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Spec mapping (AQA 7037): Paper 2, §3.2.4 Population and the Environment — population structure (age and sex) and its variation over time and space; the implications of an ageing population; the causes, patterns and consequences of migration as a component of population change. This lesson builds directly on the DTM and population-pyramid work of the previous lesson and supplies the second engine of population change (after natural increase): migration. It links synoptically to §3.2.1 Global Systems and Global Governance (international migration is a defining flow of globalisation, governed by states, the UN and supranational bodies) and to §3.2.3 Contemporary Urban Environments (rural–urban migration drives urbanisation and the growth of megacities). Assessment objectives: AO1 — knowledge of dependency ratios, ageing, and the migration theories of Ravenstein and Lee; AO2 — application to named, contrasting contexts (the UK since 2004, China's internal migration); AO3 — interpretation and evaluation of migration and structure data, including calculation of dependency ratios and critical judgement of the costs and benefits of migration.
This lesson explores how populations are structured by age and sex, the concept of dependency, and the causes, patterns and consequences of migration. You will study Ravenstein's Laws of Migration (1885) and Lee's Push–Pull Model (1966), alongside contemporary case studies from the UK and China.
The age–sex structure of a population describes the proportion of people in different age bands, divided by sex. It is the single most informative summary of a population's demographic state because it encodes the legacy of past births, deaths and migration and the potential for future change. It is conventionally represented by the population pyramid introduced in the previous lesson.
Why does structure matter so much? Because almost every economic, social and political consequence of population flows from the balance between producers and dependants, not from the headline total. Two countries with identical populations of 60 million but radically different age structures — one youthful (a wide-based pyramid), one ageing (a top-heavy pyramid) — face opposite policy problems: the first needs schools, maternal healthcare and millions of new jobs; the second needs pensions, geriatric care and immigration to staff its workforce.
Structure is also the link between the two engines of population change studied in this option. The previous lesson showed how the DTM drives structure through natural change: a Stage 2 country has a wide-based, youthful pyramid because of high fertility, while a Stage 5 country becomes top-heavy as fertility falls below replacement. Migration then reshapes that structure on top of natural change — and does so selectively, because migrants are overwhelmingly young working-age adults. Emigration therefore tends to age the source country (removing young adults and their future children), while immigration tends to rejuvenate the destination (adding young workers who also raise the birth rate). A full account of any country's age structure must combine both processes: Japan's extreme ageing reflects very low fertility and very low immigration, whereas the UK's slightly younger, more slowly ageing structure reflects sustained net immigration partially offsetting its own sub-replacement fertility. This is exactly why the DTM's neglect of migration (criticised in the previous lesson) matters so much in practice.
Key Definition: The dependency ratio measures the size of the economically dependent population (conventionally those aged 0–14 and 65+) relative to the working-age population (15–64). It is a crude but powerful indicator of the economic "load" each worker notionally carries.
| Measure | Formula | Interpretation |
|---|---|---|
| Total Dependency Ratio | ((population 0–14 + population 65+) / population 15–64) × 100 | Higher values mean more dependants per 100 workers |
| Youth Dependency Ratio | (population 0–14 / population 15–64) × 100 | High in Stage 2/3 DTM countries (Niger, Chad) |
| Old-Age Dependency Ratio | (population 65+ / population 15–64) × 100 | High in Stage 4/5 DTM countries (Japan, Italy) |
The total dependency ratio is the sum of the youth and old-age ratios. Its great weakness is that the 15–64 boundary is arbitrary: many 15–18 year-olds are in education (and so dependent), and rising numbers of over-65s remain in work, so the "true" economic dependency ratio differs from the demographic one. Candidates who note this limitation strengthen evaluative answers.
UK worked example (ONS, 2023 structure):
Using these shares, the total dependency ratio is:
Total DR=63.817.6+18.6×100≈57
i.e. roughly 57 dependants for every 100 people of working age. Notice that the youth and old-age components are now almost equal in the UK — a hallmark of a Stage 4 country in which a large elderly cohort has come to match the (shrinking) child cohort. By contrast, in Niger the youth ratio alone exceeds 100, because children outnumber the working-age population.
An ageing population occurs when the median age rises, driven by falling fertility (fewer young people entering at the bottom) and rising life expectancy (more people surviving at the top). This is the demographic destiny of every country that completes the DTM, and it creates a recognisable cluster of challenges:
Exam Tip: Ageing is not inherently a catastrophe. Older people contribute as consumers, as unpaid carers (grandparental childcare is worth billions to the UK economy), as volunteers, and as taxpayers — many work well beyond 65. The "old-age dependency ratio" labels them dependants, but many are net contributors. A balanced evaluation that resists the "ageing = burden" framing scores higher. The strongest answers also note that ageing is a consequence of success — longer, healthier lives — not a failure.
