Age Composition of the Population and its Global Pattern

• Age composition (age structure) is the distribution of a population across age groups. With sex, it is the most basic of the demographic attributes.
• It decides the size of the labour force, the burden of dependants, the demand for schools, jobs, pensions and health care, and the population’s future growth.
• Every age structure is the record of a population’s past fertility, mortality and migration. It is read through broad age groups, age indices such as the dependency ratio, the median age and the age–sex pyramid.

Concept and Determinants

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Why Age Composition Matters

• Age structure shapes almost every other population characteristic:
  • Growth: a young population carries momentum. It keeps growing for decades after fertility falls, because large cohorts are still entering the reproductive ages.
  • Work participation and employment: the share in the working ages sets the potential labour supply.
  • Age at marriage and fertility: the number of women in the reproductive ages (15–49) drives the number of births.
  • Educational structure: a broad base demands schools and teachers; an old population demands health care and pensions.
• It is also a planning tool. Projections of school enrolment, job seekers, voters and pensioners all start from the age structure.

Determinants

• Age structure is shaped by three interrelated factors: fertility, mortality and migration.
• John I. Clarke stressed that the three are interdependent. A change in one changes the others, and through them the whole age structure.

Fertility

• Fertility is the most powerful determinant. It sets the size of each new cohort and so the share of children (0–14).
• High fertility gives a large child population and a high dependency ratio. It is linked with:
  • High natural growth, early marriage and low educational attainment.
  • Pressure on employment, with effects felt most by women in less developed countries.
• Falling fertility is the main cause of population ageing. Fewer births narrow the base, so the share of older people rises even if no one lives longer.
  • Fertility levels and their measures are treated under fertility measures and determinants.

Mortality

• Mortality acts through age-specific death rates.
• In less developed countries, the first fall in mortality saves mostly infants and children. This makes the population younger, not older.
• In developed countries, mortality is already low at young ages. Further gains come at older ages, so falling mortality now adds to ageing (“ageing from the top”).

Migration

• Migration is age-selective: most migrants are young adults, usually 20–35 years.
• Out-migration removes working-age adults from the source and leaves children and the elderly behind. Examples are the Uttarakhand hills, rural Kerala and eastern Uttar Pradesh.
• In-migration swells the working ages at the destination. Examples are the Gulf states, Delhi and Mumbai.
• Its effect is less predictable than that of fertility or mortality, but it can redistribute age groups between regions within a short time.

Measuring Age Composition

Age Data and Age Grouping

• Age is collected in completed years: an infant under one year is recorded as 0, a child who has completed one year as 1.
• Single-year data are presented in five-year groups (0–4, 5–9 … 80–84, 85+). These smooth reporting errors and allow regional and temporal comparison.
• Age data carry errors:
  • Age heaping (digit preference): people report ages ending in 0 or 5. It is measured by Whipple’s index.
  • Under-reporting of infants and young girls, and exaggeration of ages among the very old.

Broad Age Groups

• Glenn Trewartha (1969) described the adult group as biologically most reproductive, economically most productive and demographically most mobile. It supports the other two groups with food, clothing, schooling and care.
• India and most developing countries use 15–59 and 60+. Developed countries and the UN use 15–64 and 65+, because working lives are longer there.
• Women outnumber men in the elderly group in most countries, because male mortality is higher at every adult age.

Age Indices

• Dependency ratio (age dependency ratio): dependants per 100 people of working age.
  • Total = (P0–14 + P65+) ÷ P15–64 × 100.
  • It splits into a young-age (child) dependency ratio = P0–14 ÷ P15–64 × 100 and an old-age dependency ratio = P65+ ÷ P15–64 × 100.
  • The same total can hide opposite burdens: schools and jobs in Niger, pensions and care in Japan.
  • The economic dependency ratio counts actual non-workers per worker. It is higher than the age ratio wherever women’s work participation is low.
• Ageing index: older people per 100 children = P65+ (or 60+) ÷ P0–14 × 100.
  • A value above 100 means the old outnumber children. Japan’s is about 260 (2024).
• Median age: the age that divides the population into two equal halves.
  • Below 20 = young population; 20–29 = intermediate; 30 and above = old.
  • It is the single best summary of population maturity.
• Potential support ratio: people aged 15–64 per person aged 65+. It is the inverse of the old-age dependency ratio and falls as a population ages.

