Indexing the soil 31
feasible in economic terms (RM: 154). Mapping, for its part, gains its full
effect only through the way in which it is linked with modelling.
To objectify environmental and weather-related risks, a map of spatially
distributed risk factors is created, and the assemblage of these factors is
treated as a proxy for events that cause damages for farmers. Because the
data is processed by third-party providers, the objectivity of index-based
risk modelling is institutionally guaranteed and thus the insurance policies
can be transferred to global financial markets. From an insurer’s point of
view, the area-based perception of risks has the important benefit of eliminating moral hazard in the contract, as it is impossible for policyholders to
affect the likelihood of payouts with their own behaviour (RM: 9–10).
On top of the map representing soil and weather risk patterns, a layer
of pricing models is added to define how the spatially standardised risk
events can be insured, thus providing the socioeconomic logic for the
process (RM: 10). The end result is an index that should be able to represent
financially homogenous risk events that affect all policyholders uniformly
within a specified geographic area:
Based on the agreed-on inputs, the actuarial analyst produces equitable
premiums for each geographical area. […] In this case, the goal of the
analysis is not to find one overall premium rate that can be applied to
the total portfolio of geographical areas, but to find the equitable premium for each area that takes into account each area’s specific characteristics and risks. It is important to note that the equitable premium is
for the area, not for individual insured units.
(RM: 62)
Basis risk
However, the area-based standardisation of risk information is simultaneously the main modelling-related problem that has thus far appeared unresolvable for index insurance projects. Indeed, Johnson (2021) argues that a
central reason for the failures of index insurance programmes is the basis risk
that plagues the product design. Basis risk refers to the difference between
risk events represented by the index and the actual losses experienced by
the policyholders. In other words, if the payout trigger levels defined by an
index insurance product do not accurately correspond with the actual damage that the instrument models, there will be situations where policyholders
have paid their premiums, yet suffer losses caused by the very risk event
that the product is supposed to cover. According to Johnson, this raises
the question of whether index insurance can fulfil its assumed potential as
a risk technology (Johnson 2021). However, it is significant that, according
to RM, such situations are simply inevitable: ‘It is important to note that
there will be situations in which an insured party experiences a loss attributable to a hazard event but does not receive a payout’ (RM: 10). Yet the
feasible in economic terms (RM: 154). Mapping, for its part, gains its full
effect only through the way in which it is linked with modelling.
To objectify environmental and weather-related risks, a map of spatially
distributed risk factors is created, and the assemblage of these factors is
treated as a proxy for events that cause damages for farmers. Because the
data is processed by third-party providers, the objectivity of index-based
risk modelling is institutionally guaranteed and thus the insurance policies
can be transferred to global financial markets. From an insurer’s point of
view, the area-based perception of risks has the important benefit of eliminating moral hazard in the contract, as it is impossible for policyholders to
affect the likelihood of payouts with their own behaviour (RM: 9–10).
On top of the map representing soil and weather risk patterns, a layer
of pricing models is added to define how the spatially standardised risk
events can be insured, thus providing the socioeconomic logic for the
process (RM: 10). The end result is an index that should be able to represent
financially homogenous risk events that affect all policyholders uniformly
within a specified geographic area:
Based on the agreed-on inputs, the actuarial analyst produces equitable
premiums for each geographical area. […] In this case, the goal of the
analysis is not to find one overall premium rate that can be applied to
the total portfolio of geographical areas, but to find the equitable premium for each area that takes into account each area’s specific characteristics and risks. It is important to note that the equitable premium is
for the area, not for individual insured units.
(RM: 62)
Basis risk
However, the area-based standardisation of risk information is simultaneously the main modelling-related problem that has thus far appeared unresolvable for index insurance projects. Indeed, Johnson (2021) argues that a
central reason for the failures of index insurance programmes is the basis risk
that plagues the product design. Basis risk refers to the difference between
risk events represented by the index and the actual losses experienced by
the policyholders. In other words, if the payout trigger levels defined by an
index insurance product do not accurately correspond with the actual damage that the instrument models, there will be situations where policyholders
have paid their premiums, yet suffer losses caused by the very risk event
that the product is supposed to cover. According to Johnson, this raises
the question of whether index insurance can fulfil its assumed potential as
a risk technology (Johnson 2021). However, it is significant that, according
to RM, such situations are simply inevitable: ‘It is important to note that
there will be situations in which an insured party experiences a loss attributable to a hazard event but does not receive a payout’ (RM: 10). Yet the