1. Modelling Annuitant Mortality: UK v. France Hélène Queau and Stephen Richards Copyright © UMR and Stephen Richards Consulting Ltd

2. Overview 1.Regional variation in France. 2.Modelling regional mortality with UMR data. 3.Socio-economic group in UK. 4.Annuitant longevity in UK. 5.Does UK-style analysis work for UMR data? 6.Conclusions and questions.

3. 1. Regional variation in France Sources : Insee, État-civil (données domiciliées), estimations localisées de population. Figures are period life expectancies in 2007.

4. 1. Regional variation in France Do UMR annuitants have these same differentials? The answer lies in building a statistical model and fitting it to some experience data…

5. 2. Modelling mortality

6. Mortality model Survival model for force of mortality,  x. 16 models considered (Richards, 2010). Best-fitting model is Makeham-Perks: α, β and ε are estimated from the data.

7. 2. UMR portfolio data Mainly educational professionals. 130,468 records of anuitants over age 60. 582,922 life-years of exposure. 8,949 deaths. 99.2% of annuitants have French address.

8. 2. Region 24 regions, including overseas territories and foreign addresses. Île-de-France has most annuitants, so treat this as the baseline to measure against. On average one department will appear significantly different just by chance: (24- 1)*5%=1.15.

9. 2. Region as risk factor in UMR Source: UMR and Longevitas Ltd. Île-de-France is the baseline and the parameter for this is implicitly zero. Other parameters — such as Age, Gender and Time — are not shown for reasons of space.

10. 2. Regional variation in UMR data 1 region has significantly lighter mortality: Provence-Alpes-Côte d'Azur. 3 regions have significantly heavier mortality: Lorraine, Pays de la Loire, and Picardie. 4 significant differences v. 1.15 expected.

11. 2. Regional variation Results for Lorraine and Picardie match population data. Can we do better than using region? What about using socio-economic group?

12. 3. Socio-economic group

13. 3. Socio-economic group in UK Socio-economic status determined by occupation. Significant variation in life expectancy…

14. Source: ONS data from longitudinal study on population data.

15. 3. Socio-economic group Socio-economic status determined by occupation. Occupation often not available or reliable. Question: How to include in annuity pricing? Answer: Use variables which are correlated with socioeconomic group or replace it.

16. 3. Socio-economic group Two possible replacements: 1.Pension size, i.e. indicator of wealth and income. 2.Geodemographic type, i.e. a direct profile of lifestyle based on location.

17. 3. Geodemographic profile Grouping of people with shared characteristics. Based on address or postcode … …using information about the people who live there (housing type, car ownership etc) In the UK this is based on postcode …

18. 3. Structure of a UK postcode

19. 3. Postcodes Each postcode covers 15–17 households 1.7 million postcodes in UK Too many to use directly! Use geodemographic type Example: Mosaic from Experian

20. 3. Mosaic classification

21. 4. Annuitant mortality in UK

22. Richards & Jones (2004) found several risk factors: Age Gender Pension size Postcode Duration since retirement Region Source: Richards and Jones (2004) using life-company annuitant mortality experience.

23. Importance of socio-economic group FactorStep changeReserve change GenderFemale→male-11.5% LifestyleTop→bottom-11.4% DurationShort→long-10.8% IncomeHigh→low -5.9% RegionSouth→North -5.9% Source: Richards and Jones (2004) “Financial aspects of longevity risk”, SIAS, p39

24. 4. Annuitant mortality in UK Postcodes have been confirmed as risk factors in other works: Richards (2008) and Madrigal et al (2009).

25. 5. Does this apply to UMR data?

26. 5. UMR log(mortality) by age Source: UMR and Longevitas Ltd

27. 5. log(mortality) by age and gender Source: UMR and Longevitas Ltd

28. 5. UMR mortality by pension size Source: UMR and Longevitas Ltd. Crude mortality rate per decile with 1 being smallest 10% of pensions and 10 being the 10% with the largest pensions.

29. 5. Risk factors for UMR annuitants As in the UK, the following are risk factors: 1.Age 2.Gender 3.Pension size But not geodemographic type!

30. 5. Geodemographics in France Geodemographic type based on Îlot. An Îlot covers around 90 households in Paris… …but can cover an entire town or village in rural areas (commune). A UK postcode covers 15–17 households.

31. 5. Geodemographics and UMR data 54 geodemographic types. Expect two or three to appear significant by chance: (54-1)*5%=2.65. F25 has largest exposure time, so make this the baseline. Only two geodemographic types have significantly different mortality.

32. 5. Why is geodemographic type not significant here? Some possibilities: 1.UK postcode has finer granularity than French Îlot. 2.UMR portfolio is homogeneous with respect to occupation: educational professionals. 3.France is more egalitarian than the UK.

33. 6. Conclusions and questions 1.Pension size a significant rating factor, but not as strong as in UK. 2.Geodemographic type not significant for UMR data. 3.Copies of papers available from Stephen (in English, sorry!)

34. References M ADRIGAL ET AL 2009 What longevity predictors should be allowed for when valuing pension-scheme liabilities?, British Actuarial Journal (to appear). R ICHARDS, S. J. AND J ONES, G. L. 2004 Financial aspects of longevity risk, SIAS.Financial aspects of longevity risk R ICHARDS, S. J. 2008 Applying survival models to pensioner mortality data, British Actuarial Journal, Vol 14, Part II, No. 61.Applying survival models to pensioner mortality data R ICHARDS, S. J. 2010 A handbook of parametric survival models for actuarial use, Scandinavian Actuarial Journal (to appear).A handbook of parametric survival models for actuarial use