MR Microinsurance_2012_03_29.indd - International Labour ...

172 Health insurance
7.3.5 Location of residence and WTP
The discussion presented above, based on the data reported in the cited studies,
leads to the assertion that WTP for health microinsurance is highly influenced
by various variables, notably the economic and socio-demographic status of
respondents, their financial exposure to healthcare costs and availability of
healthcare facilities. All these features vary markedly by location, within and
between countries. A simple comparison of results of the different studies
reviewed here is impossible due to the dissimilarity in methods used to elicit
WTP and to interpret the data. Specifically, the question arises as to whether the
significant explanatory variables identified at the level of single households can
account for the difference across locations.
We submit that the way to address this question with confidence would be to
include a “location variable” in multiple regressions performed to identify the
explanatory variables at the level of single households when the dataset includes
more than one location. Following this process could ensure that household features
and WTP levels would be elicited using the same methodology. Only two
of the studies reviewed have actually followed this procedure, Dror et al. (2007)
(comparing seven locations in India) and Onwujekwe et al. (2010) (comparing
six locations in Nigeria). Both studies reported that the location still remained a
highly significant explanatory variable for the variation in WTP, even in a multivariate
model including all the features of the individual households (economic
and socio-demographic status and recent experience with healthcare costs). This
finding suggests that the studies published so far on WTP for health microinsurance
have not yet identified all of the reasons that explain differences in WTP for
health insurance across communities; we cannot exclude that some features, e.g.
those reflecting cultural or lifestyle choices, could explain some underlying
reasons that have not been recognized. The impact of location as an explanatory
variable will need to be explored more thoroughly in future research.
7.4 Lessons learned and implications for practitioners
“We had to work backwards taking into consideration what the community
could pay, while creating a long-term, self-sustaining scheme.” This testimony of
an HMI practitioner in India presents the essential role of WTP estimates,
namely that members’ WTP determines the income side, and by extension also
what the insurer can offer by way of cover.
This review of field experiments has illustrated that all researchers agree that
WTP must be explored locally, because there is no “one-size-fits-all” option to
determine the income side of different HMI schemes operating in different settings.
The review has also shed light on the difficulties associated with obtaining
WTP estimates that would be relevant locally. Such estimates require data that is