Abstract
In Kenya life insurance has contributed widely and still remains a vital aspect of the social-economic development of the society. It focuses on safe- guarding the future as well as ensuring that there is some savings that can be used later in life. Despite its importance, the penetration of life insurance is currently only at one point three percent in Kenya. This is a small percentage in comparison to the developed countries where life insurance penetration is quite high. In this research, local regression (LOESS) method was used. LOESS specifically denotes a method that is also known as locally weighted polynomial regression. At each point in the data set a low-degree polynomial is fitted to a subset of the data, with explanatory variable values near the point whose response is being estimated. In local polynomial regression, a low-order weighted least squares(WLS) regression is fit at each point of interest using data from some neighbourhood around x. The value of the regression function for the point is then obtained by evaluating the local polynomial using the explanatory variable values for that data point. In this research income, education, age and marital status were found as the major factors associated with low insurance intake in Uasin Gishu County. The research highly recommends the insurance companies to apply the knowledge of LOESS to determine the major factors associated with low life insurance uptake in the country. Insurance companies should strive to provide educative seminars to the public to increase life insurance uptake. In this research we had uptake of life insurance as dependent variable and level of income, education level, age and marital status being independent variables.
Highlights
Background information The business entities and individuals are mostly exposed to substantial risk that is associated with losses to property, income, and wealth due to the damage to assets, legal liability, disability, retirement, and death
Low income earners face risks and economic shocks that might be the same as conventional insurance clients, the low end market is more susceptible due to limitation of resources and knowledge Akotey (2011), are not able to mitigate risks compared to their higher income participants; and in case of economic loss from perils, they are less equipped to cope with the aftermaths
Designing a multiple local polynomial regression Consider a set of scatterplot data {(X1, Y1 ),..., (Xm, Ym )} from the model
Summary
Background information The business entities and individuals are mostly exposed to substantial risk that is associated with losses to property, income, and wealth due to the damage to assets, legal liability, disability, retirement, and death. Most of the Kenyan Insurance Companies deliver insurance products to participants at the bases of the pyramid; Micro Insurance. Low income earners face risks and economic shocks that might be the same as conventional insurance clients, the low end market is more susceptible due to limitation of resources and knowledge Akotey (2011), are not able to mitigate risks compared to their higher income participants; and in case of economic loss from perils, they are less equipped to cope with the aftermaths. Micro-insurance serves as their best bet in building financial confidence and wealth restoration in the event that risks materialize Butt (2010).The poor face two types of risks namely; idiosyncratic (specific to the household)and covariate
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