Population as a system

A human population can be viewed as a storage within a system. Births add newly born people from inside the population; immigration adds people who arrive from elsewhere. Both are population inputs.

The Crude birth rate measures the number of live births per 1,000 people in a population per year. It is described as “crude” because it compares births with the whole population, not just the people capable of giving birth. As a result, differences in age structure can affect comparisons.

The Immigration rate is the number of immigrants per 1,000 population per year. An immigrant is someone who enters an area intending to live there for the long term.

These rates can be calculated for a neighbourhood, town, country or region. At the global scale, births still count as an input, but immigration cannot alter the total number of humans because movement between countries occurs within the global system. Always check the boundary of the population being studied.

Population outputs

Mortality removes people through death; emigration removes those who leave the defined area. Both are population outputs.

The Crude death rate measures the number of deaths per 1,000 people in a population per year. As with crude birth rate, population structure affects it. For example, an ageing country may have good healthcare and long life expectancy but still record a relatively high crude death rate because so many residents belong to older age groups.

The Emigration rate is the number of emigrants per 1,000 population per year. An emigrant is someone who leaves an area to live elsewhere for the long term. That person counts as an emigrant from the origin and an immigrant to the destination.

Output rates can be compared across different scales, from towns to regions and nations. A local population may decline through emigration even when births exceed deaths. At the global scale, under normal demographic accounting, there is no emigration from the human population.

Demographic indicators

Total fertility rate is the average number of births per woman of childbearing age. It measures fertility directly, unlike crude birth rate, which divides births by the entire population.

Life expectancy is the average number of years a person can be expected to live, usually from birth, if demographic factors remain unchanged. This is a population average, not a prediction of a named person’s lifespan. A high life expectancy alone doesn’t prove that quality of life is high.

Doubling time is the number of years a population would take to double in size at its current growth rate. It can be estimated using the rule of 70: divide 70 by the growth rate, expressed as a percentage. This calculation assumes that the current percentage growth rate continues, though that rarely happens across an entire doubling period.

Natural increase is birth rate minus death rate, shown either as a number per 1,000 or as a percentage. To get the percentage, divide the difference between the birth rate and death rate by 10. Migration is excluded—the small word “natural” matters here.

Calculating natural increase

In rate-per-1,000 form:

$$r_n=b-d$$

Here, $r_n$ is the natural increase rate (people per 1,000 population per year), $b$ is the crude birth rate (births per 1,000 population per year) and $d$ is the crude death rate (deaths per 1,000 population per year).

To express it as a percentage:

$$p_n=\frac{b-d}{10}$$

In this equation, $p_n$ is the natural increase rate (% per year). Dividing by 10 converts a rate per 1,000 into a rate per 100. When deaths exceed births, the result is negative and shows natural decrease.

For example, suppose a population records 24 births and 9 deaths per 1,000 people each year. In that case, $r_n=15$ people per 1,000 per year and $p_n=1.5\%$ per year.

When total population growth is needed, migration can be included: net migration equals immigration minus emigration. As a result, a population can grow despite natural decrease, or decline despite natural increase.

Applying the rule of 70

$$T_d=\frac{70}{g}$$

Here, $T_d$ is doubling time (years) and $g$ is the annual population growth rate (% per year). At $g=1.5\%$ per year, $T_d\approx46.7$ years. Don’t insert a rate per 1,000 directly; convert it to a percentage first.

The rule gives an approximation based on exponential growth. It cannot be used for a zero or negative growth rate, and the answer becomes unreliable if fertility, mortality or migration changes substantially.

The rapid growth curve

For most of human history, global population grew very slowly. Then food supply and sanitation improved, and medicine reduced mortality. Birth rates stayed high at first, so growth accelerated. The result was a steep, broadly J-shaped curve, although the global percentage growth rate has fallen since its twentieth-century peak.

Projections, not prophecies

A population projection is a conditional model output. It estimates a future population using assumptions about fertility, mortality and migration. UN models commonly give low-, medium- and high-fertility scenarios. Even small differences in assumed births per woman build up over generations, causing the lines to spread further apart over time.

Fertility is particularly uncertain. It responds to education, gender equality, child survival and urbanization, as well as income, access to family planning, cultural expectations and government policy. Pandemics, conflict, improvements in healthcare or environmental change can shift mortality too. Migration has a major effect on individual countries, but it doesn’t change global totals.

