Living Longer at What Price

8th July 2008
Living Longer – At What Price?
Introduction to Longevity

Contents
Contents
• Introduction 3
- Living longer 4
- Why 65? 6
- Aging Population 7
- Demographers’ views on future longevity 8
• The Basics 9
- Mortality rate, survival probability and mortality base table 11
- Different types of mortality risk 13
• Determinants of Future Mortality 20
- Age effect 20
- Period effect 21
- Cohort effect 23
Smoking Ban in England 26
2

Introduction
3

What is Longevity Risk?
Jeanne Louise Calment
on her 119th birthday.
• Jeanne Louise Calment is the oldest person on record, living for
122 years and 164 days. (Feb. 21, 1875 – Aug. 4, 1997)
• At the age of 90, Jeanne signed a deal to sell her apartment to a
man named Raffray, then aged 47, who agreed to pay a monthly
sum until she died (reverse mortgage).
• Big mistake by Raffray. Jeane Calment refused to die and he had
to make mortgage payments for 30 years. He never moved into
the apartment.
• Raffray himself died in Dec 1995, leaving his widow to continue
the payments for twenty more months.
• That is Longevity Risk.
Source: The New York Times
Facts
4

Japan, Italy, USA
People live longer in these three places than anywhere else in the world.
5 in 1,700 people
are centenarians
Source: BBC
900 in 1 million
people are
centenarians
Diet Genes Faith
Individuals live between 5 and
10 years longer than fellow
citizens
Facts
5

• Germany was the first nation in the world to adopt an old-age social insurance program in 1889,
designed by Germany’s Chancellor, Otto von Bismarck.
Why 65? The Origins of the Retirement Age in Social Security
UK Retirement Age
• Normal retirement age set at 65 in 1925 by the Contributory
Pensions Act. This Act established a pension of 50p a week for
manual workers and any others earning up to £250 a year.
• In 1925, the life expectancy at birth was 59 years.
“Call it Socialism or whatever
you like. It is the same to me.”
----- Bismarck during the
1881 debates
• The act was initially designed to provide a pension annuity for
workers who reached the age of 70 years. Note that in 1889, the
average life expectancy at birth of a German citizen was 45 years.
• In 1916 (27 years later) the retirement age was lowered to 65 years.
Source: U.S. Social Security Administration
Facts
6

Aging Population
Source: U.S. Census Bureau
Facts
• The Old-Age dependency ratio measures the ratio of the population over age 65 per person of
working age (15 to 64 years) as percentage.
• The Old-Age dependency ratio is an indicator used to measure the portion of the population that
is economically dependent on the active age group.
• France, which has one of the highest “number of children per mother” (1.9) in Europe, has a
“third child policy”, for which the benefit paid for the third child and subsequent children is
greater, and no allowance is paid for the first child.
• In Italy, there is a “Baby Bonus” of €1,000 for the second child.
Country No. of people aged 65+ % of total population
Japan 26,759,722 21.00%
Italy 11,564,416 19.89%
France 10,315,266 16.19%
UK 9,622,004 15.83%
USA 37,849,672 12.57%
China 104,040,756 7.87%
Old-Age
Dependency Ratio 2000 2050
WORLDWIDE 11% 25%
ASIA 9% 26%
UK 24.3% 45.3%
EU - 25 23.4% 52.8%
Source: Journal of Population Research and European
Union Public health Information System
7

Demographers’ Views on Longevity
Demographers Views on Longevity
Carbon Dioxide Loladze (2002) and Obesity Mizuno et al. (2004)
Future life expectancy might level off or even decline due to lifestyle and
environmental factors, such as obesity and the decreased food-derived health benefits
associated with higher levels of atmospheric CO2.
Increasing
Mortality
Immortality
Entropy in Life Table S. Jay Olshansky et al. : (2001)
Given the presence of entropy in the life table, a life expectancy at birth of 100 years, if
it ever occurs, is unlikely to arise until well past the time when everyone alive today
has already died.
Slow Down in Obesity Olshansky et al. (2005)
Increase in obesity will likely slow, or even reverse, increases in life expectancy in the US
Genetic and Non-genetic Changes Vaupel et al. (1998)
Non-genetic changes and discovery of genes and other survival attributes affecting longevity,
will lead to even longer lives.
Biomedical Development Aubrey de Grey
“The first 1000-year-old is probably only ~ 10 years younger than the first 150-year-old”
8

