# **The demand function**

Clarke and Dercon touch only briefly on demand constraints, pointing to the potential of a substantial investment in education. Matul et al (2013) explore this aspect in some detail via a review of more than 30 studies. They find that trust, liquidity constraints, quality of client value proposition and behavioral constraints are the most important determinants of demand for first sales. For renewals the key constraints were limited understanding of insurance, value proposition and behavioral constraints. The impacts of these sources of friction vary according to the nature of the risk involved. Some key lessons are as follows:

- Consumer education seems to stimulate demand for index insurance but not health microinsurance.

- Liquidity is often a temporal issue – premium timing should reflect the buyer’s cash flow.

- Peer influence is important – sometimes more so than personal loss experiences.

- The cost of insurance is more than the premium – ease and comfort of access are important both at the premium paying and claims stages.

- The poor quality of health centers is often a key impediment to demand for health insurance.

- Bundling insurance with other products/ services can increase uptake – the perception being that it is a free service. However this effect appears to be context sensitive.

- Personal characteristics such as gender, age and risk aversion do not affect demand significantly.

Eling et al (2013) similarly review 41 demand oriented papers published since 2000 using the demand framework developed in Outreville’s 2013 survey. Significant drivers are summarized as follows: Economic – price, wealth (including access to credit) Social and cultural – risk aversion, non-performance risk, trust and peer effects, religion and financial literacy.

Structural – informal risk sharing, quality of service, risk exposure Personal – age, gender Two factors that stand out for microinsurance versus mainstream markets are the impacts of trust and financial literacy. In addition price appears to be a particularly important consideration for the poor.

In her doctoral dissertation Morsink (2012) details the results of interviews with 850 low income households over 1.5 years in India, Kenya, the Philippines and Ethiopia. The assumptions of expected utility theory are compared with actual findings in the field and the following modifiers identified – price, subsidies and discounts, the nature of the insured risk and the household’s risk profile, credit constraints, informal risk management techniques, marketing characteristics, risk preferences, the understanding of insurance, social capital and networks and trust levels. One interesting finding is that take up of insurance by poor households is often low even when the price is below the expected loss – pointing to the importance of the other factors.

While Morsink found a correlation between risk level and insurance take up, she also found that aversion in isolation led to reduced insurance take up – insurance could not be evaluated properly and was seen as a direct cost (see also Karlan and Mordock, 2009). The fact that positive peer claims experiences had a larger positive impact on riskaverse households was perhaps not surprising in light of this finding. Social capital and networks were similarly seen to be positive for take up. In her final chapter Morsink finds evidence based on general patterns of insurance take up that formal insurance reduces the likelihood that households have to use more costly consumption smoothing methods (such as asset sales, mortgaging their children’s futures by removing them from schooling and sub16