Pension Plan Termination and Retirement

Employee termination and retirement probabilities affect the valuation of employee benefit plans and thus are of concern to actuaries. To provide timely experience for the profession, the Society of Actuaries’ Non-Mortality Decrement Task Force organized a data collection effort. Thirty-two contributors provided over 1.7 million life years of pension plan turnover data for years 1994–2000. This article summarizes the results of this data collection effort.Traditionally, the most important determinants of termination and retirement are age, a proxy for attachment to the workforce, and service, a measure of attachment to a firm. This article documents the importance of these traditional quantities using current data and provides tables so that actuaries may quantitatively assess their importance when valuing pension plans.For the middle working years, ages 25–55, we find female termination probabilities are higher than males, although the differences are smaller than has been true historically. The differences are insignificant for the younger working years or early service years. Moreover, for ages 55 and older, males have higher retirement probabilities than females.We also document the effect of several plan characteristics: eligibility for postretirement health benefits, benefit formula, hourly/salary and union status, as well as plan size. To assess the effects of plan characteristics while controlling for age, service, and gender, we use multinomial logit analysis, a regression methodology suitable for categorical outcomes. We find that small plans have slightly higher termination probabilities compared to medium and large plans (plan size is our proxy for employer size). Union hourly plans have lower termination probabilities than salaried plans; in turn, salaried plans have lower termination probabilities than nonunion hourly plans. Firms that offer richer benefits enjoy lower turnover.The data for this study were gathered using a traditional industry experience studies approach. To highlight the strengths and weaknesses of this approach, we compare this data set to several government sponsored probability samples on job turnover. In general, these samples are smaller and thus provide less credible estimates yet they allow the analyst to explore the complex interactions of the effects of several variables on turnover.