Models help governments plan housing, schools, healthcare and pensions because they make their assumptions explicit. However, they become less reliable as the projection period gets longer. Unexpected policies, technologies, crises or changes in behaviour may push the real population away from every scenario. A range is therefore more defensible than one exact figure when estimating future growth.

Direct intervention

A population policy is a government strategy designed to alter population size, structure or growth. Direct policies act on fertility or migration itself.

An anti-natalist policy is a population policy that seeks to reduce the birth rate or population growth. Governments may provide access to contraception and run public family-planning campaigns. They can also raise the legal marriage age or remove financial incentives for large families.

A pro-natalist policy is a population policy intended to increase the birth rate or population growth. Measures may include paid parental leave or subsidized childcare, as well as housing support and tax benefits for parents.

Migration policies work directly through entry visas, labour recruitment, border controls and residence rights. Refugee and asylum rules or assisted return may also be used. Governments might encourage immigration to fill labour shortages. Alternatively, they may restrict it in response to political pressure, demand on infrastructure or concerns about cultural integration.

Named examples

Bangladesh adopted a largely anti-natalist approach, using extensive community family-planning services, contraceptive provision and public communication. At the same time, female education increased and child mortality fell. Fertility declined substantially, though policy alone cannot take all the credit because wider development also changed family preferences.

France has a long history of pro-natalist measures, including family allowances, parental leave and subsidized childcare. Such economic and social support makes it less costly to combine employment with parenthood. It can support fertility, but housing costs and employment insecurity limit the effect. Personal preferences matter too.

Economics isn’t the only influence on the instruments governments choose. Cultural expectations about family size and religious positions on contraception shape both acceptance and effectiveness, as do social attitudes to gender and political views about individual rights. Coercive measures may change rates quickly, but they carry serious ethical costs and unequal impacts. They can also create long-term distortions in age or sex structure.

Policies that change demographic decisions indirectly

Indirect policies focus on education, health or welfare rather than specific fertility or migration targets. Even so, they shape the circumstances in which people decide whether to have children, survive to old age or move.

Greater gender equality and female education often delay marriage and first childbirth. They can improve access to paid work and contraception while raising the opportunity cost of leaving employment, so fertility commonly falls. Better maternal healthcare, vaccination, clean water and nutrition reduce death rates. As parents become more confident that their children will survive, desired family size may also fall. Pensions make older people less reliant on their adult children for support, while regional investment may reduce economically driven out-migration.

None of these relationships is automatic. Education may increase migration by giving people the qualifications and resources needed to move. Improved healthcare can initially accelerate population growth because death rates fall before birth rates do.

Two named examples

Kerala, India: sustained investment in female literacy, primary healthcare and maternal services helped produce high life expectancy and low fertility compared with many places at a similar income level. Rather than directly imposing a family size, the policies changed health, security and women’s choices.

Costa Rica: expanded public healthcare, vaccination, sanitation and social welfare reduced mortality and increased life expectancy. Alongside education and urban development, these improvements contributed to declining fertility. Economic opportunities and relative political stability also influenced immigration and internal migration.

Purpose is the key distinction. A contraceptive quota directly targets births, whereas schools or clinics primarily pursue development and welfare but still have demographic consequences.

Reading an age–sex pyramid

Population composition describes how people are distributed across demographic categories such as age and sex. An age–sex pyramid uses paired horizontal bars to show the proportion of either gender in each age group. The values can be given as absolute numbers or as percentages of the total population.

Age groups are arranged vertically, with the youngest at the base and the oldest at the top. By convention, one sex appears on the left and the other on the right. Always check the axis before comparing pyramids. Absolute numbers show population size; percentages show composition and allow a fairer comparison between countries with different population sizes.

The shape can reveal demographic patterns:

• a broad base suggests many young children and usually recent high fertility;
• a narrowing base suggests falling fertility;
• a large elderly share points to long survival and an ageing population;
• a bulge may show a baby boom or age-selective immigration;
• an indentation may result from war, epidemic, emigration or a past fall in births;
• sex imbalance in particular working-age groups may suggest strongly sex-selective labour migration.

Treat these patterns as evidence rather than certainty. A wide working-age section may have several causes, so use birth, death and migration data alongside the pyramid before drawing a conclusion.

Five stages of demographic change

The demographic transition model (DTM) tracks how birth and death rates change as a human population moves through different stages of development over time. It models rates and the population changes they produce; it is not a timetable that every country must follow.

