Lectures 2, 3 Variance in Death and Mortality Decline Shripad Tuljapurkar Ryan D. Edwards
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Transcript of Lectures 2, 3 Variance in Death and Mortality Decline Shripad Tuljapurkar Ryan D. Edwards
Lectures 2, 3Variance in Death and Mortality Decline
Shripad Tuljapurkar
Ryan D. EdwardsQueens College & Grad Center CUNY
MORTALITY LEVELS, DECLINES ARE ASSESSED IN TERMS OF e0
e0 = LIFE EXPECTANCY AT BIRTH
= AVERAGE AGE AT DEATH
= E(T) where T = Random age at death
Density of T is (.)
1750 1800 1850 1900 1950 2000 205010
20
30
40
50
60
70
80
90
YEAR
SWEDEN LIFE EXPECTANCY AT BIRTH
MORTALITY CHANGE
THE DETAILS ARE MESSY
•Year to year decline irregular
•Persistent, puzzling differentials
•Cause of death structure difficult to understand & to predict
•Poor understanding of causal relationship to driving forces
•Startling reversibility -- the Former Soviet Union
BUT…
IN THE AGGREGATE (i.e., age/sex)
OVER THE LONG-TERM ( >40 years)
IN HIGHLY INDUSTRIALIZED NATIONS
THERE APPEARS TO BE A
Simple, general (?) pattern of decline
log m(x,t) = s a(x) k(t) + r b(x) g(t) + …
Singular Values s > r > … > 0
IF s >> r > …
THEN
DOMINANT TEMPORAL PATTERN IS
k(t)
% VARIANCE EXPLAINED IS
s2/(s2 + r2 + …)
Lee Carter (US)
Tulja, Li , Boe (G7)
In every G-7 country
ONE TEMPORAL COMPONENT
EXPLAINS OVER 92 % OF CHANGE IN log m(x,t)
m(x,t) = central death rate
G-7 = Canada, France, Germany, Italy, Japan, UK, US
Period = 1950 TO 1994
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995-20
-15
-10
-5
0
5
10
15
20
CanadaFranceGermanyItalyJapanUKUS
LEE CARTER MORTALITY
log ( , ) log ( , 1) ( ) ( ) ( )m x t m x t z b x e t b x
OEPPEN-VAUPEL
Best-in-world life expectancy has risen in a straight line for 160 years, as shown
by
0 20 40 60 80 100 1200
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Age
Pro
b D
yin
g q
x*lx
SWEDEN Prob Death by Age 1875
0 20 40 60 80 100 1200
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
Age
Pro
b D
yin
g q
x*lx
SWEDEN Prob Death by Age 2000
0
before befor
Age at death
Prob{die age A} {age at death|die age A}
Prob{die age A} {age at death|die age A}
( )
e
after after
T
p e E
p e E
E Te p e p e
Death – young death before age A, – adult death after age A
p
p
0
Age at death
dominated by
T
e e
Most death – adult death after age A
2 20 0
Var( )
( ) ( )
T p V p V
p e e p e e
Variance in age at death –
young death, adult death
From young deathFrom adult death
+
Age at death
=V Var( | die after age )
T
T A
Most variance in death – variance in adult death after age A
0 20 40 60 80 100 1200
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Infant Mortality – leave out Mode
S10 – Variance of Age at Death if Die after Age 10
“ADULT” DEATHS AGES > 10 YEARS
CAPTURES MOST VARIANCE IN AGE OF DEATH
V(10) = VAR (AGE AT DEATH | DIE AT AGE > 10)
S(10) = √ V(10) = STANDARD DEVIATION IN AGE AT ADULT
DEATH.
US
JapanSweden
Age a
( ) ( ) ( )a a l a Conditional distribution --- die after age 10
0
2
0 2
2 0''
0
Mode of ( ) is at
( )( ) ( ) exp
2
( )
| ( ) |
( )o
a a
a aa a
a
a
0
Gompertz Slope and Variance in Death
1
( )= aa e
Did β change through history? Is it still changing?
