Chapter 4 — Age and sex composition

What we mean by age and sex composition
Age composition is the breakdown of a population into age groups. Sex composition is the count and proportion of males and females. Together they provide a two-dimensional picture: not just how many people live in a place, but who they are in terms of age and gender. This structure matters because different age-sex groups use different services and contribute differently to the economy and society.

How to summarise composition
We summarise structure with tables of age-by-sex, percentages, and visual tools such as age-sex pyramids. Analysts also compute summary indicators: sex ratio, child sex ratio, median age, dependency ratios and ageing index. These measures condense complex distributions into numbers that make comparison and trend analysis easier. Reading both the numbers and the pyramid together gives the clearest insight.

Demographic drivers and timing
Composition results from three processes: fertility (births), mortality (deaths) and migration (movement). Fertility determines the size of youngest cohorts; mortality determines how many survive to older ages; migration shifts people across places and often selects young adults. These processes have time-lags: past fertility shapes current adult population, so demographic effects appear decades after underlying causes change. Understanding timing helps explain why the same policy can have immediate or delayed effects.

Practical consequences
A young population requires investment in education, childcare, maternal health and future jobs. A large working-age share creates potential for economic growth, provided jobs and skills exist. An ageing population increases demand for pensions, chronic disease management and long-term care. Sex balance affects marriage patterns, labour force participation and caregiving responsibilities. Planners who ignore age-sex structure risk misallocating resources and failing to meet future needs.

Reading structure critically
Always ask: what created this shape? Are changes due to fertility decline, improved survival, or large migration? Look for bulges (baby booms or migration inflows), indentations (wars or epidemics) and sex asymmetries (migration or sex-differential mortality). Link observed patterns to local history and policies to make sensible interpretations. Students should practise with multiple examples to develop the habit of linking data to processes and policy implications.

Purpose of standard classification
Using standard age groups brings clarity and comparability. Five-year age groups (0–4, 5–9, 10–14, …) are widely used because they provide enough detail for demographic analysis while avoiding excessive fragmentation. For policy use we often combine these into broad bands: children (0–14), working-age (15–64) and elderly (65+ or 60+ depending on local context). Choosing consistent groupings ensures that statistics from different years or regions can be compared reliably.

Why five-year intervals are helpful
Five-year intervals smooth short-term fluctuations and allow cohort tracing: a group aged 5–9 in one census will be roughly 10–14 five years later. This makes it possible to study cohort effects—how a set of people born in the same period move through the age structure. Age-specific fertility and mortality rates are usually computed for five-year groups; summing these rates provides summary measures such as the total fertility rate.

Special-purpose groupings
Policy and research needs sometimes require different classifications. For schooling, groups like 5–9 and 10–14 match primary and middle school cohorts. For fertility studies, women aged 15–49 are the standard reproductive-age group. Youth studies often use 15–24 or 15–29 to focus on transition to work and family formation. For eldercare, planners may separate 60–69, 70–79 and 80+ to reflect differences in care needs.

Working-age definitions and labour market relevance
The widely used 15–64 band measures the potential labour force, but it is only a proxy. Labour force participation varies by sex, education, and cultural norms. Some countries use 15–59 where legal retirement is earlier or labour market entry later. Analysts should clarify whether working-age is a potential supply (15–64) or an active workforce (those actually employed or seeking work).

Comparability and caution
Always state the age cut-offs used when presenting statistics. Differences in definitions (for example, 60+ vs 65+ for elderly) can change indicators like dependency ratios and ageing indexes. When comparing regions, adjust for these definitional differences. Good practice includes showing both five-year detailed tables and broad aggregated bands to serve analytical and policy audiences.

Meaning and measurement
Sex ratio is a simple yet powerful measure: number of males per 1000 females. It is calculated for the whole population, for age groups, or for births. Child sex ratio usually refers to girls per 1000 boys in the 0–6 or 0–5 group and is sensitive to recent birth and survival patterns. Age-specific sex ratios show how the balance changes across life stages and reveal biological and social influences.

