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CMI UpdateACA Conference 2015
Katherine Fossett
5 February 2016
CMI Structure
2
Executive Committee
Annuities AssurancesIncome
ProtectionMortality
ProjectionsSAPS
Management Committee
High Age Mortality
5 February 2016
Self Administered Pension Schemes (SAPS)
35 February 2016
SAPS timeline
4
Date Activity
2002 to 2006 Research and consultation
October 2008 “S1” Series tables released (based on data covering 2000-2006)
April 2010 & May 2011 Annual experience updates covering 2001-2008 & 2002-2009
July 2011 Mortality improvements of self-administered pension schemes
May 2012 Analysis of mortality experience by industry classification
May 2012 & April 2013 Annual experience updates covering 2003-2010 & 2004-2011
April - May 2013 Consultation on proposed “S2” Series tables
February 2014 “S2” Series tables released (based on data covering 2004-2011)
July 2014 Annual experience update covering 2005-2012
December 2014 Annual experience update covering 2006-2013
November 2015 Analysis of mortality experience by industry classification
5 February 2016
Industry Analysis – Exposure
55 February 2016
2.8%3.6%
19.2%
8.6%
1.1%
14.7%
5.1%
7.7%
1.6%
9.7%
4.9%
21.1%
Exposure by Industry - Males
1.1%1.4%
8.6%
11.1%
1.1%
15.7%
2.5%
12.8%0.7%
19.1%
2.7%
23.2%
Exposure by Industry - FemalesOil and Gas
Basic Materials
Industrials
Consumer Goods
Health Care
Consumer Services
Utilities
Financials
Technology
Local Authority
Miscellaneous
Not Included inIndustry Analysis
Industry Analysis – Experience – Males
6
80
85
90
95
100
105
110
115
120
80 85 90 95 100 105 110 115 120
Am
ou
nts
10
0A
/E
Lives 100A/E
Oil and Gas
Basic Materials
Industrials
Consumer Goods
Health Care
Consumer Services
Utilities
Financials
Technology
Local Authority
Miscellaneous
All Industries
5 February 2016
Industry Analysis – Experience – Females
7
80
85
90
95
100
105
110
115
120
80 85 90 95 100 105 110 115 120
Am
ou
nts
10
0A
/E
Lives 100A/E
Oil and Gas
Industrials
Consumer Goods
Health Care
Consumer Services
Basic Materials
Utilities
Financials
Technology
Local Authority
Miscellaneous
All Industries
5 February 2016
What is next for SAPS?
• Annual experience update for 2007-2014
– Due for release soon
• Analysis of public sector data
• Mortality Improvements of self-administered pension schemes?
• “S3” Series tables?
– Considering co-graduation
– Awaiting recommendations for high ages
– No immediate plans to begin work on “S3” Series
85 February 2016
High Age Extrapolation of “S2” Series
9
−60%
−40%
−20%
Nil
+20%
+40%
+60%
+80%
60 65 70 75 80 85 90 95 100 105 110
Dif
fere
nc
e in
lo
g μ
x
Age
Difference in log μx SnPMA_X v SnPMA
S1PMA_H
S1PMA_L
S2PMA_H
S2PMA_L
Heavy
Light
• Edge-effects
• No co-graduationHigh age
mortality
5 February 2016
Data Collection – Size of Current Dataset
• Total: 8.7m Male Pensioners and 4.7m Female Pensioners
• Dataset is large but could still be larger
• Continued submissions essential to ensure analyses remain current
105 February 2016
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
2,000,000
2007 2008 2009 2010 2011 2012 2013 2014
Ex
po
se
d t
o R
isk
(live
s-w
eig
hte
d)
Year
Exposed to Risk by year - 2007-2014 dataset
Male
Female
Lots of data in
earlier years.Later years
incomplete
Data Collection – Submitting Data
• Data submission specifications in SAPS Coding Guide
– Continuation data to be submitted where possible
– Ideally submit data starting from earliest date possible (i.e. include
previous valuation cycles, even if already submitted)
• CMI has reviewed its data handling procedures
• Postcode data has proved insufficient for analysis
– Committee will stop collecting postcode data
115 February 2016
High Age Mortality Working Party
125 February 2016
Background
High Age Mortality Working Party (HAMWP)
• Established Summer 2014
• Members predominantly drawn from CMI investigation committees
HAMWP terms of reference
• Investigate and summarise published research on high age mortality (90+)
• Investigate absolute mortality rates in respect of closing published tables
• Analyse data issues with available data sets (population / portfolio data)
CMI Working Paper 85
• Details analysis to date and possible future work
135 February 2016