15 20 25 30 35 40 45 50 5518.5
19
19.5
20
20.5
21
21.5
22
Sweden Female S(20) VS e0. 1751-1891
50 55 60 65 70 7512
14
16
18
20
22
24
Sweden Female S(20) VS e0. 1891-1953
73 74 75 76 77 78 79 80 81 82 8311.8
12
12.2
12.4
12.6
12.8
13
Sweden Female S(20) VS e0. 1954-2003
σ DECREASED and
β INCREASED
through the first half of the 20th century everywhere*
σ is still DECREASING and
β INCREASING
in Sweden
Forecasting Models
Bongaarts
0 0( ) ( 1)
( ) ( 1)
a t a t D
t t
Forecasting Models
Lee-Carter
0
log ( , ) log ( , 1) ( )
( ) 0 ( ) 1
( ) peaks at age Mode of ( , )a
a t a t z b x
b a b a
b a a a t
00 0 2
Mode and variance ( , ) projected by Lee-Carter
2 ( 1)( ) ( 1)
( 1)
( ) ( 1) if ( / ) 0 at mode
a t
z b a ta t a t
t
t t db dx
Shape of b(x) at ages past mode could reverse this – case of Japan
Role of T and V(T) (adult death)
Annuities, Life insurance
Longevity bonds
Risk – life cycle savings and consumption
Risk – societal pension risk
Optimization without constant environments – economic models
Variance in age of adult death
= Var. betwen groups +
Var. within groups
Var. within groups
AV
0 10 20 30 40 50 600
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0 10 20 30 40 50 600
0.005
0.01
0.015
0.02
0.025
Can racial differentials explain high U.S. S10?
14
15
16
17
18
19
1970 1975 1980 1985 1990
African Am. S10 White S10 USA S10Canada S10 France S10
S10 in the U.S. by race; compare Canada, France
African Americans & Whites
-10
-8
-6
-4
-2
0
2
0 10 20 30 40 50 60 70 80 90 100 110
African Americans
Whites
Source: Berkeley Mortality Database (NCHS data for 1981)
Lo
g m
ort
alit
yA
ges
at
dea
th
0
0.01
0.02
0.03
0.04
0 10 20 30 40 50 60 70 80 90 100 110
African Americans = 70.7
S = 17.4
Whites = 75.8
S = 15.2
WHAT ELSE MAKES US SPECIAL?
“EXTERNAL CAUSES OF DEATH” (Homicide, suicide, violence, other)
SEPARATE OUT EXTERNAL DEATHS, FIND S10 FOR WHAT’S LEFT
FACT: Education & Income affect Mortality Risk
BUT: Variance within educational/income groups??
USUAL Q: how much Mortality when Educ
HH income and age at death using the NLMS
0.00
0.01
0.02
0.03
15 25 35 45 55 65 75 85 95
Lif
e t
ab
le d
eath
s (
den
sit
ies)
Lowest 20% of HH income = 71.92 = 16.8
Upper 80% of HH income
= 77.42 = 14.4
Source: first year of NLMS, roughly 1981
Education and age at death using the NLMS
0.00
0.01
0.02
0.03
15 25 35 45 55 65 75 85 95
Lif
e t
ab
le d
eath
s (
den
sit
ies)
Less than high school
= 72.92 = 16.7
High school graduate = 78.02 = 14.6
Source: first year of NLMS, roughly 1981
WHAT ABOUT AGGREGATE INEQUALITY?
DOES INCOME INEQUALITY IMPLY
INEQUALITY IN AGE AT DEATH?
Risk factors?
Epidemics of risk factors (obesity, smoking, alcohol)?
Comparative analysis
( )a
Age a
Age a
( )l a
Age a
( ) ( ) ( )a a l a