Insurance Linked Strategies Life Special Report

Transcription

1 Credit Suisse Asset Management Insurance Linked Strategies Life Special Report September 2009

2 Guest Report At the Crossroads of Science and Finance 18

3 Investments in Longevity and Mortality Demographic changes in the 20 th century led national governments around the globe to create age entitlement programs such as Social Security and Medicare that were designed as financial and health safety nets for people surviving to older ages. Because these programs are financed by payroll taxes exacted on the working-age population and consumption taxes exacted on the general population, they are social and economic contracts between generations. When these pension and health care programs were created at the dawn of the Post World War II baby boom, life expectancy (at birth) was in the 60s and 70s and not expected to change significantly. In 1935, the US Social Security Administration projected that by the year 2000 and beyond, no more than 20 million people would ever draw benefits from Social Security in a given year. The reality was far different. Today, more than 38 million people in the US are on Social Security, and hundreds of millions of people around the globe are involved in some form of government or private pension program. Authors S. Jay Olshansky, Ph.D. University of Illinois at Chicago Bruce A. Carnes, Ph.D. University of Oklahoma Michael S. Mandell, Esq. Highbrook Capital Fund Robin Willi BlueCrest Capital Management Government pension programs began in the 18 th century, but it was not until the 20 th century that a significant number of large companies created pension schemes as a benefit for their employees. Their goal was to create a long-term financial benefit that would induce employees to remain with their companies in exchange for financial security after retirement. These pension schemes were then, and continue to be an extraordinarily appealing benefit. However, a serious problem has emerged that threatens the very existence of these benefit arrangements. Advances in public health, science and medicine have enabled people to live far longer and in greater numbers than was expected when these age-based entitlement programs were created. The forecasters of survival and longevity at that time failed to detect the trend toward increasing life expectancy or the forces responsible for 19

4 that trend. Add to the unexpected rise in longevity the fact that global population aging is leading a wave of humanity toward the shores of retirement that will begin in 2011, and age-based pension and health care schemes today are already exposed to rapidly escalating financial risk. An estimated USD 20 trillion of longevity exposure exists worldwide, and this number is likely to increase in the coming decades. A perfect economic storm is not just on the distant horizon, it has already arrived. Ever increasing financial obligations resulting from a rapidly worsening beneficiary/worker ratio has lead to frantic and, to date, unsuccessful attempts by national governments to bolster their financial defenses against this escalating storm. In response to these unique demographic and economic conditions, a handful of financial institutions have recently created investment vehicles based on actuarial assumptions and forecasting models that enable, among other investment opportunities, those at risk to hedge their longevity exposure. The investment alternatives include longevity swaps, options, and structured notes based on newly developed indices. Readers can learn more about these investments from the organizations that created them. Our goal here is to help readers understand the fundamentals of mortality and explore a handful of variables that will influence the future direction of human longevity. What is a death rate? A death rate is a statistic that quantifies the rate of death occurring in a population over a specified time interval typically a calendar year. A death rate has two components, a numerator representing the number of deaths observed during a calendar year and a denominator representing the population at risk of dying. Death counts come from vital statistics reported for a specific population by the government. The population at risk used in an annual rate typically comes from a government census (or forecast) of the number of people alive at each age on July 1 in the relevant calendar year. For example, in England and Wales the number of women alive at age 75 on July 1, 1993 was 178,615 and 6,287 of those women died in that year. Thus, the death rate (also known as the central death rate or mortality rate) for that year and age equals 6,287/178,615 = In order to create a life table which is used to estimate life expectancy, death rates are algebraically converted to a statistic known as the conditional probability of death denoted by the symbol q which is the foundation upon which all life tables are built and it has an intuitive meaning that is easy to understand. In the above example, the death rate [.03519] is converted to q(75) = As its name implies, this is the probability of death conditional upon having survived to age 75. More simply, it means approximately 3.5% of females aged 75 in England and Wales in 1993 will die before reaching their 76 th birthday. Conversely, the conditional probability of survival p = 1 q reveals that 96.5% of females aged 75 in England and Wales in 1993 survived to their next birthday. From 1993 to 2003, q(75) for all females in England and Wales declined from to a decline of 13.1% in the decade or an annual rate of mortality improvement of about 1.3%. Death rates may change, but their form remains constant One of the more interesting and consistent characteristics of human mortality is the shape of the death rate across all age groups (Chart 1). The shape derives from high infant mortality that rapidly declines to its lowest point at puberty, at which point it transitions to an exponential increase thereafter. Note that this mortality data is plotted on a semi-logarithmic scale. Therefore, a rising straight-line means that the risk of death is increasing exponentially with age. The important message to take away from this figure is that despite documented declines in death rates over the course of human history, the general age pattern of mortality has remained the same, especially at older ages. 20

