Process / pipelineGender StudiesExcess mortality & sex ratiosPipeline

Missing Women Estimation

Also known as: Missing Women, Excess Female Mortality Estimation, Sen Missing Women Method

OriginatorAmartya SenYear1990Sources3Related methods2

Missing women estimation quantifies the number of women and girls who are absent from a population because of gender bias in mortality and, in some settings, sex-selective abortion. Introduced by economist Amartya Sen in 1990 and 1992, the method compares the observed female population (or female deaths) with the number expected under a benchmark sex ratio that would prevail absent discrimination. The resulting deficit — famously estimated at more than 100 million worldwide — is a stark demographic measure of cumulative anti-female bias.

Key highlights

  • Renders extreme, cumulative gender inequality visible in a single, arresting headcount figure.
  • Grounded in basic demographic accounting, so it is transparent and reproducible from standard population data.
  • Flow-and-cause decompositions locate the deficit by age and cause, sharpening policy attention beyond infanticide alone.
  • Has driven decades of research and policy on sex-selective abortion, maternal mortality, and son preference.

Intuition

This section is available to Pro members. Upgrade to Pro

How it works

This section is available to Pro members. Upgrade to Pro

When to use it

Use missing women estimation to quantify the cumulative demographic toll of gender bias in mortality and natality across countries or over time, and to locate where in the life course and through which causes that bias operates. It is well suited to macro-level demographic and development analysis. It is less suitable for individual-level causal inference: it is an accounting comparison against a counterfactual benchmark, sensitive to the reference chosen, and cannot by itself distinguish discrimination from non-discriminatory differences in mortality and population structure.

Strengths & limitations

Strengths
  • Renders extreme, cumulative gender inequality visible in a single, arresting headcount figure.
  • Grounded in basic demographic accounting, so it is transparent and reproducible from standard population data.
  • Flow-and-cause decompositions locate the deficit by age and cause, sharpening policy attention beyond infanticide alone.
  • Has driven decades of research and policy on sex-selective abortion, maternal mortality, and son preference.
Limitations
  • The estimate depends critically on the benchmark sex ratio, which is a contested counterfactual choice.
  • A stock estimate conflates births never realised (sex selection) with deaths of girls and women, which differ in cause and remedy.
  • Differences in population age structure and migration can bias the comparison if not handled carefully.
  • It identifies a deficit consistent with discrimination but cannot, on its own, prove discrimination as the cause.

Common pitfalls

This section is available to Pro members. Upgrade to Pro

Applications

This section is available to Pro members. Upgrade to Pro

Frequently asked

Why are women expected to outnumber men absent discrimination?

Although slightly more boys are born than girls, females have a biological survival advantage at almost every age, so in populations with equal care the female-to-male ratio rises with age and women come to outnumber men overall. When men outnumber women, especially after accounting for migration and age structure, it signals that girls and women are dying at elevated rates or are not being born — the demographic footprint Sen called missing women.

What is the difference between the stock and flow estimates?

Sen's original calculation is a stock: it compares the current female population with the number expected under a benchmark, yielding a one-time deficit. Anderson and Ray's flow approach instead estimates how many excess female deaths occur each year, decomposed by age and cause. The flow framing shifts the picture from a fixed historical total toward an ongoing annual toll and shows that much of it is adult disease mortality, not only infant deaths or sex selection.

Does the missing-women figure prove discrimination?

Not by itself. The estimate is an accounting comparison against a counterfactual benchmark; it shows a female deficit consistent with gender bias. Establishing discrimination as the cause requires additional evidence — for example skewed sex ratios at birth pointing to selection, or excess female mortality concentrated where care is allocated unequally. The number is a powerful indicator, but causal attribution rests on supplementary demographic and epidemiological analysis.

Sources

  1. 1.
    Sen, A. (1992). Missing women. BMJ, 304(6827), 587–588.
  2. 2.
    Sen, A. (1990). More than 100 million women are missing. The New York Review of Books, 37(20), 61–66.
  3. 3.
    Anderson, S., & Ray, D. (2010). Missing women: Age and disease. The Review of Economic Studies, 77(4), 1262–1300.

You have read it. What now?

Cite this page

ScholarGate. (2026, June 22). Missing Women Estimation. ScholarGate. https://scholargate.app/gender-studies/missing-women-estimation