The Basics
9

The risk that a specific population will live longer than anticipated (Blake et al, 2006).
The Basics
Facts
• Life expectancy is increasing 5 hours a day.
• The most frequently used life expectancy by
FTSE100 DB pension schemes for a 65 yr old man is
20 years.
• The Pension Protection Fund (PPF)’s latest S143 and
S179 valuation assumptions set life expectancy for a
65yr old man as 22 years.
• “If the life expectancy for a male currently aged 60 is
understated by two years, depending on the
assumptions adopted, this could understate the
value of his pension by around 5%.”
The Purple Book (2007).
Definition of Longevity Risk
0
5
10
15
20
25
1920 1940 1960 1980 2000
LifeExpectancy
Increase in Life Expectancy
65 year old in England & Wales
Male Female
10

From the most commonly used PMA 92 table produced by the CMI (Continuous Mortality Investigation),
= 0.001315.
This means a man aged exactly 50 on 1st Jan 1992 had about 0.13% probability of dying during that year.
is the probability that this man will reach his 51st birthday.
In this case = 1 – 0.001315 = 0.99869
50 501p q 
Mortality Base Table – Mortality Rate and Survival Probability
Mortality rate : the probability an individual age “x” will die the following year.
Survival probability : the probability an individual age “x” will live at least another year.
Cumulative survival probability : the probability an individual age “x” will live at least another t years.
Life expectancy: The average number of years a person of a given age would live under a given set of
mortality conditions.
xq
xp
Example:
50q
t xp
11
The Basics

Mortality base table is the table setting out where x ranges 0 to 120.xq
Mortality Base Table
• Mortality just after birth
is very high.
• Mortality falls during the
first few years of life.
• “Accident hump” for
males at ages around 18-
25.
• No discernable “hump”
for females.
• From middle age onwards
there is a steep increase in
mortality rates.
0.0000
0.0010
0.0020
0.0030
0.0040
0.0050
0.0060
0.0070
0.0080
0.0090
0 10 20 30 40 50
MortalityRate
Mortality Table
England Males produced by the GAD
Male Female
12
The Basics

Mortality and Longevity Risk
Mortality
Risk
Pooling Risk
Longevity
Risk
Systematic
Longevity Risk
Longevity
Basis RiskCatastrophic
Mortality Risk
Mortality risk: The randomness of mortality rates causes fluctuations in liability
values of pension schemes. This risk is defined as mortality risk.
13
The Basics

• Pooling Risk: represents the random fluctuations of realised mortality rates around the
expected trend line.
• Catastrophic Mortality Risk: defined as sudden and unexpected spikes in mortality as a result
of human made events such as wars, or natural disasters like earthquakes, tsunamis or
pandemics.
Mortality Risks
14
The Basics

• Systematic Longevity Risk: The life expectancy of the population increasing faster than expected.
Mortality Risks
15
The Basics

Longevity Basis Risk – Example 1: Region
• Significant difference in life expectancy by
geographic areas.
•This may be partially due to natural
environment, climate. Also, it reflects the
different mix of industries and occupations.
•Life expectancy is reducing, in general, from
south to north. But certain areas show very
high mortality rate. e.g. Central London.
Source: Department of Health
16
The Basics
Longevity Basis Risk: People in a sub-population, such as a pension scheme, showing
different improvement trends than the referenced population.

• Life expectancy differs by
social class.
• Difference in life expectancy
can be as large as 4.2 years
• The trends of improvement
are similar.
• But the gap between top
and bottom classes are
widening through time.
• Should we anticipate a
catch up in life expectancy of
unskilled manual in future?
Longevity Basis Risk – Example 2: Social Classes
Source: ONS
17
The Basics

• People with insurance
policies exhibit different
mortality rates from the
general population.
• Annuity holders on
average tend to be
wealthier and in higher
social class.
• Mortality rates can be
significantly different.
Longevity Basis Risk – Example 3: Insurance vs. General Population
18
The Basics

