1.1 Introduction and Statement of the Problem
The life insurance market is large, dynamic, and somewhat fragmented. Consider the following facts from the Insurance Information Institute (2015) and LIMRA (2014):
- Life insurance has been sold in the United States for over 200 years;
- Consumers paid nearly $164 billion in life insurance premiums in 2013;
- The five largest insurers (i.e., MetLife, Prudential, New York Life, TIAA-CREF, and Northwestern Mutual) each have revenues that exceed $24,000 million;
- Only 49% of consumers age 25–64 own individual life insurance;
- Fifty percent of the adult US population say they need life insurance;
- Nearly 90% of consumers believe life insurance is too expensive to purchase;
- Approximately 10% of the US population plans to purchase a life insurance policy within the next year; and
- Forty percent of consumers report that a life event (e.g., death of family member or close friend, getting married or divorced, etc.) prompted them to purchase life insurance.
These insurance details highlight the diverse nature of the life insurance marketplace. On the one hand, the insurance industry has a long history in the United States and is today a multi-billion dollar business. On the other hand, life insurance, as an important financial planning product, has limited market penetration. This helps explain why much of the existing life insurance research has been devoted to understanding the pricing mechanisms of life insurance and sales delivery and purchasing trends among consumers. The life insurance industry has been preoccupied with identifying pricing strategies that will attract new consumers to the insurance marketplace. A quick glance at the insurance industry’s leading Web sites and trade publications shows that industry groups spend a great deal of time, effort, and resources attempting to document life insurance ownership patterns and possibilities.
As will be discussed in this chapter, nearly all previous studies that have focused on the demand for life insurance have been conceptualized from a supply-side perspective. This study attempts to reframe the discussion of life insurance demand by conceptualizing the demand for life insurance as being shaped by many interrelated factors. Further, this study is premised on the notion that these factors can best be modeled using advanced statistical techniques that rely on artificial neural network techniques. This chapter provides a broad overview of the models most often used to predict and explain demand for life insurance among consumers, the factors most often hypothesized to be useful in predicting demand, and the conceptualization of this research. Later chapters provide a review of the relevant literature, a discussion of the research methodology, results from the statistical analyses, and an applied discussion.
It is important to start any discussion about life insurance demand by clarifying the role of life insurance within a consumer’s financial plan. Essentially, life insurance serves as a precaution for an unexpected event like the premature death of a family’s breadwinner, providing protection for the loss of a member of a household is the main reason people should, and most often do, purchase life insurance (Rejda, 2008; Thoyts, 2010). Losing a household’s breadwinner critically increases the financial vulnerability of a household, since the major source of income disappears with the death of the breadwinner. Even if the person who dies is not the household’s breadwinner, the loss of a related household member generates various economic burdens on the other members in the household. For instance, the premature death of a household member could leave unpaid medical bills and possible debt to be paid by others in the household (Rejda, 2008). These examples illustrate how life insurance works as a tool for managing the risk of premature death in a household.
When attempting to understand how life insurance works as a financial buffer against financial disaster, it is important to predict who will be more likely to purchase life insurance and the factors that lead people to purchase life insurance. Specifically, financial planners provide consulting advice on the purchase of appropriate life insurance policies based, in part, on their clients’ socioeconomic situation. Financial planners need valid, reliable tools to predict which people would like to purchase life insurance and the kinds of factors that are associated with purchasing behavior. In addition, policy makers have a need to understand the reason people purchase insurance and the manner in which people decide to purchase. This interest is premised on the notion that political issues, such as Social Security funding, are strongly related to the purchase of life insurance (Black & Skipper, 2000). Policy makers need to enforce market controls to help the market work efficiently and effectively. Furthermore, educators and researchers need information about consumers and purchase decision factors because predicting the demand for life insurance is one possible way to increase consumers’ financial well-being (Lynch, 2010).
There have been two major approaches used by researchers to understand the demand for life insurance: (a) actuarial science and (b) lifespan-related economic perspectives (e.g., human capital theory and the life cycle hypothesis). Actuarial science and lifespan-related economics are well developed and easily adapted for the use in understanding and explaining the life insurance market. For instance, these two major approaches explain well the price of life insurance and the quantity of selling in the real market. However, these approaches have serious limitations. These approaches tend to focus on finding and explaining the macro-level equilibrium in the real market rather than identifying and predicting influential factors in the decision-making process at the household le...