Classifying Populations by Age

• Gustav Sundbärg (1900) observed that the 15–49 group forms about half of most populations. Populations differ in how the other half is split between the young and the old:

• The UN (1956) classed populations by the share aged 65+: young below 4%, mature 4–7%, aged above 7%.
  • Current usage adds thresholds: an ageing society at 7%, an aged society at 14% and a super-aged society at 21% (Japan since 2007).
• The classes follow the demographic transition:

Age–Sex Pyramid

• The age–sex pyramid shows each five-year group as a horizontal bar, males on the left and females on the right.
• Its three basic shapes match the three Sundbärg types:
  • Expansive: broad base, high fertility (Niger, Afghanistan).
  • Stationary: near-vertical sides (USA, France).
  • Constrictive: narrow base, top-heavy (Japan, Italy, South Korea).

Global Pattern

Broad Age Groups by Region

• In 2024 the world had 24.7% aged 0–14, 65.1% aged 15–64 and 10.2% aged 65+ (UN World Population Prospects 2024).
  • In 1975 the shares were 37.1%, 57.4% and 5.5%. The world has lost a third of its child share in fifty years.
• Sub-Saharan Africa is the youngest region: 41% under 15 and only 3% aged 65+.
  • Its child share has fallen only slowly, from 45% in 1975, because fertility is still above four children per woman in much of the Sahel.
• South Asia and Latin America are in the middle. Their child share fell by 16–19 points after 1975, and the working ages have swelled to about 68%.
• East Asia changed fastest. Its child share fell from 39% to 18%, and the share aged 65+ rose from 4% to 14%.
• Europe is the oldest region. In the European Union 22% are aged 65+ and only 14% are under 15: the elderly already outnumber children.
• Median age in 2025 ranges from about 16 in Niger and 18 in Nigeria to 29 in India, 40 in China, 48 in Italy and 50 in Japan. The world figure is 31.

Country Contrasts

• Age composition varies most within Asia (from Yemen and Afghanistan to Japan and South Korea) and least within Europe.
• Japan is the oldest large country: 30% aged 65+, a median age near 50, an old-age dependency ratio of 51 and an ageing index of about 260.
• Niger is the youngest: 47% under 15, a young-age dependency ratio of 92 and an ageing index of 6.
• The Gulf states (Qatar, UAE) have very low dependency ratios (about 20–22). This is because of a mass of male migrant workers in the working ages, not because of their own fertility.

• Developed countries (low fertility, long life):
  • Rising dependency ratios driven by the old.
  • A high ageing index.
• Least developed countries (high fertility):
  • High dependency ratios driven by children.
  • A low ageing index.
• Countries in mid-transition (India, Bangladesh, Brazil, Indonesia, China until recently) have the lowest dependency ratios of all. This is the age structure that offers a demographic dividend.

The Demographic Dividend Window

• As fertility falls, the child cohorts shrink before the elderly cohorts grow. For several decades the working-age share peaks and the dependency ratio is at its lowest.
• This is the demographic window of opportunity. The UN Population Division places it where the under-15 share is below 30% and the 65+ share is below 15%.
• The window yields a demographic dividend (faster growth of income per head) only if the extra workers find productive jobs, and if the gains are saved and invested in health, education and skills.
  • East Asia (Japan 1950–1970s; South Korea, Taiwan and China from the 1970s) converted its window into rapid growth through schooling, export manufacturing and high savings.
  • Many Latin American countries had the window but gained far less, because of informal jobs and low investment.
• The window closes as the large working cohorts retire. Dependency then rises again, this time through population ageing.
• How a large working-age population becomes an asset is treated under population as social capital.

