Determinants of the lapse rate in life insurance operating companies.

Introduction

The purpose of this study is to determine whether factors influencing the lapse rates on ordinary life insurance products can be identified and their importance statistically assessed. In this study, we refer to ordinary life insurance as including the two traditional components of term and whole life. The lapse rate is a key operating parameter that reflects both consumer behavior and insurer managerial decisions involving life policies. Projections of the lapse rate are also critical in structuring securitization arrangements.

Interest in lapse rate determinants has become more prominent in recent years as some financial service firms have applied securitization techniques to life insurance policies held by individuals. Under securitization arrangements, the insured person conveys the payment rights of his/her policy to the firm structuring the securitization product. In return, the insured person receives a one-time cash payment. The securitizing firm pools these policies, using them as the basis for asset-backed securities. Insurance companies, too, may engage in the securitization of their life insurance liabilities by transferring life policies and the assets that back them. For example, such an action was undertaken by Prudential Financial in 2001 as a component to its demutualization. In both cases, assumptions concerning lapse rates are basic to the securitization process. In this paper, we do not attempt to assess the impact of securitization, since this trend is still in its early stages. (1) Rather, we direct our efforts to expand our understanding of lapse rate determinants.

Of special interest in the area of lapse rate analysis is the role of the financial stress position of the insurer, and whether and in what ways stress considerations may influence policyholder lapsation/persistence. To our knowledge, financial stress has not been previously examined in the framework of a formal cross-sectional statistical study to identify the influence on the lapse rates for ordinary life policies.

The model presented in this paper is shown to work best in the case of life companies operating under relatively high financial stress.

We interpret this result as suggesting that a life company under financial stress operates with a reduced level of managerial discretion since company management, in these circumstances, is typically concerned about the company's credit rating position. In contrast, the ordinary life lapse rates for companies characterized by low and mid-range financial stress do not closely fit the model. These findings provide analytical direction to studies of ordinary life products by showing the important role of the financial stress dimension.

Lapsation Issues Shaping Model Development

The lapse rate on life policies has traditionally been one of the central parameters in the managerial framework for life insurers. Consumer demand is widely recognized to be sensitive to pricing for both term and whole life product lines. These ordinary life products are regarded as "commodities." That is, these products are relatively homogeneous in nature, offered by a large number of insurers, and are competitively priced. Lapsation and policy terminations may be initiated by consumers at any time by failing to pay premium billings on term and whole life policies. The non-renewal of term and whole life policies may also occur on renewal anniversaries for policies with guaranteed renewable riders.

From the company's perspective, life insurance products, and especially whole life products, typically entail large underwriting and upfront origination costs, heavily driven by sales costs and commissions. (2) This cost structure provides insurers a strong motive toward lower lapse rates. Too high a lapse rate may impair the ability of the insurer to recoup these costs, given the projected benefits payouts required under these lines of insurance. Companies that are in a stronger market position will be able to price more aggressively than companies in a weaker market position.

From the consumer's perspective, both term and whole life policy holders with health or other insurability problems tend to lapse less frequently, because their alternatives are limited and can be more expensive. The effect is to introduce a tendency toward adverse selection. Healthy individuals may lapse or fail to renew at the guaranteed-renewal dates if they can find less expensive term insurance when passing a medical exam. This adverse selection effect causes the insurer to experience a higher rate of claims on the remaining policies than would have been expected from the entire pool of original insureds. (3)

Managers in the life insurance industry incorporate assumptions about the expected level of lapsation in the design of life products. Unfortunately, information relating to the planned or anticipated lapse rates for companies is proprietary, and thus is not generally available to the public. By studying life companies for which data is available, conclusions can be drawn regarding the factors that affect life policy lapsation.

The Statistical Approach, Data Availabilities, and Variable Specification

This study uses the single equation-multiple regression statistical framework to examine the issue of lapse rate determinants. The cross-section analytical approach is selected. In the course of our study, we identified heteroskedasticity problems, and chose to deal with these using the White (1980) estimation technique.

The lapse rate and other data in this study are compiled based on the set of 162 companies rated for financial stress by the Moody's Investors Service credit rating organization. All available Moody's rated companies were used. This company set was matched to operating life insurance companies reported by the National Association of Insurance Commissioners (NAIC). The data used were taken from the Five-Year Historical Data summary tables in the NAIC "Blue Book." After accounting for data availabilities, the number of useable company observations was 139. These firms constitute 52% of the total life policies in force in the United States in 2003.

The base year for our work is 2003; this was the latest available year at the time of this study. By 2003, a slow economic expansion was widely acknowledged to be underway, following the recession that ended in November 2001. This base year is not cyclically exceptional in the sense of being neither a year of recession nor the peak of a boom.

The company-level characteristics considered in this study, and the specifications for these variables, are now considered.

Lapse Rate. As compiled by the NAIC organization, the lapse rate is reported for ordinary life insurance products. To form the lapse rate for a specific life insurer, the value of lapsed policies for ordinary life products is divided by the average total life insurance in force during the time period. This ratio is multiplied by a scaling factor of 100. Ordinary life policies include the following types of insurance plans: level term life; decreasing term; renewable term; traditional whole life; interest sensitive whole or universal life; and graded-premium whole life.

Financial Stress Ratings. Several outside agencies provide financial stress ratings, including: Moody's, A. M. Best, and Fitch. After reviewing the methodologies of these agencies and after discussions with practitioners and state-level insurance regulators, we chose the Moody's ratings because of the greater degree of delineation provided by their evaluation system. We wish to examine whether the Moody's financial stress measure is statistically supported as a determinant of the lapse rate.

Life operating companies face the decision of how to structure assets and liabilities to achieve specific target financial ratings. The lapse rate is one of the internal operating parameters that many analysts believe will be affected by a company's rating. For our purpose, the Moody's ratings range from Aaa (as 1) to B2 (as 15). A positive relationship is expected between financial stress ratings and the lapse rate.

These considerations are examined in this study for the Full Sample of life companies taken together, and also by segmenting the sample, as follows: the Low Risk panel consists of all life companies rated by Moody's as Aa1 and above; the Mid-range Risk panel, for Aa2 to A1; and the Higher Risk panel, with ratings below A1. In the segmented analysis, the effect of the financial stress rating will be transferred to the constant term within the linear regression framework. The constant intercept term is expected to be higher in the case of the Higher Risk panel, relative to the Mid-range and Low groups. The Moody's ratings variable is included in the segmented analysis to capture any possible "within panel" financial stress effects.

The Joint Pricing Variables. Ordinary life insurance pricing can be interpreted at two levels. The first interpretation is the traditional time-series oriented microeconomic perspective, where pricing serves as a mechanism for rationing consumer demand. Under the traditional micro interpretation, a higher price may contribute to increased lapsation, a positive relationship.