Different source of basis risk:

- Model (index and payout function)

- Spatial (distance to station)

- Idiosyncratic basis risk (individual / plot specificities).

Weather index-based insurance: building on two ex ante evaluation in the sudano-sahelian zone

A. Leblois (École Polytechnique), P. Quirion (CIRED & CNRS), B. Sultan (Locéan, IRD), leblois@centre-cired.fr

4 – Additional results

-Simple indices does not have much lower performance in both studies.

- Simulated sowing dates perform well for millet

- Sowing date need to be observed in the case of cotton (institutional issues:

delay in input delivery)

- Considering micro-fertilised millet plots the incentive to use insurance but

does not insurance gains).

- Sodecoton’s inter-annual implicit price insurance (announce price before

sowing) impact on certain equivalent income > index insurance

• We look for an optimal weather-index insurance policy for cotton farmers.

• Cotton growers pay a premium every year and receive an indemnity if

weather index < defined threshold = strike (S).

• We assume an insurer’s charging rate (10% of all indemnifications), & a

transaction cost (1% of average yield).

• Insurance parameters (S, λ, M) optimized in order to maximize farmers’

certain equivalent income (CEI), with constant relative risk aversion utility

function including initial wealth (W):

Evaluation of risk aversion of cotton producers using lotery games: half of

the sample (N=64) have a relative risk averion > 1.

tested relative risk aversions: [1, 2 , 3].

1 – Data

Yield:

Rainfall:

2 – Methods: insurance contract optimization

Matching yield &high density daily rainfall data.

Indemnification: function of 3 insurance policy parameters:

strike (S), max. indemnity (Μ) and a slope-related

parameter (λ)

S

Μ

λ index

3 – High basis risk at different scales

Sector level cotton yields (33

sectors, 1977-2010, N=849),

obtained from Sodecoton (the

Cameroonian cotton company).

Rainfall (small circles)

Weather (large circles) stations,

> one per sector different

sources: Sodecoton, IRD, GHCN (NOAA).

Normalised Diff. Vegetation

Index (NDVI) was also

considered.

Niger Northern Cameroon

Plot specific yields of 30 farmers

per villages surveyed (10 villages,

2004-2011, N=1780). Each farmer

has one plot with traditional

growing techniques (low or no

fertiliser use) and one plot with

micro-fertilisation.

Rainfall station in each of the 10

villages surveyed.

Plots < 3 kilometers from the

nearest rainfall station.

Cameroon: certain equivalent income gain of weather

insurance always < 50% lower than the same insurance

using the yield observation.

Unique calibration of index insurance

parameters for heterogeneous zones

subsidization of the dryest one

& taxation of the most humid one.

≠ calibration of index insurance

parameters for each rainfall zones:

cross-subsidisation

but does not basis risk.

Indices considered:

Cumulative rainfall over the simulated and observed rainy

season and for ≠ critical growing phases.

Duration of the rainy season in days.

NDVI (satellite data) in Cameroon.

Very low CEI gains: ≈1.5% for millet in Niger lower than 1% in Cameroon

Niger: dry area (<500mm/year) and millet

yields largely depends on rainfall but:

High intra-village variation of yields