Description: Identifies current trends and considerations when setting the mortality improvement assumption for individual life insurance in the US.

Description: The premise underlying the method illustrated is simply that, in a suit for damages based on earnings lost by reason of premature death or permanent disability, the value of future earnings should not be measured in terms of the whole of life but rather in terms of a more realistic work life expectancy.

Description: Paper provides an overview of the impacts of population aging on the design and marketing of group life and health and group pension products over the next 40 years. While the paper is written with a group focus, the implications of the material for individual product design and marketing are immediately obvious and equally important. From the Transactions of Society of Actuaries 1994, Vol. 46.

Description: This session at the SOA 1990 Annual Meeting explores the status and impact of AIDS in the U.S., Canada and elsewhere in the world.

Description: Mortality Improvement Scale MP-2016 is an update to Mortality Improvement Scale MP-2015 that includes three additional years of historical U.S. population mortality data and modification of two input values designed to improve the model’s year-over-year financial stability. The page includes the updated Scale MP-2016, the accompanying Scale MP-2016 document, and an updated RPEC_2014_v2016 Model Implementation Tool for actuaries to create their own mortality improvement scales using RPEC’s model and the updated data set.

Description: Trends in mortality have a huge impact on the insurance industry and its products, risks and profitability. Worldwide, life expectancy has increased continuously over the past decades. However, this progress—even without considering the Covid-19 pandemic—has been subject to strong fluctuations. In recent years, an increase in volatility can be observed. Various aspects—medical science, environment, lifestyle—have influenced the trend in the past. How can the life and health industry move forward in these uncertain conditions? In this article, we examine what has gone before and what can be learned from a closer analysis. What are the main drivers of the historical increase in life expectancy? What characterizes the dynamics of the recent past? How can mortality be projected into the future? How good are our methods of prediction and what are the risks? What risk management options are available?

Description: Women have different resources and experiences than men in retirement, partly due to demographic and life cycle differences and partly due to societal practices. This article reviews the differences, the situation 25 years ago, and then compares the situation 25 years ago to the situation today. Some of the big long lasting issues are differences in life spans and periods of disability, differences in pay, different shares of caregiving responsibility and differences of time out of the workplace. It finds some things that have improved, but other issues that existed 25 years ago have still not been addressed. New issues have arisen. The article draws on a presentation given at the 25th anniversary of the Women’s Institute for a Secure Retirement (WISER). It also focuses on the impact of COVID and the changes that have taken place in the workplace in the last few years. It provides insights into and discussions of the pros and cons of remote and hybrid work arrangements. It provides insights into issues that still need to be addressed.

Description: This paper reviews the recently published Canadian literature on changing Canadian demographic profiles and highlights the actuarial aspects of the information provided therein. Discussions of this paper are included.

Description: This page contains research and research tools related to U.S. population data.

Description: Presented at Living to 100 symposium, January 2011. Using data from Social Security's Death Master File, this study shows that mortality deceleration in humans observed at advanced ages may be an artifact caused by age exaggeration, data heterogeneity or use of improper estimates of hazard rate. The phenomenon of mortality deceleration at older ages vanishes for datasets of higher data quality.