pos,t-1 = -5

model, CZNDVI_pos,t-1 = 5 model, CZNDVI_pos,t-1 = 15

The number of observations is 437. State variables are from observations with only k_tilde_t_t+1 > 0 in data.

insured livestock by vegetation condition

Key Findings & Policy Implications• How much of their herd and under what conditions

do pastoralists insure?– households seek to buy more insurance when current

vegetation conditions are bad and they expect poor range conditions – and thus a higher livestock mortality rate – in the following season

– Is the current design of the insurance contract OK?• Do not adjust pricing as baseline range conditions change • do not use an index that is conditional on range conditions as of

the contract sales date

– => Intertemporal opportunistic behavior problem– => negative for insurance company

4. Results

b. Compare model prediction with data

model predicts too large TLU insured in bad seasons

010

2030

k_til

de_t

,t+1

0 10 20 30k_t

data, CZNDVI_pos,t-1 = -15 data, CZNDVI_pos,t-1 = -5data, CZNDVI_pos,t-1 = 5 data, CZNDVI_pos,t-1 = 15model, CZNDVI_pos,t-1 = -15 model, CZNDVI_pos,t-1 = -5

model, CZNDVI_pos,t-1 = 5 model, CZNDVI_pos,t-1 = 15

number of observation is 5519.

insured livestock by vegetation condition

model predicts too large offtake in bad season under IBLI

-.2

-.1

0.1

.2i_

t

0 10 20 30k_t

data, CZNDVI_pos,t-1 = -15 data, CZNDVI_pos,t-1 = -5data, CZNDVI_pos,t-1 = 5 data, CZNDVI_pos,t-1 = 15model, CZNDVI_pos,t-1 = -15 model, CZNDVI_pos,t-1 = -5model, CZNDVI_pos,t-1 = 5 model, CZNDVI_pos,t-1 = 15

Note: Seasons are those when IBLI are on sale. Number of observations is 4305.

investment by vegetation condition under IBLI

model predicts too large offtake even under autarky

-.1

-.0

50

.05

.1i_

t

0 10 20 30k_t

model, CZNDVI_pos,t-1 = -15 model, CZNDVI_pos,t-1 = -5

model, CZNDVI_pos,t-1 = 5 data, CZNDVI_pos,t-1 = -15data, CZNDVI_pos,t-1 = -5 data, CZNDVI_pos,t-1 = 5

Note: Seasons are the first 3 seasons before IBLI launch. No observation with CZNDVI_pos_t-1 = 15 Number of observations is 2536.

investment by vegetation condition under autarky

Next step• Model predicts large offtake and large TLU insured

in bad seasons but data do not show such intertemporal opportunistic behaviour with such magnitudes

• Model predicts too large offtake even under autarky in good seasons under IBLI

• For too large offtake, we relax assumption of constant TLU value over time

• For too large TLU insured, we introduce learning of indemnity payout function (maybe in a separate following paper)

Thank you

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www.ilri.org/ibli/