1.  Pension seminar 2004 CURRENT ISSUES IN MORTALITY Dublin – 1 June 2004 Tony Leandro

2. Key >4.2% 4.2% 3.6% 3.0% 2.4% 1.8% 1.2% 0.6% 0% -0.6% -1.2% <-1.2% 20 30 40 95 50 60 70 80 90 Age 194819601970198019901999 GAD Contour map Males, England & Wales

3. 20 30 40 95 50 60 70 80 90 Age Key Local Peak > 1.5% >4.2% 4.2% 3.6% 3.0% 2.4% 1.8% 1.2% 0.6% 0% -0.6% -1.2% <-1.2% 194819601970198019901999 Contour map of 2D graduation Assured lives, males, all durations

4. Expectation of life for males aged 60 1970198019902000201020202030 a(55)M PA(90)M PMA80 PMA92 PMA92mc PMA92lc PMA92sc 17 19 21 23 25 27 29 Expectation of Life Times 1955 1968 1980 1992 1999 mc 1999 lc

5. u=2000u=2010 PA(90)-2100% PA(90)-4107% PMA92117%121% PMA92 pilot112%118% PMA92mc128%131% PMA92mc pilot124%128% Financial effects, Males aged 65, 3%

6. year PMA92 (u=year)mc PMA92 (u=year)mc pilot 2000-2.7%-2.5% 2005-2.7%-2.5% 2010-3.0%-2.8% 2015-3.0%-2.8% Financial effects, interest adjust. from PA(90)-2, Males aged 65, 3%

7. Current issues in mortality - Agenda n Update on self-administered pensioner investigation n Update on CMI investigations nData collection nThe work of the Working Parties n Some observations on projecting mortality

8. The SAPS mortality investigation - Summary n 99 Schemes n Number of records in database 1.04m n 6 largest schemes cover 50% of the data n 9 Consultancies have contributed data n Data for 1996 to 2003 n 13 industry types, significant amounts of data for 7 n Lots of data categories

9. Data collection cycle

10. The SAPS mortality investigation - Males LivesAmounts (£’000) Average Exposure2000452,5702,803,9376,196 2001531,1033,404,4616,410 2002412,2833,045,7847,388 All1,395,9579,254,1816,629 Deaths200016,46670,2454,266 200119,86388,6214,462 200214,62273,0684,997 All50,951231,9354,552

11. The SAPS mortality investigation - Females LivesAmounts (£’000) Average Exposure2000324,681840,3322,588 2001368,510973,5042,642 2002266,597819,4773,074 All959,7882,633,3132,744 Deaths200011,13724,2262,175 200113,36429,5712,213 200210,06426,3022,613 All34,56580,0992,317

12. Mortality of self-administered pensioners 2000-02 All retirements : Males : Lives Age

13. Mortality of self-administered pensioners 2000-02 All retirements : Males : Lives Age

14. Mortality of self-administered pensioners 2000-02 All retirements : Males : Amounts Age

15. Mortality of self-administered pensioners 2000-02 All retirements : Males : Amounts Age

16. Mortality of self-administered pensioners 2000-02 All retirements : Females : Lives Age

17. Mortality of self-administered pensioners 2000-02 All retirements : Females : Lives Age

18. Mortality of self-administered pensioners 2000-02 All retirements : Females : Amounts Age

19. Mortality of self-administered pensioners 2000-02 All retirements : Females : Amounts Age

20. Mortality of self-administered pensioners 2000-02 Dependants : Females : Lives Age

21. Mortality of self-administered pensioners 2000-02 Dependants : Females : Amounts Age

22. Mortality of self-administered pensioners 2000-02 Normal : Males : Lives v Amounts (on PML92) Age

23. Status of CMI Data collection n Have reported on 2002 and Quad to life offices n Data problems do exist n1999-2002 Quad is complete

24. Life Office Pensioners 100A/E using the “92” Series projected mortality rates : Males

25. Life Office Pensioners 100A/E using the “92” Series projected mortality rates : Females

26. Life Office Pensioners 100A/E using the “92” Series - medium cohort, projected mortality rates : Males

27. Life Office Pensioners 100A/E using the “92” Series - medium cohort, projected mortality rates : Females

28. Work on the “00” Series mortality tables n Graduation Working Party nWhich tables (not too many!) nHow should they relate to each other nDurations, lives and amounts n Experience paper (a CMIR) nProjections Working Part nWP3 out now(?)

29. The work of the projections working party n Behaviour of different mortality models nDifference between graduation and projection n Effect of size of data set on results nConsidering how to derive “error” range on projection nModel error nParameter error nData error

30. What you need to attempt mortality forecasts (In the absence of a crystal ball ) n... how individual genes affect the ageing process n …how various risk factors affect the ageing process n … how soon can medical technology reduce the effects of ageing n … the impact of lifestyle changes on the various risk factors nUnderstanding of the ageing process

31. Why projections will not be met nMedical technology improvements nEarlier medical interventions to reduce tissue damage nStalling or reversal of ageing processes nHidden diseases of old age nEpidemics nLifestyle changes n Better diets due to health education n Increased intake of vitamins and micro nutrients n Increasing obesity

32. Variation by smoker status,1995-98, Males (AM92) Age

33. Variation by smoker status,1995-98, Females (AF92) Age

34. Mortality by social class

35. Claims by cause as percentage of All Claims Critical Illness v Life Assurance - Males % Cause of Claim / Death Life Assurance Critical Illness

36. Claims by Cause as percentage of All Claims Critical Illness v Life Assurance - Females % Cause of Claim / Death Life Assurance Critical Illness

37. Expectation of life at age 65 in 2000 CountryMaleFemaleCountryMaleFemale Japan17.5022.40Greece15.9118.56 France17.1921.63Norway15.7919.68 Switzerland16.7720.93Belgium15.7019.65 Australia16.7320.23Austria15.6619.61 Sweden16.6520.01Denmark15.2717.77 Israel16.6418.87Netherlands15.1319.54 New Zealand16.5619.93Finland15.0719.18 Italy16.4620.57United Kingdom15.0618.54 Spain16.2220.23Germany15.0618.91 USA16.0219.15Portugal14.3118.01 Canada15.9519.75Ireland14.2518.05 Singapore15.9218.65

38. Projection methodologies Process-based Explanatory-based Extrapolative

39. Fitted and projected model of larger (top) and smaller (bottom) mortality experience. P-spline model with separate smoothing parameters. 95% c.i.s shown.

40. Fitted and projected model of larger (top) and smaller (bottom) mortality experience. P-spline model with smoothing parameter chosen to favour goodness-of-fit. 95% c.i.s shown.

41. Fitted and projected model log μ 65 (t) = a + log μ 65 (t) of larger (top) and smaller (bottom) mortality experience. P-spline model with smoothing parameter chosen to favour goodness-of-fit. 95% c.i.s shown.

42. Things to read nWorking paper 3 – projections nWorking paper 4 – SAPS investigation nWorking paper 8 – Which tables? nLongevity in the 21 st Century nPlus more to come … nWorking paper 1&2 (SIAS paper) – cohort

43. Summary nFalling inflation has magnified the financial effect of this nIt is likely that this mortality trend will continue nIt is possible that medical science will provide a dramatic step forward nAny forecast will be wrong – the range of possible results is wide nThe financial consequences are equally uncertain nIn recent years mortality rates have improved very quickly

44.  Pension seminar 2004 CURRENT ISSUES IN MORTALITY Dublin – 1 June 2004 Tony Leandro