MR Microinsurance_2012_03_29.indd - International Labour ...

What is the impact of microinsurance?
3.1.2 How is impact assessed?
The challenge of impact assessment is to properly attribute causality to the intervention
in question (in this case insurance). For example, if a person purchased a
health product and subsequently used more medical care, would the insurance cover
be responsible? What if the price of clinic consultations had simultaneously dropped,
or if the person’s subscription had coincided with the beginning of the rainy season,
and by extension the increased incidence of diseases such as malaria? Given these
possibilities, the best way to determine impact involves studying the same person
twice – once with insurance and once without – over the same period. Since this is
impossible however, researchers use various techniques to select uninsured
comparison groups to determine what would have happened in the absence of
insurance.
However, choosing comparison groups is also difficult because a mere
comparison of the results of insured and uninsured individuals, for instance,
disregards characteristics that can influence both the insurance purchasing decisions
and outcomes of the people in question. If those buying insurance are
generally more ill, for example, they might use more medical care regardless – a
phenomenon known as self-selection in impact analysis and adverse selection in
insurance. Impact assessments that fail to take account of self-selection are susceptible
to bias, or the systematic over- or under-estimation of an intervention’s
actual effects.
Impact evaluations must therefore create suitable control groups – a challenge
that different study designs overcome with varying degrees of success. Randomized
controlled trials (RCTs), for example, are considered in this respect the most rigorous
approach available. By randomly allocating study subjects to receive insurance
or not, RCTs distribute the observable and unobservable characteristics that could
potentially influence the outcomes equally on average across the insured treatment
group and uninsured control group, for sufficiently large sample sizes.
RCTs are often complicated, expensive and impractical to implement, and
subsequently less robust techniques predominate instead. Of these, “quasi-experimental”
approaches, which use statistical or econometric procedures to improve
the affinity between comparison groups, are considered more rigorous.
However, the majority of currently completed microinsurance impact
assessments merely contrast study subjects’ results without correcting for the selfselection
bias. As such – and even after using a method called regression analysis to
take account of mitigating factors such as income, race or gender – they produce
information that is susceptible to bias and thus have to be considered with
caution. 1
1 For a more in-depth discussion of research design for microinsurance impact assessment, see Radermacher
et al. (2012).
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