Global population: distribution, density, growth trends, components of change

Population & Migration – Cambridge IGCSE/A‑Level (9696)

Learning Objective

Develop a detailed understanding of the spatial patterns and dynamics of the world’s population, the structure of societies, the policies that influence natural increase, and the causes, constraints and impacts of migration. Use this knowledge to analyse data, interpret maps/diagrams and evaluate the usefulness of key models (AO1‑AO3).

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1. Global Population

1.1 Key Definitions

• Population distribution – where people live on the Earth’s surface.
• Population density – number of people per unit land area (people km⁻²).
• Population growth – change in the number of people over a given period.
• Components of change – natural increase (births – deaths) and net migration (immigration – emigration).

1.2 Current Global Distribution (2024)

Region	Population (millions)	% of World Population
Asia	4,800	60 %
Africa	1,450	18 %
Europe	750	9 %
Latin America & Caribbean	660	8 %
North America	370	5 %
Oceania	45	0.5 %

1.3 Why People Cluster Where They Do – Drivers of Distribution

These drivers explain the patterns shown in the table above and are the basis of the syllabus “Drivers of distribution” sub‑point.

• Physical environment – climate (temperate zones are favoured), water availability (river valleys, coastal plains), fertile soils and gentle topography reduce agricultural costs.
• Economic factors – proximity to markets, industry, services and major transport corridors (ports, railways, highways) creates employment opportunities.
• Historical & cultural legacy – colonial settlement patterns, historic trade routes, religious centres and ancient river‑valley civilizations.
• Political & policy influences – national borders, security zones, development incentives (e.g., special economic zones), and land‑use planning.
• Scale of analysis – patterns differ when examined globally (continents), regionally (South‑Asia) or locally (city‑region).

1.4 Visual Summary (World Density Schematic)

1.5 Typical Density Gradients

• Coastal‑to‑interior – high density along coasts (Japan, Bangladesh) falling sharply inland.
• River‑valley – dense settlement along major rivers (Nile, Ganges, Yangtze).
• Urban‑rural – metropolitan cores >10 000 people km⁻² versus surrounding countryside < 100 people km⁻².

1.6 Illustrative Density Table (2024)

Country / Region	Land Area (km²)	Population (millions)	Density (people km⁻²)
Bangladesh	147,570	176	1,193
India	2,973,190	1,420	478
United States	9,147,593	335	37
Australia	7,692,024	26	3
Russia	16,377,742	144	9

Formula: Density = Total Population ÷ Land Area (km²)

1.7 Global Growth Trends

World population grew from ~1 billion in 1800 to >8 billion in 2024. The annual growth rate is now slowing.

Annual growth rate (r) is calculated as:

$$r = \frac{P_{t} - P_{0}}{P_{0}} \times 100\%$$

Decade	World Population (billions)	Average Annual Growth Rate (%)
1950s	2.5	1.9
1960s	3.0	2.0
1970s	3.7	2.1
1980s	4.4	1.9
1990s	5.3	1.7
2000s	6.1	1.4
2010s	7.0	1.2
2020s (proj.)	8.0	0.9

1.8 Regional Growth Variations

• Sub‑Saharan Africa – highest current growth (≈2.5 % yr⁻¹); youthful age structure fuels future increase.
• South‑Asia – large absolute increase, but growth rate falling (≈1.0 % yr⁻¹) as fertility declines.
• Europe & East‑Asia – low or negative growth; ageing populations dominate.
• Latin America – moderate growth; urbanisation > 80 %.

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2. Population Structure

2.1 Age‑Sex Pyramids

Insert labelled diagrams for the two contrasting cases (use textbook or exam‑style figures).

• High‑growth country – Niger (2022) – broad base, narrow top, high youth dependency.
• Low‑growth country – Japan (2022) – constricted base, wide top, high old‑age dependency.

2.2 Dependency Ratios

Dependency ratio = (Population < 15 + Population > 64) ÷ Population 15‑64 × 100.

Country	Young‑dep. %	Old‑dep. %	Total Dep. %
Niger	55	5	60
Germany	15	28	43
India	28	8	36
United Kingdom	18	19	37

The ratio links directly to the Demographic Transition Model: high youth‑dependency characterises Stage 2, while high old‑dependency typifies Stage 5.

