3 Demographic Transition and Population Change

How the transition works

The Demographic transition model (DTM) describes a broad historical shift from high birth and death rates to low birth and death rates. Death rates generally decline before birth rates do. During the interval between these declines, births greatly outnumber deaths, so population grows rapidly. Later, as fertility falls, growth slows and may turn into decline.

The model was developed from Western European experience, especially that of England and Wales. It is a simplified framework for describing broad patterns, not a universal timetable or a precise forecasting tool.

Accounting for population change

Natural increase is the difference between births and deaths. Population change also reflects movement into and out of a country and the effects of its age structure. A useful accounting relationship is:

Net migration is immigration minus emigration. The relationship shows why total population change can differ from natural increase. Population momentum—the continued growth or decline associated with age structure—also affects population trajectories, though it is not a separate term in this accounting equation.

Population size, population growth, density, and spatial distribution are related but distinct. Arithmetic density is population divided by land area; it is not a measure of the rate of population growth.

The stages and their population effects

The model is commonly presented in four stages, with a fifth stage sometimes added. The stages are simplified descriptions, and their boundaries are approximate.

• Stage 1: Pre-transition. Birth and death rates are both high and fluctuate. Growth is low or uneven because high births are offset by high deaths. This pattern describes many societies historically, not a typical present-day national situation.

• Stage 2: Early transition. Death rates fall while birth rates remain high. Improved food supply, sanitation, and health care can reduce mortality, especially among children. The widening gap between births and deaths produces rapid natural increase.

• Stage 3: Later transition. Birth rates begin to fall while death rates are relatively low. Fertility decline may be associated with urbanization, education, changing family choices, access to contraception, and lower child mortality. Growth slows, although the population may continue to increase substantially.

• Stage 4: Low-growth stage. Birth and death rates are both low. Natural increase is small, so populations may be stable or grow slowly. An older age structure becomes more common.

• Stage 5: Optional extension. Birth rates remain below death rates for a sustained period. Natural decrease and population ageing may occur. Immigration or population momentum can moderate or delay overall decline.

The timing of falling mortality and falling fertility matters. If both declined at the same time, the rapid-growth interval would be much smaller. The stages help explain why population growth can temporarily accelerate even as health and survival improve. The fifth stage describes some low-fertility patterns, but it is not universally accepted as a necessary part of the model.

What the model explains and what it misses

The Demographic transition model (DTM) is most useful as a descriptive framework for broad trends. It is often presented as assuming that countries tend to move through stages in sequence from high rates to low rates; that social and economic change, often associated with industrialization and urbanization, accompanies falling mortality and fertility; and that mortality usually declines before fertility, creating a period of rapid growth. It also assumes that national birth and death rates capture the main pattern of population change.

These assumptions have limits. The model reflects a particular European historical experience, and countries may follow different routes, move at different speeds, or experience interruptions and reversals. It does not specify one universal cause or timetable for fertility decline. Conflict, epidemics, economic disruption, and public policy can alter population trends. Because the simple model omits migration and population momentum, total population change may differ from natural increase.

Stage labels can hide important differences: stage boundaries are simplified, and national averages can conceal substantial variation among regions, social groups, and cities. The model does not explain where people live within a country or predict population density. A densely populated country may be shrinking while a less densely populated country grows quickly. The fifth stage is a useful description of some low-fertility patterns, not a settled prediction that every country will follow.

Different countries, different trajectories

Countries can be at different points in the broad transition at the same time. United Nations World Population Prospects estimates and projections illustrate that some populations are still growing while others have already peaked. These figures are estimates and projections, not certainties.

• Nigeria: The United Nations projects growth from about $233$ million in $2024$ to $376$ million in $2054$, and $477$ million in $2100$. A younger age structure and continued natural increase can contribute to growth; momentum can sustain it even as fertility changes. These figures alone do not assign Nigeria a precise DTM stage.

• India: The projected population grows from about $1.45$ billion in $2024$ to $1.69$ billion in $2054$, followed by a lower projection of about $1.51$ billion in $2100$. A population can keep increasing for a time while fertility falls because large cohorts are entering or remain in reproductive ages.

• China: The United Nations identifies China among countries whose populations had already peaked by $2024$. Its population is projected to fall from about $1.42$ billion in $2024$ to $1.22$ billion in $2054$. Low fertility and an ageing population can shift a country from growth toward decline.

• Germany: Germany is also among the countries whose populations had peaked by $2024$. The United Nations notes that immigration has helped delay or moderate decline in Germany and other low-fertility countries. Migration can therefore make total population change differ from what births and deaths alone suggest.

These contrasts are not simply a matter of high versus low density or of “developed” versus “developing” countries. They reflect different combinations of fertility, mortality, age structure, and migration. Population momentum is an important contributor to projected future change, while migration can substantially affect national outcomes in some countries.