Biological baseline and deviations
Biologically, slightly more boys are born than girls; natural sex ratio at birth tends to be around 103–107 boys per 100 girls (about 1030–1070 males per 1000 females). Deviations from this range at birth suggest human intervention: prenatal sex selection or misreporting. At older ages, higher male mortality typically reduces male share, so elderly populations often have more women.

Social and migration influences
Child sex ratio is affected by gender preferences, access to sex-determination technologies and differential care. Migratory flows strongly affect working-age sex ratios: male-dominated labour migration increases the male share in destination cities, while female-dominated migration for domestic or care work can increase female shares in some places. Mortality differences—due to occupation, behaviour, or healthcare access—also shift balances across ages.

Interpreting patterns
When interpreting sex ratios, consider multiple explanations and examine age patterns. A low child sex ratio combined with normal adult ratios suggests prenatal selection or female child neglect. A high working-age male share likely points to male labour migration. An elderly female majority is typical of higher male mortality. Use supporting data—birth registers, health records, migration statistics—to validate interpretations.

Policy responses and ethical issues
Correcting skewed sex ratios requires legal measures (banning sex-selective practices), health system strengthening to reduce female child mortality, incentives for girls’ education, and norms-change campaigns. Ethical approaches emphasise rights-based family planning and female empowerment rather than coercion. Monitoring trends through regular data collection is essential to assess policy impact.

Construction steps
To construct an age-sex pyramid, group the population by age intervals (commonly five years) and by sex. For each age group, draw horizontal bars on opposite sides of a vertical axis: males on the left and females on the right. Bars can represent absolute numbers or percentages. Order age groups from youngest at the bottom to oldest at the top.

Key shapes and their meaning
There are three broad shapes often used in teaching: expansive, constrictive and stationary. An expansive pyramid has a wide base and rapidly tapering sides, indicating high fertility and a large child population. A constrictive pyramid has a narrower base and larger middle sections, reflecting low fertility and a growing working-age share. A stationary pyramid shows roughly equal widths through adult ages and slower population change, typical of stable or low-growth populations.

Reading signals
Look for base width (fertility), slope of the sides (mortality and past fertility trends), bulges (baby booms or migration inflows) and indentations (wars, epidemics or excess mortality). Compare male and female sides for symmetry: consistent differences at older ages point to sex-differential mortality; working-age asymmetry suggests migration patterns. Pyramids for different years show the transition over time.

Uses in planning
Pyramids guide decisions about schools, workforce training, healthcare and pensions. An expansive pyramid warns planners to invest in education and maternal-child health; a bulging working-age band signals potential for demographic dividend if jobs are available; an ageing pyramid points to future pressures on pensions and eldercare. Planners must combine pyramid reading with contextual data on fertility, mortality and migration.

Practical tips
Always label axes and age groups, indicate whether bars show numbers or percentages, and check data quality for age reporting errors. Practice by sketching pyramids for different hypothetical scenarios to build intuition about how demographic processes shape the form.

Definitions
Dependency ratios summarise the relative size of dependent age groups compared with the working-age population. Total dependency ratio = ((population 0–14 + population 65+)/population 15–64) × 100. Child dependency = (0–14 / 15–64) × 100 and old-age dependency = (65+ / 15–64) × 100. The ageing index compares elderly to young: ageing index = (65+ / 0–14) × 100.

Interpretation for economies
High dependency ratios imply more non-working people per worker and higher public and private spending needs for education, health and pensions. Low dependency ratios, especially when due to falling fertility and rising working-age share, create a potential demographic dividend: a window for higher economic growth if the workforce is educated and employed.

Child vs old-age dependency
Child dependency focuses resources on education and maternal-child health, while old-age dependency focuses them on pensions and chronic disease management. Policy responses differ: investing in schooling and job creation for young peoples; pension reform and healthcare reorientation for ageing societies.

Limitations
Dependency ratios are crude: they assume all 15–64 are productive and all 0–14 and 65+ are dependents, which ignores female labour force participation, informal work, and varying retirement ages. They also mask internal heterogeneity—two regions with similar ratios may have very different employment and social structures.