2.3 Demographic Transition Model (DTM)

Stage	Crude Birth Rate (CBR) per 1,000	Crude Death Rate (CDR) per 1,000	Population Growth	Typical Countries (examples)
1 – Pre‑industrial	>30	>30	Low	None today
2 – Early‑expansion	30‑40	10‑20	High	Afghanistan, Yemen
3 – Late‑expansion	20‑30	10‑15	Moderate	India, Brazil
4 – Low‑fluctuation	10‑20	8‑12	Low	China, Mexico
5 – Declining	<10	<8	Zero/Negative	Japan, Germany

2.4 Evaluation of the DTM

1. Strengths
   • Clear link between fertility, mortality and economic development.
   • Provides a framework for predicting future growth patterns.
   • Useful for AO1 (knowledge) and AO2 (interpretation of pyramids).
2. Limitations
   • Assumes a linear, unidirectional path – many countries “skip” stages (e.g., rapid fertility decline without a prolonged Stage 3).
   • Ignores migration, which can dramatically reshape size and structure.
   • Based on historic European experience; cultural, religious and policy differences mean it does not fit all contexts.
   • Fails to capture sub‑national variation (urban vs. rural) and the impact of government policies.
   • Non‑European contexts: Sub‑Saharan Africa often shows high CBR but also relatively high CDR because of disease, giving a flatter growth curve than the model predicts; East‑Asian economies (e.g., South Korea) moved quickly to Stage 5 due to intensive family‑planning and education, bypassing a classic Stage 3 plateau.
3. Relevance to the syllabus – AO1 (knowledge of the model), AO2 (interpretation of pyramids and ratios), AO3 (critical evaluation).

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3. Government Attempts to Manage Natural Increase

3.1 Policy Types

• Pro‑natal policies – tax incentives, child‑bearing allowances, subsidised childcare (e.g., France, Hungary).
• Anti‑natal policies – legal limits, financial penalties, promotion of smaller families (e.g., China’s One‑Child Policy, Iran’s 1990s family‑planning programme).
• Comprehensive reproductive‑health programmes – free contraception, sex education, women’s empowerment (e.g., Bangladesh, Thailand).

3.2 Comparative Outcomes

Country	Policy (Year)	Target TFR	Latest TFR (2022‑23)	Income Group	Key Success / Failure Factors	Evaluation (AO3)
China	1979 – One‑Child (relaxed 2015, two‑child 2021)	1.5	1.7 (2023)	Upper‑middle	Strong enforcement, penalties; urban‑centric incentives; later policy rigidity.	Rapid fertility fall, but now severe ageing and gender imbalance; coercive measures raise ethical concerns.
Iran	1989 – Voluntary family‑planning	2.1	1.7 (2022)	Upper‑middle	Extensive health‑service network; religious endorsement; media campaigns.	Effective short‑term decline; recent reversal to encourage higher fertility shows limited long‑term sustainability.
Bangladesh	1970s‑1990s – Integrated health & education programmes	2.5	2.0 (2022)	Low	Female‑education expansion; community health workers; affordable contraceptives.	Gradual decline linked to empowerment; still faces high youth dependency.
Sweden	1970s‑present – Generous parental leave, child‑care subsidies	2.1	1.8 (2022)	High	Universal childcare, gender‑equal parental leave, strong welfare state.	Improves work‑life balance but fertility remains below replacement; cultural preferences for small families persist.
Kenya	2000s – Family‑planning vouchers & community outreach	2.5	3.4 (2022)	Low	Voucher scheme increased access; however, low literacy, religious opposition, and rural‑area service gaps limit uptake.	Shows that financial incentives alone are insufficient without broader socio‑cultural change.

3.3 Critical Evaluation (AO3)

• Economic incentives work best when paired with education and health services.
• Coercive approaches achieve rapid results but generate human‑rights issues and long‑term demographic imbalances.
• Socio‑cultural factors (religion, gender norms) can blunt policy impact – e.g., high fertility persists in parts of Sub‑Saharan Africa despite free contraception.
• Female empowerment and urbanisation naturally lower fertility; policies that invest in schooling and employment for women tend to be the most sustainable.