5 This remained true even during the 20 th century when death rates were declining rapidly and life expectancy experienced its most dramatic increase in history. This demographic regularity has its origins in an underlying biological regularity; a biological influence that produces a characteristic mortality shape that emerges even when human populations are separated by chronological time or geographic location. Chart 1: Death Rates in Different Countries and Years in the 20 th Century Deaths/1000/Yr (All Causes) Chart 2, the conditional probability of death for Swiss females who reached the age of 65 was highly volatile until the last quarter of the 20 th century declining from about 5% in 1875 to about 0.5% today. Subsequent to 1985 there have been small but steady declines in old age mortality such that today there is very little movement in the death rate from one year to the next. An even better way to appreciate the transformation from volatility to stability is to examine the annual absolute change in q(65). According to Chart 3, the amplitude of the annual fluctuation in the death rate was extremely high until the middle of the 20 th century, in large measure due to the periodic impact of infectious diseases, but it has only been in the last 15 to 20 years that the amplitude of annual change has been reduced to very low (and we might add) highly predictable levels. The fact is that this pattern of change from volatile annual swings in death rates to small stable patterns is common among almost all older ages in most developed countries. Chart 2: Conditional Probability of Death at Age 65 q(65) for Females in Switzerland ( ) India, 1900 Mexico, 1940 Sweden,1949 United States,1900 United States,1940 United States,1950 % From volatility to stability Although the general age pattern of death in people has remained constant throughout recorded history, what has changed significantly is the risk of death we experience and its resulting expectations for length of life. Consider the example of Switzerland from the late 19 th century until now. As shown in

6 Chart 3: Annual Change in q(65) for Females in Switzerland ( ) % Outlook: The re-emergence of uncertainty Although patterns of mortality at all ages have been characterized by a transition from volatility to stability and steady declines in death rates have been observed in recent decades, new uncertainties have emerged in recent years that should capture the attention of anyone interested in or concerned about longevity/ mortality risk. What is particularly relevant about this new uncertainty affecting survival and mortality is that it is virtually invisible to the actuaries and demographers responsible for forecasting trends in these phenomena. The reason for this lack of visibility is their reliance on antiquated mathematical models that function entirely by extending past trends into the future. Consider the modern rise in obesity in developed countries. In Charts 4 and 5, we can observe an epidemic of adult obesity that has already swept across the United States within a brief 20-year period. Charts 6 and 7 illustrate the same phenomenon for males in the European Union the same phenomenon has been observed for females. As alarming as these trends are, the modern rise in childhood obesity is even more disturbing because it occurred so rapidly, and because it has already led to a new set of diseases never before seen among young people early onset diabetes and fatty liver disease. Collectively, the modern rise in obesity among adults and children is poised to have a dramatic short-term negative affect on health and disability (which has already been observed), and unless new interventions are devised and implemented, it is likely to have a measurable negative impact on near-term death rates and life expectancy (over the next 10 to 20 years). In a recently published paper in the New England Journal of Medicine, we suggested that the obesity epidemic could, for the first time in the modern era, cause today s generation of children to live shorter and less healthy lives than their parents generation. Although global trends in obesity are poised to raise death rates, shorten life expectancy, and contribute to rising levels of disability, there are also promising developments emerging in medicine and the biomedical sciences that are extraordinarily exciting. There is no doubt that advances in biomedical technology will continue to accelerate throughout this century and fundamentally change what medical interventions can accomplish. One of the more promising areas of scientific research is the current effort to understand and intervene in the aging process itself. In articles published in The Scientist in 2006 and the British Medical Journal in 2008, we outline the rationale for why an investment in developing the scientific means to slow aging in people would pay enormous health and economic dividends to current and future generations. We believe these breakthroughs are forthcoming, and they are likely to have a notable impact on near-term survival prospects. More significantly, research in this area and its potential impact on mortality/survival is also invisible to the forecasting methodologies currently in use by actuaries and demographers. 22

