Life Table Analysis
Also known as: Mortality Table, Actuarial Table, Survival Table, Yaşam Tablosu
A life table is a systematic, age-structured summary of the mortality experience of a population. It traces a hypothetical cohort of births — conventionally 100,000 — through successive age intervals, recording how many survive, how many die, and how many person-years are lived at each interval. The method was formalized in its modern probabilistic form by Chiang (1984), synthesizing centuries of actuarial and demographic practice into a rigorous statistical framework applicable to human and biological populations alike.
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When to use it
Life table analysis is appropriate when age-specific mortality or survival data are available for a well-defined population and the goal is to summarize longevity, compare mortality across groups or time periods, or provide baseline survival estimates for demographic projections. The period life table assumes current rates apply throughout a cohort's lifetime (cross-sectional assumption); the cohort life table follows an actual birth cohort and requires long-term data. The method assumes age-specific rates are constant within intervals and that deaths are uniformly distributed. It is not suitable for small populations where stochastic variation makes rates unstable, nor for dynamic populations without reliable vital registration. Alternatives include the Kaplan-Meier estimator (censored clinical data) or the Lee-Carter model (mortality forecasting).
Strengths & limitations
- Provides a complete, age-by-age portrait of mortality that single summary statistics cannot capture.
- Life expectancy at birth (e_0) is internationally comparable and independent of population age structure.
- Flexible framework: period and cohort variants address different research questions with the same computational core.
- Easily extended to multiple-decrement tables (cause-specific mortality, disability) without changing the core algorithm.
- Period life tables conflate cross-sectional rates with individual experience; they do not predict any real cohort's future.
- Reliable results require large, well-registered populations; sparse data lead to unstable q_x estimates in open-ended age groups.
- The uniform distribution assumption for deaths within each interval introduces bias in ages with rapidly changing mortality (e.g., infancy).
- Cause-deleted life tables assume independence of competing causes, which is often violated in practice.
Frequently asked
What is the difference between a period life table and a cohort life table?
A period life table uses age-specific death rates observed across all age groups in a single calendar year (or short period), constructing a synthetic cross-sectional picture. A cohort life table follows an actual birth cohort from birth to extinction, requiring decades of data. Period tables are far more common because they are timely and do not require waiting for a cohort to die out, but they may misrepresent the experience of any real cohort if mortality is changing over time.
How is life expectancy at birth different from average age at death?
Life expectancy at birth (e_0) is a weighted average of the years lived across all age groups under the current age-specific mortality schedule, anchored to a radix of 100,000 births. Average age at death is a raw mean of the observed death distribution in a given year, which is strongly influenced by the age structure of the living population. The two measures differ whenever a population is growing, aging, or experiencing rapid mortality change, making e_0 the preferred comparative indicator.
Can a life table be applied to non-human populations?
Yes. The mathematical structure of the life table is generic: any population for which age-specific birth and death events can be tracked yields valid input. Ecologists routinely construct life tables for plant and animal cohorts to estimate net reproductive rates and intrinsic growth rates (r). The only modification needed is redefining the time and age axes to match the species' life history; the core columns and their relationships remain unchanged.
Sources
- Chiang, C. L. (1984). The Life Table and Its Applications. Robert E. Krieger Publishing. ISBN: 978-0-89874-565-2
How to cite this page
ScholarGate. (2026, June 2). Life Table Analysis. ScholarGate. https://scholargate.app/en/demography/life-table
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Cohort-Component ProjectionDemography↔ compare
- Kaplan-MeierSurvival↔ compare
- Lee-Carter ModelDemography↔ compare