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4. Migration

4.1 Classification of Migration

Type	Voluntary / Forced	Typical Duration	Examples
Economic (labour) migration	Voluntary	Temporary or permanent	Mexican workers in the USA; Indian IT professionals in the UK
Educational migration	Voluntary	Temporary (study) → possible settlement	Chinese students in Australia
Family reunification	Voluntary	Permanent	Filipino nurses moving to Canada
Refugee / asylum‑seeker movement	Forced	Usually permanent (if resettled)	Syrian refugees to Europe; Rohingya to Bangladesh
Internal rural‑to‑urban migration	Voluntary (often economic)	Permanent	People moving from Uttar Pradesh to Delhi

4.2 Push‑Pull Framework (Expanded)

• Economic – job opportunities, higher wages, better living standards.
• Social – education, family ties, lifestyle aspirations.
• Political – stability, democracy, freedom from persecution, visa policies.
• Environmental – climate change, natural disasters, resource scarcity.
• Cultural / Religious – language, religion, diaspora networks.
• Security – safety from conflict or crime.

4.3 Detailed Specific Examples

Example 1 – Rural‑to‑Urban Migration in Ethiopia (Low‑income Country)

• Causes (push): limited agricultural income, land fragmentation, recurrent drought, low rural services.
• Pull factors: expanding manufacturing in Addis Ababa, higher wages, better education and health facilities.
• Scale & pattern: 2015‑2020 saw an average annual net rural‑to‑urban flow of ~1.2 million people, concentrated along the Addis Ababa–Dire Dawa corridor.
• Impacts on source: labour shortages in agriculture, ageing rural populations, remittance inflows.
• Impacts on destination: rapid urban growth, informal settlements, pressure on housing, water, and sanitation services; increased demand for low‑skill jobs.
• Evaluation of management policies:
  • Government’s “Growth and Transformation Plan” (2010‑2015) promoted industrial parks and urban infrastructure – partially successful in creating jobs.
  • Limited rural development (e.g., irrigation, extension services) means push factors persist.
  • Urban planning lagged, leading to sprawling slums and inadequate service provision.

Example 2 – Syrian Refugee Crisis to Europe (High‑income Destination)

• Causes (push): civil war (2011‑), widespread destruction, persecution, lack of basic services.
• Pull factors: safety, asylum rights, higher living standards, existing Syrian diaspora in Germany, Sweden, and the UK.
• Scale & pattern: By 2022, >5 million Syrian refugees were registered in Europe; main entry points were Greece and Italy, followed by onward movement to Germany and Sweden.
• Impacts on source (Syria): “Brain drain” of professionals, demographic shift toward younger, displaced populations.
• Impacts on destination:
  • Short‑term pressure on housing, health, and education services in reception areas.
  • Long‑term contributions to the labour market (especially in care and construction sectors).
  • Political debate over integration, rise of anti‑immigration parties.
• Evaluation of management policies:
  • EU‑Turkey Statement (2016) reduced arrivals via the Aegean Sea but created “externalisation” of borders and raised humanitarian concerns.
  • National resettlement programmes (Germany’s “Integration Act”) facilitated language training and labour market entry – praised for successful integration of many refugees.
  • Inconsistent asylum procedures across EU states led to “asylum shopping” and uneven burden sharing.

4.4 Impacts of Migration

• Source country – loss of labour (especially skilled), remittance inflows, possible demographic ageing.
• Destination country – labour‑market supplementation, cultural diversity, pressure on public services, potential social tension.
• Migrants themselves – improved income and opportunities, but risk of exploitation, discrimination, and loss of social networks.

4.5 Management of Migration (Policy Tools)

• Border control & visa regimes (e.g., points‑based systems, biometric checks).
• Refugee‑status determination and resettlement programmes (UNHCR‑led, EU‑wide frameworks).
• Labour migration schemes (seasonal worker visas, skilled‑worker quotas).
• Development‑assistance programmes aimed at reducing push factors (e.g., climate‑adaptation projects in Sahel).
• Integration policies – language courses, recognition of qualifications, anti‑discrimination legislation.

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5. Summary for Exam Preparation (AO1‑AO3)

• AO1 – Memorise key definitions, the DTM stages, major policy examples, and the push‑pull categories.
• AO2 – Practice interpreting world density maps, age‑sex pyramids, and migration flow diagrams; be able to calculate density and dependency ratios.
• AO3 – Develop balanced arguments: strengths/limitations of the DTM, effectiveness of population‑control policies, and the trade‑offs in migration management.