Policy implications
To capture a demographic dividend, countries must invest in education, health and job-creating sectors and adopt policies that increase labour force participation (especially among women). To handle ageing, policy options include pension reform, encouraging later retirement, lifelong learning and healthcare reorientation toward chronic and geriatric care.

What causes population ageing?
Population ageing results from sustained declines in fertility and improvements in mortality that increase life expectancy. When fewer children are born and more people survive to older ages, the age distribution shifts upward. Migration patterns—outflow of young people—can accelerate ageing locally.

Socioeconomic consequences
Ageing populations change public spending needs and labour market dynamics. Pension and healthcare costs rise, demand for long-term care increases, and the ratio of workers to retirees falls. This can strain public finances and reduce overall economic growth if productivity gains do not offset the smaller workforce.

Labour market and household effects
Ageing may shrink labour supply and raise wages in some sectors, encourage automation, and increase demand for services tailored to older adults. Households may face higher caregiving burdens and lower intergenerational support if family sizes shrink. Gendered effects matter: women often provide unpaid care and may leave formal employment to care for elderly relatives.

Health and social considerations
Health systems must move from acute infectious disease models to chronic disease management and geriatric care. Prevention and healthy ageing programmes can reduce morbidity even if longevity increases. Social policies must address isolation, elder abuse, and adequate housing and transport for older adults.

Policy responses
Responses include pension reform to ensure sustainability, encouraging later retirement and employment among older adults, promoting lifelong learning and re-skilling, and improving primary healthcare and long-term care infrastructure. Policies to boost female labour participation and controlled immigration of working-age people can mitigate workforce declines. Effective action requires advance planning and multisectoral coordination.

Definition and origin
A youth bulge is when a large share of the population falls into young adult ages (often 15–29). It typically follows a phase of high fertility combined with declining child mortality, producing large birth cohorts that move into adolescence and young adulthood together.

Opportunities
When a youth bulge coincides with investments in education, health and job creation, it can produce a demographic dividend: accelerated economic growth as a large working-age cohort enters employment. Young entrepreneurs, increased savings and higher productivity are potential benefits if policies and markets absorb the labour supply.

Risks and social effects
If the economy cannot provide jobs, a youth bulge can generate high youth unemployment, underemployment and social frustration. This can increase crime rates, social unrest and political instability, especially where governance is weak and opportunities are limited. The timing is critical—policy failure during the bulge can create long-lived cohorts of underutilised adults.

Policy measures
Key measures include expanding quality education and vocational training, aligning curricula with labour market needs, promoting entrepreneurship and access to finance, and creating public and private sector jobs. Reproductive health and family planning help shape future cohort sizes, while social protection and targeted youth employment programmes alleviate short-term pressures.

Long-term view
If well managed, a youth bulge can convert into decades of higher productivity as cohorts age into prime working years. If mismanaged, it can leave persistent economic and social challenges. Thus early and sustained investment is crucial to turn demographic potential into development outcomes.

Population momentum explained
Population momentum is the continued growth of a population even after fertility declines to replacement level, caused by the existing age structure. If many people are in or entering reproductive ages, total births remain high for some decades despite lower fertility rates because there are more potential mothers.

Why momentum matters
Momentum shows that demographic change is slow-moving: policies that change fertility today will take time to alter population size and age structure. This matters for long-term planning: housing, schools and pensions must account for the lagged responses of population to policy interventions.

Projection fundamentals
Population projections estimate future numbers based on assumptions about fertility, mortality and migration. Simple projections use current growth rates algebraically, but for planning age-sex structures the cohort-component method is preferred because it projects cohorts forward applying age-specific rates of survival, fertility and migration.

Scenario-building and uncertainty
Projections produce scenarios—high, medium, low—by varying assumptions. These scenarios are conditional on the assumptions; they are not exact predictions but useful ranges. Sensitivity analysis helps planners understand which assumptions (fertility, mortality or migration) most affect results and where policy can alter trajectories.