1. Stage 1: birth and death rates are high, and both fluctuate. Natural increase is small, leaving population size broadly stable.
2. Stage 2: the death rate drops while the birth rate remains high. Mortality may fall because of improved food supply, clean water, sanitation and healthcare. A large gap between births and deaths then drives rapid, approximately exponential population increase.
3. Stage 3: the birth rate falls, often alongside urbanization, education, contraception, lower child mortality and changing roles for women. The death rate stays low. Population continues to grow, but at a slower rate.
4. Stage 4: birth and death rates are both low. With little natural increase, population size tends to stabilize, although migration may still cause change.
5. Stage 5: the birth rate may drop below the death rate. Without sufficient immigration, the population ages and may decline.

Linking pyramids to the model

A Stage 1 pyramid has a broad base but narrows quickly because fertility and mortality are both high. Stage 2 produces a very broad base and a strongly triangular profile. In Stage 3, many young people remain, though the base begins to narrow. A Stage 4 pyramid is more column-shaped. Stage 5 has a narrow base with a comparatively wide upper section.

The model simplifies reality. It was developed mainly from the historical experience of industrializing countries and does not explicitly include migration. On its own, it cannot explain sudden shocks, government intervention or countries where fertility falls before they become wealthy.

Current projections

In 2024, the global population stood at about 8.2 billion. The UN’s 2024 medium projection puts it at roughly 10.2 billion around 2075, about 50 years later. It is expected to peak near 10.3 billion in the mid-2080s before reaching approximately 10.2 billion in 2100. Estimates a full century ahead, around 2125, are much less secure. Extending the medium trend suggests a population near 10 billion rather than continued rapid exponential growth.

These figures are conditional. The trajectory could shift because of higher or lower fertility, changes in life expectancy, pandemics, conflict, public policy, education, gender equality and environmental disruption. Even a small sustained difference in total fertility rate creates a large long-term difference because the effect carries through successive generations.

Population, consumption and biocapacity

Biocapacity is the capacity of biologically productive land and water to generate renewable resources and absorb wastes under prevailing management and technology. A biocapacity disparity arises when demand for ecological resources is distributed differently from the ecosystems capable of supplying them. It can also occur when a population’s demand exceeds locally or globally available capacity.

As populations grow, demand for food, freshwater, energy, land and materials can rise, along with waste and emissions. Headcount alone, though, doesn’t determine environmental pressure. Consumption per person, technology and distribution also matter: a smaller, wealthy, high-consuming population may exert more pressure than a larger low-consuming one.

Doughnut economics

The doughnut economics model is a sustainability framework that identifies a safe and just operating space between a social foundation and an ecological ceiling. Its inner boundary represents minimum social needs such as food, water, health, education, equity and political voice. Falling inside this boundary means human deprivation. The outer boundary represents planetary limits; crossing it means ecological overshoot.

Rapid population growth can make housing, sanitation, education and healthcare harder to provide, leaving people below the social foundation. At the same time, meeting these needs through resource-intensive production can increase climate change, biodiversity loss, nutrient disruption and other pressures beyond planetary boundaries. The aim isn’t simply to minimize population. It is to satisfy human needs while keeping total resource use within Earth’s regenerative and absorptive capacities.

Dependency ratio

Dependency ratio describes the relationship between the number of people classed as dependent and the number classed as economically productive. For this demographic measure, dependants are people under 15 years and over 64 years. The economically productive group covers those aged 15–64 years.

A youthful population with high fertility has many child dependants. At the other extreme, a very low-fertility, ageing population may have fewer children but many older dependants. Dependency can therefore be high at both fertility extremes.

The ratio helps with planning, though age is only a proxy for economic activity. Some people under 15 or over 64 work, while many aged 15–64 do not. Unpaid carers also make economically valuable contributions. So, age-dependency ratio is a more accurate description than a literal worker-to-non-worker count.

Population momentum

Population momentum explains why a population continues to grow even when the fertility rate declines. Growth depends on the number of women of reproductive age as well as the number of children per woman.

A broad-based pyramid contains a large cohort of children. Once this group reaches reproductive age, the sheer number of potential parents can produce many births in total, even if each woman has fewer children than women in the previous generation. Growth slows only after the youthful age structure has worked its way through the population. An ageing population can show the reverse effect: fertility may rise slightly, but births remain few because the reproductive-age cohort is small.

Nigeria: a youthful, rapidly growing population

Around 1995, Nigeria had high fertility, falling mortality and a population structure with a very broad base. This placed it broadly in Stage 2 of the DTM. Better child survival widened the gap between birth and death rates. Large families were also supported by the country’s large rural population, limited pensions and the economic value of children.

