A-P11 · TOPIC · mortality-and-longevityGRA dossier · service level R0
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Topic dossier · Actuarial and Quantitative Risk

Mortality and longevity

Mortality and longevity assumptions decide the cost of life insurance, annuities and pensions. Base tables describe current death rates; improvement projections estimate how they will fall. Small errors compound over decades. Pandemics, medical advances and social change make the future uncertain. This page sets out how mortality and longevity risk are estimated, where estimates go wrong, the data and models used and who decides.

Systems · around this subjectDesign, not a record
Reserving workbench: the screen's structure, terms and states for Mortality and longevity.Placeholder names and illustrative values; a design, not a record or a live system.

Fig.Reserving workbench. Design wireframe with placeholder names and illustrative values, not a record or a live system.

01In brief

The short answer.

Mortality and longevity risk is the uncertainty in how many people die and how long they live, which decides the cost of life insurance, annuities and pensions. Base tables and improvement projections estimate it, with small errors compounding over decades. Actuaries, trustees, boards, supervisors and governments hold the decisions.

02Geography

Environmental exposure in Japan and Korea.

For mortality and longevity, in GRA's spatial-finance reading, the map opens on population density and night lights with built-up and settlement extent from NASA GIBS, framed on Japan and Korea; 4 further NASA layers wait in the list.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 2

Environmental exposure, for this page

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

For mortality and longevity, in GRA's spatial-finance reading, the map opens on population density and night lights with built-up and settlement extent from NASA GIBS, framed on Japan and Korea; 4 further NASA layers wait in the list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · GDACS · CIESIN/Columbia, NASA SEDAC HBASESources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). CC BY 4.0
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • MODIS and VIIRS atmosphere products, NASA LANCE and LAADS DAAC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • Live: GDACS (EC JRC / UN OCHA) Events are indicative, not official warnings.
  • Context: CIESIN/Columbia, NASA SEDAC HBASE. Center for International Earth Science Information Network (CIESIN), Columbia University, and NASA Socioeconomic Data and Applications Center (SEDAC): Global Human Built-up And Settlement Extent (HBASE) 2010; Seto, Guneralp and Hutyra 2012, Global Grid of Probabilities of Urban Expansion to 2030; Global Urban Heat Island (UHI) Data Set, 2013.
  • Optional layers (14 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Aerosol optical depth, daily: VIIRS NOAA-20 Deep Blue AOD, NASA LANCE · 13 Oct 2026
  • True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026

03The question

Mortality and longevity: how the risk develops.

Health, heat and demographic change in life and pension risk. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.

Mechanism

Improvements compound

A small error in the annual rate of mortality improvement grows across decades of annuity payments, changing liability values materially.

Mechanism

Insured lives differ

Insured and pension populations differ from national populations in income and health. Applying national tables without adjustment misstates mortality.

Mechanism

Shocks disrupt trends

Pandemics raise mortality in the short term and may change trends afterward. Deciding how much weight to give recent data is a judgement.

Mechanism

Longevity risk is transferred

Pension schemes and insurers transfer longevity risk through buy-ins, buy-outs and longevity swaps, concentrating it in insurers and reinsurers.

04Key terms

Terms used on this page.

Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.

TermDefinition
Base mortality tableA table of current death rates by age and sex for a population.
Mortality improvementThe rate at which death rates fall over time at each age.
Pension buy-outA transaction in which an insurer takes over a pension scheme's liabilities.
05Indicators

What is measured, and how well.

Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.

IndicatorUnitCadenceSource typeLabel
Period life expectancy at stated ageyearsannualNational life tablesOFFICIAL
Annual mortality improvement ratepercent per yearannualMortality studiesESTIMATED
Actual-to-expected mortalityratioannualExperience studiesESTIMATED
Excess mortalitypercent above baselineperiodicCivil registration analysisESTIMATED
Longevity risk transfer volumescurrencyannualIndustry reportsREPORTED
Life expectancy gap by deprivationyearsperiodicNational statisticsOFFICIAL
06Canvas

From sources to decisions.

The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.