Practical use and timing
Projections inform education demand, healthcare needs and pension liabilities. Momentum explains why population size may continue growing even after fertility falls to replacement: a large cohort already in or entering reproductive age keeps births high. Policymakers should therefore set realistic timeframes and plan for phased adjustments rather than expect immediate demographic outcomes from fertility policies.

Key fertility measures
Crude birth rate (CBR) is births per 1000 population per year. General fertility rate (GFR) is births per 1000 women aged 15–49. Total fertility rate (TFR) is the average number of children a woman would bear over her lifetime given current age-specific fertility rates. TFR indicates whether a population is above or below replacement level (about 2.1 in most contexts).

Direct effect on the pyramid base
Fertility determines the number of children born and thus the width of the pyramid base. High TFR sustains broad bases and youthful populations; sustained low TFR gradually narrows the base, reduces child dependency and eventually contributes to ageing. The pace of change matters: rapid fertility decline shortens the transition and creates a faster shift in age structure.

Drivers of fertility decline
Fertility declines with rising female education, increased female labour participation, urbanisation, improved child survival, and wider access to contraception. Changes in cultural norms, economic incentives, and policies that empower women also reduce desired family size. Fertility transition is usually gradual and differs by region and socioeconomic group.

Policy instruments
Family planning services, reproductive health education, and female schooling are effective and rights-based ways to lower fertility. Economic incentives and social security can also influence family size decisions indirectly by changing the perceived cost and benefit of children. Coercive measures should be avoided for ethical and social reasons.

Interaction with other components
Fertility interacts with mortality and migration: improved survival increases the number of women reaching childbearing age, and migration of reproductive-age people alters local birth numbers. Analysts must consider all components when projecting age structure; fertility alone does not determine future population without accounting for survival and movement.

Mortality measures and patterns
Crude death rate (CDR) is deaths per 1000 population. Infant mortality rate (IMR) counts deaths under age one per 1000 live births. Under-five mortality focuses on deaths before age five. Life expectancy at birth summarises average longevity. Age-specific death rates show which ages experience higher mortality and are essential to construct life tables and survivorship curves.

How mortality shapes age structure
When mortality falls, especially at young ages, more children survive, temporarily increasing the child population. Improvements in adult and old-age survival raise the number of elders and widen the pyramid top. Patterns of mortality decline influence the timing of ageing: if child mortality falls first, the base grows; if adult mortality falls later, the older cohorts expand and ageing accelerates.

Sex differences in survivorship
Men often have higher mortality in many age groups because of occupational risks, lifestyle factors and certain diseases, producing a female-majority at older ages. These differences affect pension planning and gender-sensitive health services. Understanding sex-specific mortality improves accuracy in projecting future populations and service needs.

Public health implications
Mortality decline shifts health priorities from infectious disease control to chronic disease management and long-term care. Health systems must adapt by training professionals in geriatric care, strengthening primary care for chronic conditions, and expanding preventive measures. Investments in maternal and child health yield long-term demographic and economic benefits.

Analytical tools and projections
Life tables provide survivorship probabilities (lx) and feed into cohort-component projections. Accurate mortality measurement, especially of infant and adult mortality, is crucial for reliable projections and planning. Analysts must be aware of data quality issues like under-registration of deaths and apply correction methods where necessary.

Selective character of migration
Migration is selective by age and often by sex. Young adults, seeking education and jobs, are the most mobile group. Labour migration is frequently male-dominated in some sectors, while migration for domestic work or education may be female-dominated in others. Because migrants concentrate in specific ages and sexes, migration changes local age-sex structures markedly even if the national population is unaffected.

Effects on origin areas
Out-migration of working-age adults raises dependency ratios in origin areas and can lead to ageing at local levels. Children and elderly left behind may face reduced informal care. However, remittances from migrants can raise household incomes, increase investment in education and health, and alter local consumption patterns. The long-term demographic effect depends on whether migration is temporary or permanent and whether migrants return with savings and skills.

