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How it works

How this simulation works

Every number traces to a source, and every assumption is written down.

Data sources

CategoryUsed for
UN WPP 2024 life tablesyearly death rates and life expectancy
World Bank · OWID (population, crude birth rate)weights for the birth draw
OWID / UNESCO (years of schooling)average schooling trajectory
OWID GDP per capita (PPP)scale of job income
OWID total fertility ratetarget for the fertility model
WHO GHE 2023cause structure by age and sex
ISO 3166 / UN M49matching entities and the map

How it is computed

Six steps, from birth to death.

  1. Birth draw: Weighted by annual births, so India is drawn as often as its share of world births.
  2. Death: Each year draws a death from that country's and sex's life table; the cause comes from WHO GHE by death year, age band and sex.
  3. Education: A target schooling length is drawn first, then accumulated year by year and cut short by dropout.
  4. Work: Jobs match by schooling; income = GDP per capita × job multiplier × experience curve.
  5. Family: Partnership, births and relatives' deaths follow that country's data and life tables.
  6. Events: Decisions trigger inside age windows, and every stat or money change is recorded.

How close is it

Life expectancy
Recomputed e0 versus the source value: mean gap 0.006 years, max 0.018 (645 tables).
Simulated lifespan
20,000 lives per country; every gap versus the source e0 is under 0.5 years.
Years of schooling
Five countries (US, DE, JP, BR, IN) stay within 0.48 years.
Fertility
Average births in four countries stay within 0.5 of the TFR.

Causes of death are shifting

Causes of death under five: infections falling, perinatal rising (global, weighted by deaths).

YearInfectionsPerinatal
200055.1%31.0%
201047.9%35.5%
201545.6%36.6%
201944.8%37.2%
202345.5%36.9%

Source: WHO GHE 2000–2023. Simulations use the 2023 structure because every life starts in 2023.

What is still shaky

This is not a prediction about any real person.