Modelling Annuitant Mortality:UK v. France

Hélène Queau and Stephen Richards

Copyright © UMR and Stephen Richards Consulting Ltd

Overview1. 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.

1. Regional variation in FranceRegion Female life expectancy Difference

Rhône-Alpes 85.2 0.3Midi-Pyrénées 85.0 0.1Poitou-Charentes 85.0 0.1Île-de-France 84.9 0.0Pays de la Loire 84.9 0.0Corse 84.8 -0.1Aquitaine 84.7 -0.2Centre 84.6 -0.3Provence-Alpes-Côte d'Azur 84.6 -0.3Languedoc-Roussil lon 84.5 -0.4Limousin 84.5 -0.4Franche-Comté 84.4 -0.5Bourgogne 84.3 -0.6Basse-Normandie 84.2 -0.7Alsace 84.0 -0.9Auvergne 83.9 -1.0Bretagne 83.8 -1.1Haute-Normandie 83.6 -1.3Champagne-Ardenne 83.5 -1.4Lorraine 83.1 -1.8Picardie 82.4 -2.5Nord-Pas-de-Calais 82.2 -2.7

Sources : Insee, État-civil (données domiciliées), estimations localisées de population.

Figures are period life expectancies in 2007.

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…

2. Modelling mortality

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.

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.

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.

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.

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.

2. Regional variation• Results for Lorraine and Picardie match

population data.

• Can we do better than using region?

• What about using socio-economic group?

3. Socio-economic group

3. Socio-economic group in UK• Socio-economic status determined by

occupation.

• Significant variation in life expectancy…

Source: ONS data from longitudinal study on population data.

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.

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.

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 …

3. Structure of a UK postcode

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

3. Mosaic classification

4. Annuitant mortality in UK

4. Annuitant mortality in UK• 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.

Importance of socio-economic groupFactor Step change Reserve

change

Gender Female→male -11.5%

Lifestyle Top→bottom -11.4%

Duration Short→long -10.8%

Income High→low -5.9%

Region South→North -5.9%Source: Richards and Jones (2004) “Financial aspects of longevity risk”, SIAS, p39

4. Annuitant mortality in UK• Postcodes have been confirmed as risk

factors in other works: Richards (2008) and Madrigal et al (2009).

5. Does this apply to UMR data?

5. UMR log(mortality) by age

Source: UMR and Longevitas Ltd

5. log(mortality) by age and gender

Source: UMR and Longevitas Ltd

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.

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!

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.

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.

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.

6. Conclusions and questions1. 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!)

ReferencesMADRIGAL ET AL 2009 What longevity predictors should be

allowed for when valuing pension-scheme liabilities?, British Actuarial Journal (to appear).

RICHARDS, S. J. AND JONES, G. L. 2004 Financial aspects of longevity risk, SIAS.

RICHARDS, S. J. 2008 Applying survival models to pensioner mortality data, British Actuarial Journal, Vol 14, Part II, No. 61.

RICHARDS, S. J. 2010 A handbook of parametric survival models for actuarial use, Scandinavian Actuarial Journal (to appear).