MR Microinsurance_2012_03_29.indd - International Labour ...

Willingness to pay for health microinsurance
usually obtained through household surveys and other costly and time-consuming
investigations. These constraints make it impracticable to obtain local data in
each place where HMI is launched.
However, if the income of the target population is known, it could serve as an
anchor for initial estimation of WTP, based on the analysis of the studies
reviewed here and shown in Table 7.2. It has been shown in Table 7.3 that WTP
estimates (when expressed as a percentage of income) converge mostly to a relatively
narrow range around two per cent of income.
It has also been shown in Tables 7.4, 7.5 and 7.6 that various other parameters
influence WTP levels. Therefore, we might be able to extrapolate WTP levels
known in one location to other comparable locations using knowledge of these
parameters. For instance, if in one location we know the level of education of
household heads as well as the WTP values, and in another location we only
know that household heads have a higher level of education, we would expect
WTP levels in that second location to be higher. Similarly, ample supply of
quality health care, or higher cost of care, or higher morbidity would all be
expected to lead to higher WTP.
We have also seen that WTP levels are sensitive to benefit package design.
Therefore we would expect that involving the target population in the design
phase would increase WTP levels.
However, for the time being, it is impossible to devise a simple standard
model to predict WTP everywhere, because of the large diversity of methods
used to elicit and estimate WTP in the different studies reviewed in this chapter.
Such a model could hopefully be devised in line with the methods used to
determine WTP and standardized for the purpose of analysing explanatory
variables.
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