Pipeline canvas · Mortality and longevity as a run graphDesign, not a record
Mortality and longevity: catastrophe model pipelineA stochastic event catalogue drives the hazard module; exposure and vulnerability turn intensity into damage; the financial module applies policy terms; event and year loss tables give exceedance curves, PML and TVaR. Design of the process, not a record of a run.

Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.

07Sources

Where the evidence comes from.

Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.

SourceWhat it givesLicence class
Human Mortality DatabaseDetailed national mortality dataCC BY 4.0
Continuous Mortality Investigation outputsUK mortality projections and studiesMembers and subscribers; summaries public (to check)
Society of Actuaries mortality tables and improvement scalesUnited States insured and pension mortalityFree, SOA terms
UN World Population ProspectsGlobal mortality estimates and projectionsCC BY 3.0 IGO
Office for National Statistics and national statistics officesNational life tables and deathsOpen Government Licence or terms per office
08Decisions

Who decides, and what the evidence serves.

Actuarial and Quantitative Risk prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.

Who decidesThe questionEvidence this page serves
Life and pension actuariesWhich tables and improvement assumptions to useExperience studies, national data and projection models
Pension trustees and insurer boardsWhether to transfer longevity riskLiability sensitivity and transfer pricing
SupervisorsWhether longevity assumptions are prudentAssumption benchmarks
GovernmentsHow state pension ages and benefits respond to longevityNational projections

Outputs this topic produces

  • Guide to base tables and improvement projections
  • Method note on adjusting national mortality to insured lives
  • Brief on pandemic effects on mortality assumptions
  • Questions for trustees on longevity risk transfer
09Core questions

The questions experts ask.

  • How will mortality rates change by age, sex and socioeconomic group?
  • How uncertain are long-term projections of life expectancy?
  • How do pandemics and extreme heat affect short-term mortality and long-term trends?
  • How do differences in longevity between groups affect pension schemes?
  • How is longevity risk shared between individuals, schemes, insurers and the state?
10Mechanisms

Drivers of mortality change.

Life expectancy has risen over long periods in most countries, driven by reductions in infant mortality, infectious disease and, more recently, cardiovascular mortality. Improvement rates have varied and in some countries slowed, and they differ markedly by socioeconomic group. Pandemics produce sharp excess mortality and can leave lasting effects on health. Extreme heat raises mortality, particularly among older people, and its effects are expected to grow with warming and ageing.

Projection models, such as the Lee-Carter family and its extensions, extrapolate historical trends in age-specific mortality, while cause-of-death and expert-based approaches try to reflect underlying drivers. All face the difficulty that future medical advances and health behaviours are unknowable, so projections carry wide uncertainty over long horizons.

For pension schemes, small changes in assumed life expectancy can materially change liabilities. Basis risk arises when a scheme's members differ from the population whose data were used for projections.

11Methods and data

Methods and their limits.

Projection

Stochastic mortality models

Extrapolating trends with uncertainty; past trends may not continue.

Experience

Experience analysis

Studying a portfolio's own mortality; small portfolios have noisy data.

Excess

Excess mortality analysis

Measuring deaths above expected levels in pandemics or heatwaves.

Limits: population data lag and may be revised, socioeconomic detail is often missing, and long-term projections cannot anticipate medical breakthroughs or new health threats.

13Questions

Questions and answers.

What is a mortality improvement scale?
A set of assumed annual rates at which mortality falls by age and year, used to project future death rates.
Why do insured lives have different mortality?
Because underwriting and income differences mean insured and pension populations are healthier on average than the national population.
What is a longevity swap?
A contract in which a pension scheme or insurer pays fixed amounts in exchange for payments that depend on how long members actually live.
Why does longevity risk matter for pensions?
Because pension payments continue for as long as members live, so longer lives raise the cost of promises already made.
Does heat affect life insurance and pensions?
Heat raises short-term mortality, especially among older people; its long-term effect on trends is an active research question.
Does GRA set mortality assumptions?
No. Assumptions are set by actuaries, schemes and regulators under their own rules.
Actuarial and Quantitative Risk

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