optimally diversifying income streams). Morsink’s findings are largely consistent with the recent microinsurance demand literature reviews already referenced (De Bock and Gelade, 2012 and Eling et al, 2013).

# **Health microinsurance**

Leatherman (2011) points out that approximately 100 million people slip under the poverty line annually – and 150 million more suffer financial catastrophe – as a result of making payments for health services. However to date only approximately 40 million people have some form of health microinsurance coverage. One major problem she identifies is that while catastrophe health events are more easily insured (and hence offered by insurers) the poor perceive more value in coverage for higher frequency low cost services – in developing countries pharmaceuticals account for 30% to 50% of total health care expenditures.

Schmidt and Mayindo (2006) explicitly investigated the trade-off between price and total premium pool for health insurance in Rwanda. They found the price elasticity was so high that <mark>‘the goals of maximizing health revenue and maximizing participation in community-based health insurance are mutually exclusive.’ On a more positive note they were able to segment the population by income level and posit a limited subsidy requirement (which would in this case apply to the bottom quartile of income earners).</mark> In their literature review De Bock and Ontiveros (2013) focus mainly on health microinsurance. They examine statistical impact findings (allowing for self-selection bias) and the channels through which microinsurance affects outcomes. Key headings and findings are as follows:

# **_Use of health care services_**

Studies in Rwanda, India, Nicaragua, and Nigeria showed that microinsurance improved access to health services and utilization rates. In addition households with increased access were more likely to engage in risk mitigation (purifying water, etc.). Results were less positive in studies in Cambodia and Kenya.<sup>17</sup>

# **_Financial protection_**

Most studies find a reduction in out of pocket expenses when there is access to microinsurance. Levine and Polimeni (2012) in an Indian study find a 44% reduction in treatment costs for serious health incidents once insured while Gustafsson –Wright (2013) finds a 40% reduction in a Nigerian study. Where the effect was not observed the reason appeared to be high co-payments or limited coverage.

# **_Health status_**

There are few studies examining the impact of health microinsurance on health status, with most relying on anthropometric measures such as height, weight, wastage metrics and biomarkers. To date outcome findings have been mixed, consistent with ambiguous findings on the use of preventative medicine.

# **_Distributional effects_**

The equity of results across income groups and ages appears to be a function of societal homogeneity, with greater homogeneity leading to a better allocation of outcomes. A Bangladesh study (Chakrabarty, 2012) found that child 17 Savitha and Kiran (2013) find similar mixed results in the literature survey included in their study of health seeking behavior in Karnatak, India. A key determinant appears to be the accessibility and quality of primary and secondary health care facilities.

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