Population Change and the Demographic Transition

This lesson examines how and why populations grow and decline over time. You will study the Demographic Transition Model (DTM), crude birth and death rates, natural increase, and population pyramids. These concepts are central to AQA A-Level Geography Paper 2, Section C — Population and the Environment.

Key Population Measures

Before analysing population change, it is essential to understand the measures used:

Measure	Definition	Formula
Crude Birth Rate (CBR)	Number of live births per 1,000 population per year	(Live births / Total population) × 1,000
Crude Death Rate (CDR)	Number of deaths per 1,000 population per year	(Deaths / Total population) × 1,000
Rate of Natural Increase (RNI)	Difference between CBR and CDR, expressed as a percentage	(CBR − CDR) / 10
Total Fertility Rate (TFR)	Average number of children born to a woman during her lifetime	Calculated from age-specific fertility rates
Infant Mortality Rate (IMR)	Number of deaths of infants under 1 year per 1,000 live births per year	(Infant deaths / Live births) × 1,000
Life Expectancy	Average number of years a newborn is expected to live	Derived from mortality tables

Key Definition: Replacement-level fertility is the TFR at which a population exactly replaces itself from one generation to the next — approximately 2.1 in developed countries (slightly above 2 to account for mortality before reproductive age).

Global Population Growth

World population reached approximately 8 billion in November 2022 (UNFPA). Growth has not been steady — it took roughly 200,000 years for the global population to reach 1 billion (around 1804), but only 11 years to grow from 7 to 8 billion.

Key Milestones

Year	World Population	Time to add 1 billion
1804	1 billion	~200,000 years
1927	2 billion	123 years
1960	3 billion	33 years
1974	4 billion	14 years
1987	5 billion	13 years
1999	6 billion	12 years
2011	7 billion	12 years
2022	8 billion	11 years

The rate of growth is now slowing. The UN projects a peak population of approximately 10.4 billion by the 2080s before a gradual decline begins.

Exam Tip: When discussing population growth, always distinguish between the rate of growth (which has been declining since the late 1960s) and absolute growth (which continued to rise until recently). The peak annual growth rate was about 2.1% in 1968; by 2023 it was approximately 0.9%.

The Demographic Transition Model (DTM)

The Demographic Transition Model was first proposed by Warren Thompson (1929) and later developed by Frank Notestein (1945). It describes how populations transition from high birth and death rates to low birth and death rates as a country develops economically.

graph LR
    S1["Stage 1<br/>High Stationary<br/>High CBR, High CDR<br/>Low growth"] --> S2["Stage 2<br/>Early Expanding<br/>High CBR, Falling CDR<br/>Rapid growth"]
    S2 --> S3["Stage 3<br/>Late Expanding<br/>Falling CBR, Low CDR<br/>Slowing growth"]
    S3 --> S4["Stage 4<br/>Low Stationary<br/>Low CBR, Low CDR<br/>Stable/slow growth"]
    S4 --> S5["Stage 5<br/>Declining<br/>Very low CBR, Low CDR<br/>Natural decrease"]

DTM Stages in Detail

Stage	CBR	CDR	Natural Increase	Example Countries	Characteristics
1: High Stationary	High (35-50)	High (35-50)	Low/Zero	No countries today; pre-industrial societies	No contraception, high infant mortality, disease, famine
2: Early Expanding	High (35-50)	Falling (15-35)	Rapid growth	Afghanistan, Niger, Chad	Improved sanitation, medicine, food supply; cultural lag in fertility decline
3: Late Expanding	Falling (15-35)	Low (10-15)	Moderate growth	India, Brazil, Mexico	Urbanisation, education (especially female), access to contraception
4: Low Stationary	Low (10-15)	Low (10-15)	Low/Zero	UK, France, USA, Australia	Post-industrial economy, high cost of child-rearing, career priorities
5: Declining	Very low (<10)	Low (10-15)	Negative	Japan, Germany, Italy, South Korea	Ageing population, sub-replacement fertility, pension crises

Factors Driving the Transition

Falling Death Rates (Stage 2):

Improved public health and sanitation (clean water, sewage systems)
Medical advances (vaccination, antibiotics — e.g., penicillin from 1940s)
Agricultural revolution increasing food security
Improved nutrition and hygiene

Falling Birth Rates (Stage 3):

Urbanisation reduces the economic value of children (no longer needed as farm labour)
Female education and empowerment — research by Lutz and KC (2011) found this is the single strongest predictor of fertility decline
Access to reliable contraception
Declining infant mortality (parents no longer need to "over-produce" children as insurance against child death)
Rising cost of raising children in industrialised societies

Exam Tip: The DTM is a descriptive model, not a predictive one. It was based on the historical experience of Western Europe. Many LICs may not follow the same trajectory — for example, some countries have rapidly urbanised without industrialising (e.g., sub-Saharan Africa). Always evaluate the model's limitations.

