This paper examines the use of age in the delivery of personal insurance to Canadians. We find that age is a reliable classification variable and one that can be practically implemented. Primary concerns about age as a classification variable centre around the issue of social acceptability. In particular, we focus on age and auto insurance where, unlike life and health insurance, there exists no strong intuitive causal relationship. In North America, the frequency and severity of auto accidents are highly correlated with age, in a nonlinear relationship. The data produce a distinctive U-shape curve when accident history is graphed against age. However, heterogeneity in driving abilities for both younger and older ages emphasizes that this relationship is one of correlation. To assess whether there exists a "better" classification variable, this paper explores possible alternatives to age. In the end, none of the variables examined captures a driver's risk with the same degree of accuracy as can be achieved using age. (Author/publisher)
Abstract