Age Composition of India

Trend

• India’s age structure has changed from expansive to working-age heavy:
  • 0–14: 41.2% in 1971 → 30.8% in 2011 (Census) → 24.0% in 2024 (Sample Registration System, SRS).
  • 15–59: 53.4% in 1971 → 66.4% in 2024.
  • 60+: about 6% in 1971 → 8.6% in 2011 → 9.7% in 2024.
• NFHS-6 (2023–24) found 25.5% of household members under 15 and 12.9% aged 60+ (26.5% and 11.8% in NFHS-5). As a household survey it misses migrants living away, so its elderly share runs higher.
• The cause is the fall in fertility, from a total fertility rate of 5.2 in 1971 to 1.9 in 2024 (SRS), below the replacement level. Only six states (Bihar 2.9, Uttar Pradesh, Madhya Pradesh, Rajasthan, Chhattisgarh and Jharkhand) remain above 2.1.
• India’s median age was 24.9 in 2011 (Census) and is about 29 in 2025 (UN WPP 2024). It is projected to reach 38 by 2050.
• India’s demographic window opened around 2013, when the child share fell below 30%. On UN projections it stays open until about 2050, when the 65+ share passes 15%.
  • The dependency ratio is projected to bottom out at about 44 per 100 around 2034.
• India’s growth and the dividend debate are traced in population growth in India. The next census (population reference date 1 March 2027) will give the first full count since 2011.

Spatial Pattern (Census 2011)

• India in 2011: 30.8% aged 0–14 and 5.5% aged 65+ (8.6% aged 60+). The dependency ratio was 57 and the ageing index 18.
• States differ widely because they differ in fertility, mortality, life expectancy and age-selective migration.

• Highest child share and dependency: Bihar (40.2%; dependency ratio 81), followed by Meghalaya, Uttar Pradesh, Jharkhand and Rajasthan.
• Lowest: Goa (21.8%; 41), Tamil Nadu and Kerala.
• Regional pattern:
  • North-central states (the Hindi belt): very high child shares and dependency ratios, low ageing indices.
  • Eastern and north-eastern states: high to moderate. Meghalaya, Nagaland and Arunachal Pradesh are young; Tripura, Sikkim and Manipur are near the average.
  • Western states: low to moderate.
  • Southern states: the lowest child shares and the oldest populations. Kerala has the highest 60+ share (12.6% in 2011; 15.1% in 2024 by SRS).
• Why the states differ: the age map closely follows social and cultural contrasts.
  • Female literacy and age at marriage: Kerala, Tamil Nadu and Goa reached small families early; the Hindi belt, where girls marry young and leave school early, did not.
  • Son preference and patriarchy keep families larger in the north-west and the Ganga plain.
  • Tribal and Christian north-east: Mizoram has high literacy but a young population. Meghalaya, with its matrilineal Khasi and Garo societies, has the highest fertility in the region. Women’s status there does not translate into small families.
  • Social reform and public health (Kerala, Tamil Nadu) lowered infant mortality first, and parents then chose fewer children.
• General rule: where fertility is high, the child share is high, the adult and elderly shares are low, the dependency ratio is high and the ageing index is low.
• Kerala’s dependency ratio (47) is higher than Tamil Nadu’s (43) despite its low fertility. Its elderly share is the highest in India, and male out-migration to the Gulf thins its working ages.

Rural–Urban Variation

• Rural areas have more children and higher dependency, because rural fertility is higher (SRS 2024 total fertility rate: rural 2.1, urban 1.5).
• Urban areas have a larger working-age share, because towns attract young migrants. In SRS 2023 the 15–59 group was 68.8% of the urban population against 64.6% of the rural.
• Rural areas also hold most of the elderly: about 71% of India’s older persons live in villages (Census 2011), which leaves them far from health care.

Implications for India

• Dividend: India will add to its working-age population until the 2040s. The gain depends on jobs, skills and women’s work. Female work participation is still low, which is discussed under economic composition and work participation.
• Regional sequence: the south will age first. Bihar and Uttar Pradesh will supply most of India’s new workers, so north-to-south migration of labour is likely to grow.
• Ageing: the 60+ population is projected to double to about 20% by 2050 (India Ageing Report 2023). This needs planning for pensions, geriatric care and the feminisation of old age.