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), [email protected]
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