7 Guest Report Chart 4: Prevalence of Adult Obesity in the US, by State (1985) <10% No data 10-14% 15-19% 20-24% Chart 6: Prevalence of Adult Obesity for Males in Europe ( ) ű Source: CDC Chart 5: Prevalence of Adult Obesity in the US, by State (2007) <10% 30+% 10-14% No data 15-19% 20-24% <5% ű 5-10% No data 10-14% 15-19% 20-24% Source: International Obesity Taskforce, 2005 Chart 7: Prevalence of Adult Obesity for Males in Europe ( ) 25-29% Source: CDC <5% ű 5-10% No data 10-14% 15-19% Self-reported data 20-24% Source: International Obesity Taskforce,

8 Capitalizing from stability Private pension funds reacted to unexpected rising life expectancy and population aging by creating an asset-based response that aimed to produce above average investment results for long-term liabilities. Many of these entities struggled with the financial maintenance of these social safety nets primarily because those responsible for estimating future financial obligations used a rear view mirror (i.e., past experience) as the basis for their forecasts. As a result, the experience of many pension funds has been somewhat disappointing. Some countries responded by switching from a defined benefit-based system to a defined contribution system while others began changing interest rates used to calculate the pension benefits for retirees. One consequence is a reduction in benefits. More recently, the contribution base for old-age retirement programs has been widened from employer and employee payroll contributions to the inclusion of general consumption taxes thus expanding and fundamentally changing the social contract between generations. The pension liabilities have grown so rapidly that recent discussions have begun to shift to politically sensitive issues such as a means test to restrict benefits, or the introduction of additional taxes for younger couples with fewer children. In industries with an aging workforce, extended survival is responsible for literally driving companies out of business as a result of years of accumulated pension problems. These problems have rarely been recognized in sufficient time to avoid financial problems, and they often surface in different forms such as healthcare costs or sub-standard research and development. General Motors is a classic example of a company in financial trouble because of pension obligations that have already spiraled out of control. These financial issues ultimately impact the individual taxpayer through higher product costs, and pension beneficiaries through lower benefits. When the healthcare company of the employees or the pension fund of the employees become the majority owners of a company, it is a sign that the company is about to collapse because the liabilities arising from the health care and pension funds compound faster than returns to shareholders. Nevertheless, the employee pension plan is as severely affected as other stakeholders. Similarly, when a state pension system slips into deficit, it ultimately becomes the responsibility of taxpayers to provide bail out funding via higher taxes or contributions. If providing adequate pensions for beneficiaries based on realistic estimates of survival is difficult, then the liability side must become tradable and manageable for pension plans. Indeed, as the baby boom generation retires, and the perfect storm is recognized in the offices of pension fund managers, pension plans will need to consider investing, somewhat counter-intuitively, into longevity and mortality transfer products. A pension fund with employees in the high-tech industry (and a generally younger profile), should consider investing in the mortality profile of a company that is active in the steel industry (with a generally older age profile), as long as the returns are attractive and the analytical tools are documented and well understood. Pension fund managers must eventually transform themselves into balance sheet managers they can no longer view liabilities as an area of study for their actuaries. In the final analysis, the future success of pension funds will be determined by the management of the liability side of the balance sheet. 24

9 Conclusion The transformation of death from a volatile event to one that is more highly predictable, and the fact that age patterns of death are a biological constant that can be relied upon as a condition unlikely to change in the near term, has set the stage for some degree of confidence in predicting the future course of mortality especially over the shortterm. Whether prospective investors can navigate their way through mortality statistics and investment opportunities without the background in understanding either the history or the biology that drives temporal change in mortality and longevity has yet to be determined. What we do know from experience is that actuaries and mathematical demographers involved in mortality forecasting have consistently struggled with their vision of the future of longevity because they have relied heavily on outdated trends in death statistics, completely ignored the underlying biological forces that influence and drive death rates, or maintained a biased view that assumes observed improvements in mortality will continue while observed declines in health and longevity will be resolved by medical technology and behavior modification. Finally, it is worth noting that when death is caused by aging-related conditions (which is the case with almost everyone who dies in the indexes now being offered), mortality rates observed today are a product of highly predictable health conditions that existed when people were alive. Scientists with expertise in understanding the linkages between biology and mortality, and the relationship between health conditions among living populations and their subsequent risk of death, may very well be the missing link that is needed to establish confidence in investments in mortality/longevity indexes that are currently outside of the expertise of those now thinking about hedging their risk or investing in this space. 25