Key Definition: Migration is the permanent or semi-permanent movement of people from one place to another, involving a change in usual residence. It may be internal (within a country) or international (across a national border), and voluntary or forced.
| Type | Definition | Example |
|---|---|---|
| Immigration | Movement into a country | Polish workers moving to the UK after 2004 EU enlargement |
| Emigration | Movement out of a country | UK retirees moving to Spain's Costa del Sol |
| Net migration | Immigration minus emigration | UK net migration was about +685,000 in the year ending June 2023 (ONS) |
| Internal migration | Movement within a country | Rural-to-urban migration in India and China |
| Voluntary migration | Chosen, typically for economic or lifestyle reasons | Skilled workers moving to the Gulf states |
| Forced migration | Compelled by war, persecution or disaster | Syrian refugees fleeing civil war (2011–present); over 6 million have left Syria |
| Temporary / circular migration | Seasonal or fixed-term movement | Mexican agricultural workers in the USA; Gulf construction labour |
A vital legal distinction underlies these categories. A refugee is a person who has fled their country owing to a well-founded fear of persecution and is protected under the 1951 UN Refugee Convention; an asylum seeker is someone whose refugee claim is awaiting decision; an economic migrant moves chiefly to improve their material conditions and has no automatic right to protection. Conflating these — for example labelling all asylum seekers "economic migrants" — is both factually wrong and a common error penalised by examiners. By 2023, UNHCR recorded over 110 million people forcibly displaced worldwide, the highest figure on record, driven by Syria, Ukraine, Afghanistan, South Sudan and Myanmar.
Ernst Georg Ravenstein, a German–English geographer, analysed census data from England and Wales to formulate the first systematic generalisations about migration. Published as "The Laws of Migration" in 1885 (and 1889), they remain a standard starting point:
Strengths: the first quantitative theory of migration; several "laws" remain broadly valid (distance-decay, step migration, counter-streams, the dominance of economic motives); they framed every subsequent model, including Lee's.
Weaknesses: based on 19th-century British census data, so the content may not transfer to modern, globalised or non-Western contexts; the laws ignore forced migration, refugees and political drivers entirely; the gender generalisations are dated — women now constitute roughly half of all international migrants and increasingly move long-distance and independently; and modern air travel and digital communication have weakened distance-decay, so a Filipino nurse may bypass nearby cities to reach London or the Gulf directly.
Everett Lee (1966) generalised Ravenstein into a more flexible framework. Crucially, Lee added the insight that migration is shaped not only by conditions at origin and destination but by obstacles in between and by the migrant's own characteristics — so the same objective conditions produce migration for some people and not others.
graph TD
subgraph Origin
A["Push factors<br/>(negatives at origin)<br/>Unemployment, poverty,<br/>conflict, hazard, persecution"]
end
subgraph Destination
B["Pull factors<br/>(positives at destination)<br/>Jobs, higher wages,<br/>safety, education, healthcare"]
end
subgraph Barriers
C["Intervening obstacles<br/>Distance, cost, visa laws,<br/>language, family ties"]
end
subgraph Personal
D["Personal factors<br/>Age, education, risk tolerance,<br/>family situation, information"]
end
A --> C
C --> B
D --> C
| Push factors (origin) | Pull factors (destination) |
|---|---|
| Unemployment and poverty | Job opportunities and higher wages |
| War, conflict, persecution | Political stability and safety |
| Natural hazards (drought, flooding, earthquakes) | Better or safer environment |
| Poor healthcare and education | Quality healthcare and education |
| Lack of political or religious freedom | Civil liberties and human rights |
| Environmental degradation, land scarcity | Family or diaspora networks already present |
A common refinement is that push factors often dominate forced migration (people are driven out), while pull factors often dominate voluntary migration (people are drawn towards opportunity) — though in reality most moves involve both. Lee also stressed that perceptions of origin and destination matter as much as reality: migrants act on information, which may be incomplete or distorted by media and by the rosy reports of earlier migrants.
Lee's most original contribution was the intervening obstacle: the friction between origin and destination that filters who actually moves. These include physical distance and travel cost, immigration laws and visa requirements, language, family and community ties at origin, and lack of information.
Key Definition: Selective migration arises because obstacles do not bear equally on everyone. The young, educated, healthy and financially resourced are best able to overcome barriers, so migrant streams are not a random sample of the origin population. The result is often a brain drain — the loss of skilled and dynamic individuals from the origin — though this can be partly offset by remittances (money sent home, worth over US$650 billion to LICs and MICs in 2023, more than total foreign aid) and by return migration of skills and capital.
The UK experienced a step-change in immigration after the EU enlargement of 2004, when eight Central and Eastern European states (the "A8", including Poland, Hungary, the Czech Republic and the Baltics) joined the EU and — unusually — the UK granted their citizens immediate rights to live and work, rather than imposing the transitional controls most member states used.
Key statistics:
Economic impacts — a genuinely balanced ledger:
Social and cultural impacts:
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