By the mid-2020s, mortality had fallen further and fertility had declined, though it was still high. Nigeria was moving unevenly through Stage 3. Its population continued to grow because of natural increase and strong population momentum. Urbanization, female education and access to contraception encouraged smaller families, while poverty, unequal healthcare, insecurity and regional differences slowed the transition.

Projections for about 2055 suggest a much larger population, still with a substantial share of young and working-age people. The base should narrow if fertility keeps falling. A demographic dividend could follow if education and employment expand. Without that expansion, pressure on housing, services and ecosystems may intensify.

Colonial boundaries, post-independence instability and uneven public-service provision are among the historical and political influences. Cultural and religious expectations differ greatly between regions and communities, so one national explanation doesn’t fit. Agricultural livelihoods and weak old-age security can make larger families economically favourable. Urbanization and girls’ education, by contrast, tend to reduce fertility.

Germany: an ageing, slowly changing population

Around 1995, Germany already had low fertility and low mortality, and its population pyramid had a relatively narrow base. It was broadly in Stage 4 and moving toward Stage 5. Reunification and migration changed regional structures. At the same time, delayed parenthood and changing household patterns kept fertility below replacement level.

By the mid-2020s, Germany had an ageing population with a large share of older adults. Natural decrease occurred in many years. Immigration partly offset the excess of deaths over births and brought in working-age adults, so fertility and mortality alone can’t explain the country’s population structure.

By about 2055, the elderly share is projected to remain large as substantial cohorts enter retirement. Population size will depend heavily on immigration, fertility and longevity. Labour shortages and pension costs are creating pressure for childcare support, later retirement and managed immigration.

War, division and reunification left historical effects on both cohorts and regions. High housing and childcare costs can discourage births, while strong welfare provision reduces the need to rely on children for old-age security. Social and cultural acceptance of smaller families and delayed parenthood also supports low fertility. Religious influence on family size is generally weaker than it was in earlier periods. Political decisions about family support and migration will strongly shape the future.

The DTM provides a starting framework, not a complete cause-and-effect explanation. Nigeria’s youthful structure creates momentum even as fertility declines. Germany’s older structure produces natural decrease, which migration can offset. Historical, cultural, religious, economic, social and political factors interact, and their effects may vary within each country.

Environmental drivers

Environmental migration refers to the long-term or permanent movement of people where environmental change is an important cause. Movement may take place within a country or across an international border. Environmental pressure usually combines with employment, poverty, conflict, family networks and government policy.

Floods, drought emergencies, forest fires and intensified storms are sudden-onset drivers. They can trigger rapid evacuation and displacement. By contrast, slow-onset drivers such as land degradation, desertification, sea-level rise and saltwater inundation develop gradually. Over time, they weaken livelihoods, food security, freshwater supply and habitability. The resulting movement may look economic, even when environmental deterioration is a fundamental cause.

Named example: the Mekong Delta, Vietnam

In low-lying parts of Vietnam’s Mekong Delta, communities face riverbank and coastal erosion, saline intrusion, land subsidence and changing flood and drought patterns. Saltwater harms crops and freshwater supplies. Erosion and unstable yields also make farming and fishing livelihoods less secure. Some households adapt by changing crops or finding seasonal work. Others move towards cities such as Ho Chi Minh City or to industrial areas elsewhere in Vietnam.

Most of this migration is internal, and it has several causes. Environmental stress rarely acts by itself. Income, land ownership, employment opportunities and family connections affect who can move, where they go and whether the move is temporary or permanent. Some poorer households may become trapped because relocation costs money.

Testing a demographic hypothesis with secondary data

One suitable investigation could test: “Countries with higher female secondary-school enrolment tend to have lower total fertility rates.” Collect data for the same year and countries from sources such as Gapminder, the World Bank or Our World in Data. Before starting the analysis, record the definitions, units and missing values. Indicators with similar names aren’t always measured in the same way.

Draw a scatter graph, placing the socio-economic indicator on the horizontal axis and the demographic factor on the vertical axis. Spearman’s rank correlation is suitable if the variables are continuous and the relationship appears monotonic but is not clearly linear or normally distributed. After entering the paired data, a statistics tool such as the Social Science Statistics calculator can calculate the coefficient and significance.

Interpret both the direction and strength of the association, then compare the significance result with the chosen threshold. Look at the outliers and limitations as well. Correlation does not establish causation: income, healthcare, urbanization, cultural expectations and policy may affect both education and fertility. National averages can hide regional and social inequalities. Missing observations or data from different dates may also bias the result.