Effects on destination areas
In-migration of young adults boosts the working-age population and can reduce median age in destination areas. If migrants are mostly male, working-age sex ratios become male-skewed, affecting housing, health services and social dynamics. Migrants may fill labour shortages, stimulate local economies, and increase demand for public services; unplanned inflows can strain infrastructure and housing markets.

Circular and seasonal migration
Seasonal migration causes predictable short-term changes in age-sex composition—rural areas may lose workers during harvest off-seasons while urban informal sectors absorb them temporarily. Circular migration complicates measurement because census definitions (usual resident vs present on census night) capture different groups. Understanding these patterns is essential for accurate planning and service provision.

Policy and data challenges
Measuring migration requires surveys, administrative records and careful census questions. Undocumented migration and short-term moves are often missed. Policy responses include regional development to reduce distress migration, urban planning to accommodate inflows, migrant rights protections, and targeted services for migrant communities. Localised demographic impacts require tailored responses rather than one-size-fits-all national policies.

Primary data sources
The census is the most comprehensive source of age-sex data, providing detailed counts for small areas. Vital registration records births and deaths and are essential for rates. Household surveys give fertility and mortality estimates where registration is incomplete. Administrative sources—school enrolment, voter lists, health registries—provide additional information but vary in coverage and purpose.

Common measurement problems
Age misreporting and age heaping (rounding to ages ending in 0 or 5) distort age distributions and bias indicators like median age. Under-registration of births or deaths leads to underestimation of child population or mortality. Migration is particularly hard to measure—temporary, seasonal and undocumented moves often go uncounted. Female under-enumeration in some contexts biases child sex ratio and female participation estimates.

Detecting and quantifying errors
Demographers use tools to detect errors: Whipple’s index measures preference for ages ending in 0 or 5; Myers’ blended index gives preference across all terminal digits. Age-sex ratios across successive censuses can reveal inconsistencies and suggest undercounting. Comparing census counts with survey estimates and registration records helps triangulate the truth.

Correction and smoothing methods
Where heaping occurs, smoothing techniques redistribute reported ages according to expected patterns. Inter-censal cohort methods track cohorts between censuses, adjusting for survival and estimated migration to reconcile counts. Model life tables can replace missing mortality schedules. Analysts must document assumptions and present corrected and original series so users understand choices and limitations.

Definitions, timing and ethics
De jure (usual resident) vs de facto (present on census night) counting changes migrant tallies. Age reporting conventions (completed years vs age nearest birthday) must be consistent. Ethical concerns—privacy and consent—are crucial when collecting sensitive reproductive or migration data. Transparent reporting of methods and caveats improves the usefulness of age-sex statistics for planning.

Overview of cohort-component method
The cohort-component method projects population by ageing each cohort forward while applying fertility, mortality and migration assumptions. It is the standard approach for producing realistic future age-sex structures because it models the components of demographic change directly.

Steps in the method
1) Start with a base population by age and sex. 2) Age each cohort forward for the projection interval (usually five years). 3) Apply survival probabilities or age-specific mortality rates to estimate survivors in each cohort. 4) Add expected in-migrants and subtract expected out-migrants by age and sex. 5) Estimate births by applying age-specific fertility rates to women in reproductive ages to produce a new 0–4 cohort and allocate births by sex using sex ratio at birth. 6) Repeat steps for each projection interval.

Advantages and assumptions
The method’s advantage is detailed age-sex output useful for policy. Its accuracy depends on the quality of base data and the credibility of assumptions about future fertility, mortality and migration. Errors in age-specific rates accumulate over long projection horizons, so periodic updates and scenario comparisons are important.

Creating scenarios
Planners produce multiple scenarios (high, medium, low) by varying fertility, mortality and migration assumptions. Scenarios show a range of possible futures and help identify sensitive variables. Sensitivity analysis reveals which assumptions most affect outcomes and where policy interventions could change trajectories.

Uses in planning
Cohort-component projections inform education planning (projecting school-age cohorts), health planning (projecting elderly care needs), labour market analysis and pension system design. Because they give age-sex specific numbers they are invaluable for sectoral planning, budget forecasting and infrastructure investment decisions.