• As the 3 examples above shows, the different mix and
composition of a pension scheme leads to differences in
mortality experience.
• Factors including occupation, socio-economic grouping,
smoking, and eating habits all influence the mortality
experience of the pension scheme.
• Even within the same scheme, the demographic structure can
change over time. So the mortality experience this year may
be different from that 10 years ago.
• Therefore, understanding scheme specific mortality
characteristics (and whether the scheme is large enough to
use “experience data”) is the key to managing longevity risk.
Longevity Basis Risk – No two schemes are the same
19
The Basics

Determinants of Future Mortality
20

Future Mortality Rate Determinants
Age Effect: In general, the older the person the higher the mortality rate. Exceptionally however:
• New-borns exhibit higher mortality rates than, say, 2 year olds.
• “Hump Effect”: young male adults see a localised peak in mortality (mainly due to accidents).
• After this point, mortality rates increase exponentially.
Period Effect: Captures mortality rate improvements for the same age group (e.g. 65 year olds) across
time.
• Advances in medical science, education and sanitary conditions are major reasons for people living
longer than previous generations.
• For example: a person aged 65 in twenty years time is likely to have a lower mortality rate than a
person aged 65 today.
Cohort Effect: Captures mortality rate improvements for people born in a particular year.
• For example: Mortality rates improved faster for groups of people born in the first half of 20th
century than those born in other periods.
21

Age effect
Age effect describes the relationship between age
and mortality rates. The probability of dying
increases as people becomes older. These mortality
trends exhibit an exponential behaviour.
Period effect
Period effect captures the impact that time-specific
events and changes have on the number of deaths.
The time period may span one year or multiple
years.
0.00
0.10
0.20
0.30
0.40
20 40 60 80 100
mortalityrate
Age
Age Effect
mortality rates for male aged 20 to 100 in E &W
in 2003
realised mortality rates for male aged 20 to 100 in year 2003
0
0.01
0.02
0.03
0.04
0.05
1920 1940 1960 1980 2000
mortalityrate
Period Effect
mortality rates for male aged 65 in 1920 to 2003
realised mortality rates for 65 years old male
22

Cohort Effect
• “Cohort effect” describes the phenomenon that different cohorts exhibit different rates of mortality
improvement.
• In the UK, it is observed that people born between 1925 and 1945 (and centred around 1931) have
experienced a higher improvement in mortality rates than people born before or after this
generation. And this group of people are referred as the Golden Cohort.
Source: GAD 23

Impact of this Golden Cohort
The significantly high mortality rate improvement for this cohort has had the following
impact on DB pension schemes,
- This cohort of people are living longer than expected.
- People born after this cohort are living even longer thanks to the raised improvement
basis.
- The impact of the cohort effect on pension payout is sustained from the first payment,
as for each and every year of pension in payment, there are more people surviving than
expected.
Life expectancy for 65 years old male:
= 19.78 years assume no cohort effect.
= 23.66 years under Long Cohort projection
24

Possible Causes of this Golden Cohort
• World War II (adverse conditions applying to the previous generation)
• Diet (more vegetable and bread, less cheese, sugar and soft drinks)
• The Welfare State (free secondary education and launch of the NHS)
• Smoking Behaviour (cigarette consumption fell steadily from 1960s)
• Birth Rates: the golden cohort is a period of low birth rate, just between two baby
booms. There may be a larger proportion of this cohort in higher socio-economic group
than previous generation.
Source: R.C. Willets (2004), The Cohort Effect: Insights and Explanations
• However, there is no concrete evidence that the factors discussed above are the direct
and true causes of significant mortality improvement of this golden cohort.
25

Smoking Ban 01 July 2007 in England
• At least 400,000 people have stopped smoking because of the ban.
• The smoke free legislation could prevent 40,000 people dying over
the next decade.
• These figures show the largest fall in the number of smokers on
record.
• This smoke-free law protects people from the harmful effects of
second hand smoke as well as helping people to quit smoking.
• Total sales of alcohol fell 8%, which also contribute to longer life.
• Does this leads to accelerating mortality reduction?
• Is another Golden Cohort forming?
Source: Cancer Research UK 26

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Dawid Konotey-Ahulu | Partner Direct: +44 (0) 207 250 3415
dawid@redingtonpartners.com
Robert Gardner | Partner Direct: +44 (0) 207 250 3416
robert.gardner@redingtonpartners.com
Redington Partners LLP
13 -15 Mallow Street London EC1Y 8RD
Telephone: +44 (0) 207 250 3331
www.redingtonpartners.com
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