Fitting mortality rates at high ages
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
80 90 100 110 120
Mo
rta
lity
Ra
te
Age
ELT16M High gate ELT16M Crude qx
ELT16M Low Gate ELT16M Fitted qx
14
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
80 90 100 110 120M
ort
ality
Ra
teAge
S2PML High gate S2PML Crude qx
S2PML Low Gate S2PML Fitted qx
S2PML Extended qx ELT16M
Observed
deceleration
5 February 2016
Smooth
progression
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
80 90 100 110 120
Mo
rta
lity
Ra
te
Age
S2PML Fitted qx Logistic Regression
Kannisto Regression Gompertz Regression
Academic Contributions
• A range of functional forms
have been proposed for shape
of mortality curve at older ages
• Graph shows impact on
S2PML curve of regressing
common functional forms
• Gompertz: μx = aebx
• Kannisto: μx = aebx/(1+ aebx)
• Logistic: μx = c + aebx/(1+ αebx)
15
Max e100 = 101.3%*
Min e100 = 80.3%*
*relative to S2PML extension (baseline e100 = 2.12)
5 February 2016
Summary of published tables
Table Run in Age Extension Approach Limit mortality rate
ELT16 n/a
Variable-knot spline regression
fitted to 108, used for high age
extensions
m120 = 2 / q120 ~ 1
“S2” Series 95 Cubic spline with constraints μ120 =1 / q120 ~ 0.64
“08” Series 90 Non-linear interpolation μ120 =1 / q120 ~ 0.64
Canadian CPM2014 94
Quartic polynomial to bridge
graduated rate to population
rates at age 103
q114=0.66
US RP-2014Between
75 and 100
Kannisto regression for high ages
75-104 / interpolation between
main regression and high age
Cap on qx of 0.5
165 February 2016
Comparison of published graduations
• S2PML extended
using methodologies
underlying various
industry tables
• Run in age used for
each table applied to
S2PML data
• Very little variation in
e100 for the extensions
shown due to
closeness of qx
values near 100
17
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
70 80 90 100 110 120
Mo
rta
lity
Ra
te
Age
S2PML Fitted qx S2 Extension
08 Extension RP-2014 Extension
ELT15 Extension
“08” extension min e100 = 2.08
RP-2014 extension
max e100 = 2.13
S2PML e100 = 2.12
5 February 2016
Theories on mortality patterns at high ages
• Debate around mortality deceleration vs Gompertz progression at high ages
• A number of authors support deceleration with heterogeneous populations
(frail die young) seen as main cause
• Gavrilov and Gavrilova (2011, 2014) propose no underlying deceleration,
primarily due to:
– Age misreporting
– Aggregation of single year birth cohorts (heterogeneity)
– Studying age-specific probabilities of death rather than force of mortality
– Data recording in older studies was less accurate
185 February 2016
What data issues should be considered?
19
Issue Population Data Portfolio Data
Exposure
estimation
Mid year population
estimates used as proxy for
exposed to risk
Exposed to risk calculated from
data
Death reportingDeath registrations required
within 5 days of death
Late reported / unreported
deaths common
Phantoms
“Phantom” cohorts possible
due to rolling forward of
census data
“Phantom” exposures possible if
deaths not removed from data
set
MigrationAssumption required for
impact on exposures
Tracing overseas deaths difficult,
likely delays in reporting
5 February 2016
Closed cohort mortality
• Considered deaths for cohorts which are essentially extinct (reached their
110th birthday)
• Estimate historical populations (and mortality) from recorded deaths
– Population for age x in calendar y year y = Px,y
– Deaths for age x in calendar y year y = Dx,y
– Pmax,y = D110,y
– Px,y = Px+1, y+1 + Dx,y
• Implied mortality compared against ONS and HMD published mortality
205 February 2016
Closed cohort mortality
21
60
70
80
90
19
61
19
63
19
65
19
67
19
69
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
Ag
e
Year
Extinct Generation mortality / ONS mortality -England & Wales, Males
60
70
80
90
19
61
19
63
19
65
19
67
19
69
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
Ag
e
Year
Extinct Generation mortality / HMD mortality -England & Wales, Males
+4%
+2%
0%
-2%
-4%
5 February 2016
Areas of further investigation – time trends
• No clear evidence for a material change in mortality patterns for lives born
between 1884 and 1898 in England & Wales data.
