living longer at what price- introduction to mortality and mortality rate determinants

8th July 2008

Living Longer – At What Price? Introduction to Longevity

Contents

Introduction to Longevity

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

Life

Exp

ecta

ncy

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

Mo

rtal

ity

Rat

e

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 Risk Catastrophic

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

mo

rtal

ity

rate

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

mo

rtal

ity

rate

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



Contacts

Dawid Konotey-Ahulu | Partner Direct: +44 (0) 207 250 3415 [email protected] Robert Gardner | Partner Direct: +44 (0) 207 250 3416 [email protected] Redington Partners LLP 13 -15 Mallow Street London EC1Y 8RD Telephone: +44 (0) 207 250 3331

www.redingtonpartners.com THE DESTINATION FOR ASSET & LIABILITY MANAGEMENT

Contacts

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27

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living longer at what price- introduction to mortality and mortality rate determinants

Economy & Finance

age effect

future life expectancy

and10 years

for122 years

years younger

life table

theaverage life expectancy

oldage dependency ratio