Criticisms of the DTM

Eurocentrism — the model is based on the experience of Western European countries during the 18th–20th centuries and may not apply universally.
No time scale — the model does not specify how long each stage lasts. The UK took over 200 years; South Korea completed the transition in under 60 years.
Migration is ignored — the model only considers natural change (births and deaths), yet migration significantly affects population size and structure.
Assumes development follows a single path — some countries may stall in Stage 2 or 3 due to conflict, disease (e.g., HIV/AIDS in southern Africa), or political instability.
Stage 5 is contested — Thompson's original model had only four stages. The fifth stage was added later to account for below-replacement fertility in countries like Japan and Germany.

Population Pyramids

Population pyramids (age-sex structure diagrams) provide a visual snapshot of a population's structure at a given point in time. They display age groups on the vertical axis and population (often in thousands or millions, split by sex) on the horizontal axis.

Interpreting Pyramid Shapes

Shape	DTM Stage	Characteristics	Example
Wide base, narrow top (triangular)	Stage 2	High CBR, high CDR, low life expectancy, large young population	Niger (median age 15.4)
Narrowing base, bulge in middle	Stage 3	Falling CBR, improving life expectancy, growing working-age population	India (median age 28.7)
Column/barrel shape	Stage 4	Low CBR, low CDR, roughly even age distribution	UK (median age 40.7)
Inverted triangle / top-heavy	Stage 5	Very low CBR, ageing population, shrinking workforce	Japan (median age 48.6)

Case Study: Japan — Stage 5

Japan illustrates the challenges of Stage 5 of the DTM:

TFR of 1.20 (2023), far below replacement level of 2.1
Population peaked at 128.1 million in 2010 and had fallen to approximately 124 million by 2024
Over 29% of the population is aged 65+, the highest proportion of any country
The old-age dependency ratio places enormous pressure on the working-age population to fund pensions and healthcare
The Japanese government has introduced pro-natalist policies including childcare subsidies, parental leave, and housing support, but fertility has continued to decline

Exam Tip: When using case studies in essays, always include specific data (dates, percentages, population figures). A well-evidenced case study demonstrates AO2 (application) skills and can significantly improve your mark.

Pro-natalist and Anti-natalist Policies

Governments intervene in population change through policies designed to increase or decrease fertility:

Policy Type	Examples	Case Study
Pro-natalist	Childcare subsidies, parental leave, tax benefits for families, baby bonuses	France: generous childcare provision, three-year parental leave; TFR of 1.80 (2022), among the highest in Europe
Anti-natalist	Family planning programmes, education campaigns, economic incentives for smaller families	China: One-Child Policy (1979–2015) — reduced TFR from 5.9 (1970) to 1.6 (2000), but caused gender imbalance, forced abortions, ageing population; now reversed to three-child policy (2021)

Case Study: France's Pro-natalist Policies

France has long pursued pro-natalist policies and maintains one of the highest TFRs in Europe:

Generous parental leave — mothers receive 16 weeks of paid maternity leave for the first two children, rising to 26 weeks for the third child
Subsidised childcare — state-funded crèches and childminding services; families receive the allocations familiales (family allowance) for every child from the second onwards
Tax incentives — the quotient familial system reduces income tax for families with more children
Result: France's TFR was 1.80 in 2022, compared to the EU average of approximately 1.46

Evaluation Point: France's relatively high fertility is also attributable to cultural factors, higher rates of cohabitation, and immigration. It is difficult to isolate the effect of policy from these broader social trends.

Summary

Population change is measured through CBR, CDR, RNI, TFR, IMR, and life expectancy.
The Demographic Transition Model (Thompson 1929, Notestein 1945) describes the shift from high to low birth and death rates through five stages.
Population pyramids reveal the age-sex structure and can be linked to DTM stages.
The DTM has significant limitations: it is Eurocentric, ignores migration, and assumes a single development pathway.
Government policies (pro-natalist and anti-natalist) attempt to manage population change but have mixed results.
Japan and China provide contrasting case studies of the demographic challenges faced by countries at different points in the transition.