0.0%
0.1%
0.2%
0.3%
0.4%
0.5%
0.6%
188
4
188
5
188
6
188
7
188
8
188
9
189
0
189
1
189
2
189
3
189
4
189
5
189
6
189
7
189
8
Su
rviv
al P
rob
ab
ilit
y
Year of Birth
Probability of survival to age 105 from age 90
Survival to 105 (F) Survival to 105 (M)
225 February 2016
Summary of findings so far
• Data and modelling issues at very high ages:
– Inconclusive debate on mortality shape at high ages
– Users should consider age misstatement and late reporting issues
– Published closed cohorts mortality appears to be understated
• Impact of different mortality rates derived using different models generally
not material except at very old ages
• Given this and data issues mentioned above, the Working Party feels that
old age extrapolation choices in recent graduated CMI tables were
reasonable
• Second phase of work now in early stages
235 February 2016
Mortality Projections
245 February 2016
CMI Model timeline
Date Model Activity
2004 to 2008 Research and consultation
Nov 2009 CMI_2009 First version of the Model
Nov 2010 CMI_2010 Annual update
Sep 2011 CMI_2011 Annual update – CMI estimate of high age population
Feb 2013 CMI_2012 Annual update – Revised population estimates after 2011 Census
Apr 2013 Consultation on the Model
Sep 2013 CMI_2013 Annual update
Nov 2014 CMI_2014 Annual update – revisions to calibration method
Mar 2015 Consultation on the release date of future updates to the Model
Sep 2015 CMI_2015 Annual update plus paper on recent mortality
Spring 2016 Consultation on the future of the Model
Mar 2017 CMI_2016 First version of revised model
255 February 2016
Estimate ‘long term’
improvements◄◄ Stitch together ►►
Estimate current
improvements
Current CMI Model – very high level overview
Uncertain More uncertain Guess (educated)
26
In the short-term, the best
guide to the likely pace of
mortality improvement is the
most recently observed
experience.
The forces driving mortality
change are themselves
subject to change over time.
Inform assumption choice
less by analysis, more by
subjective judgement.
5 February 2016
Current CMI Model – overview
1. Data
(a) ONS provides raw death and
population data for England &
Wales
(b) Population data at high ages
constructed by CMI (using ONS
methodology)
(c) CMI caps unlikely data points
2. Fit P-spline surface
to estimate smooth
mortality rates
3. Derive annual
rates of mortality
improvements
4. Determine age-
period / cohort
decomposition
5. Projection
(a) Step back two years
for initial rates
(b) Sum separate
projections of age-period
and cohort components
275 February 2016
Recent mortality
285 February 2016
-15,000
-10,000
-5,000
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
0 4 8 12 16 20 24 28 32 36 40 44 48 52
Week number
Cumulative report deaths by week compared with average over 2005 to 2014
2015
2005
to
2014
Recent mortality
Standardised mortality ratio (2011), England & Wales, and 2000-2011 trend
Note: 2015 data has been estimated based on actual deaths to 31 July.
90%
100%
110%
120%
130%
2000 2005 2010 2015
295 February 2016
Recent mortality improvements
Annual mortality improvements (1976-2015)
Note: 2015 data has been estimated based on actual deaths to 31 July.
−3%
−2%
−1%
Nil
+1%
+2%
+3%
+4%
+5%
+6%
1975 1980 1985 1990 1995 2000 2005 2010 2015
305 February 2016
Recent mortality improvements
Four-year average annualised mortality improvements (1979-2015)
Note: 2015 data has been estimated based on actual deaths to 31 July.
Nil
+1%
+2%
+3%
1975 1980 1985 1990 1995 2000 2005 2010 2015
315 February 2016
Changes between CMI Model versions
Male life expectancy at age 65, male, for different Model versions
Assumptions: S2PMA at 1 January 2007, projected using CMI_20yy_M[1.5%]
21
22
23
2010 2011 2012 2013 2014 2015 2016 2017
CMI_2009 2010
2011 2012
2013 2014
2015
325 February 2016
Financial impact CMI_2015 vs CMI_2014
• Change in cohort expectation of life*:
• Impact of moving from CMI_2012 considerably greater
33
Age Male Female
55 −0.9% −0.9%
65 −1.2% −1.4%
75 −1.8% −1.9%
85 −1.9% −2.0%
* Age exact on 31 December 2015 and S2PxA + CMI_ 2015 vs CMI_2014 (LTR=1.5%).
5 February 2016
Recent mortality – deaths since CMI_2015
345 February 2016
-15,000
-10,000
-5,000
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
0 4 8 12 16 20 24 28 32 36 40 44 48 52
Week number
Cumulative report deaths by week compared with average over 2005 to 2014
2015
2005
to
2014
Reviewing the CMI Model – Avenues of
investigation
Data
• Correcting for artefacts
• High age mortality
Deterministicprojection
models
• Deal with concerns with the current model
• Consider alternative models
State space modelling
• Able to answer statistically the questions:(a) where are we now?(b) how certain of that are we?
• This would be a fundamental change in methodology
Other
• Guidance on parameterisation
• Coherent modelling of multiple populations
• Cause of death modelling
355 February 2016
What is coming next?
365 February 2016
Things to look out for in 2016
• SAPS annual experience update for 2007-2014
• CMI Model consultation
– Consultation paper during Spring 2016
– Public meetings in April/May 2016
• No CMI Model release during 2016
– CMI_2016 due out in March 2017
• HAMWP second phase working paper
375 February 2016
Questions
The views expressed in this presentation are those of the presenter.
Please send any questions, views or feedback to
5 February 2016 38
Comments
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5 February 2016