Health sector impacts
Age structure determines health service needs. Young populations need maternal and child health services, immunisation and nutrition programmes. Ageing populations require chronic disease management, geriatric care and long-term support. Health workforce planning must match these changing needs by training appropriate specialists, community health workers and primary care staff.

Education planning
The number of school-age children drives demand for teachers, classrooms and curriculum reform. Rapid growth in young cohorts requires building schools and scaling teacher training. Declining school-age populations shift focus to quality, higher education and vocational training to match labour market demands.

Labour market effects
Large working-age populations create opportunities for growth if jobs exist. High youth cohorts require rapid job creation; failure leads to unemployment and social problems. Ageing reduces labour supply and may increase wages where labour is scarce, but automation and productivity improvements can offset shortages. Policies promoting female labour participation, re-skilling and lifelong learning help maintain labour force size.

Sectoral and spatial planning
Age-sex composition varies across regions, so health and education investments must be geographically targeted. Urban planning must accommodate working-age migrants with affordable housing and transport, while rural areas with ageing may need outreach health services and social support networks.

Integrated policy response
Coordinated policies across health, education, labour and social protection are essential. For example, investing in girls’ education reduces fertility and improves labour outcomes; health improvements raise labour productivity. Anticipatory planning yields better outcomes than reactive measures when composition changes rapidly.

Policy types
Policies to address age and sex imbalances are varied: family planning and maternal-child programmes to influence fertility; laws and campaigns to prevent sex-selective practices; pension and labour reforms to manage ageing; education, skill development and job creation to harness youth bulges; and migration policies to balance regional needs.

Measures to improve child sex ratio
Effective measures combine legal bans on prenatal sex determination with enforcement, incentives for families with girls, awareness campaigns valuing girls’ education and safety, and improved access to maternal and child health to reduce female child mortality. Monitoring and local outreach are essential because norms change slowly.

Managing ageing
Policies include pension reform for fiscal sustainability, promoting active ageing and later retirement, healthcare reorientation toward chronic disease management, and support for informal caregivers. Promoting higher labour participation of women and older workers mitigates labour shortages and reduces pension burdens.

Realising demographic dividend
To benefit from a large working-age share, governments must invest in education, health, job creation, and good governance. Creating a business environment that encourages private sector growth, vocational training aligning with industry needs, and social protection for vulnerable workers helps convert potential into actual economic gains.

Cross-cutting and rights-based approach
Policies should respect human rights and avoid coercion. Gender-equitable policies that enhance women’s choices, safety and economic opportunities have far-reaching demographic and social benefits. Multi-sectoral coordination is crucial because age-sex composition affects many policy areas simultaneously.

Learning from real cases
Case studies show how different countries and regions have faced age and sex composition challenges. Successful examples highlight integrated investments in education and jobs to catch a demographic dividend; cautionary cases show how neglect of youth employment led to social problems. Examining comparative lessons helps design context-sensitive policies.

Examples of successful transitions
Some East Asian countries combined rapid fertility decline with large investments in education, health and export-led industrialisation, resulting in decades of high growth. These cases underscore the role of governance, human capital and private sector development in converting demographic opportunity into growth.

Cases of skewed sex ratios
Countries that experienced declining child sex ratios used legal restrictions on sex determination, awareness campaigns, and incentives cautiously. Where social norms changed slowly, long-term investment in girls’ education and economic opportunities produced more durable improvements.

Adaptation and local solutions
Urban slums, rural ageing districts and migrant-receiving cities require tailored responses. For example, local vocational centres matched to city industries reduced youth unemployment in one region, while mobile health teams improved elderly care in remote districts. These examples show the need for local data and community engagement.

Comparative analysis for students
Students should compare outcomes across case studies to identify which policies led to desired results and why. Consider institutional capacity, timing of interventions, cultural context, and economic structure. Critical comparison helps formulate feasible recommendations for different demographic situations.