You are viewing a free preview of this lesson.
Subscribe to unlock all 5 lessons in this course and every other course on LearningBro.
Spec mapping (AQA 7037): Paper 2 (Human Geography), §3.2.4 Population and the Environment and the migration strand of Global systems and global governance (§3.2.1) — the causes, consequences and management of real migration flows, and the costs and benefits for source and host societies. This depth lesson supplies the located, quantified evidence with which to test the theories of the previous lesson: a long-distance economic flow (Mexico→USA), an EU free-movement flow (Poland→UK), a forced-displacement crisis (Syria), and the largest internal migration in history (rural→urban China). It exercises AO1 (precise case-study knowledge), AO2 (explaining drivers and impacts using theory) and AO3 (manipulating remittance, net-migration and burden data and weighing costs against benefits). Synoptic links run back to the theory lesson (Lee, Todaro, Stark, Zelinsky), to Global systems (remittances, governance, the EU), and to §3.2.5 (Resource security — refugee influxes stress water and food in host states such as Lebanon and Jordan).
Examiners reward case studies deployed analytically, not recited. For every flow, organise the evidence around four questions, and always attach the relevant theory:
flowchart LR
C["CAUSES<br/>push/pull · Stark risk · conflict"] --> P["PATTERN<br/>scale · selectivity · direction"]
P --> I["IMPACTS<br/>source vs host · economic/social/political"]
I --> M["MANAGEMENT<br/>policy · effectiveness · for whom?"]
The decisive evaluative move is always for whom, and at what scale? — a flow can be a net benefit nationally while harming a particular community, or benefit migrants while straining the host's poorest. The four cases below are chosen to contrast on type (voluntary/forced, internal/international) so they can be played against one another in essays.
The Mexico–US corridor is the largest sustained bilateral flow in modern history. Mexican-born residents of the USA numbered ~10.7 million in 2022 (Pew), down from a peak of ~12.8 million in 2007 — net Mexican migration has been near zero or negative since the 2008 recession, an important corrective to the popular image of an ever-rising flow. The migration is heavily selective (young, working-age) and historically circular before border militarisation made repeated crossing riskier and so, paradoxically, encouraged permanent settlement.
| Push (Mexico) | Pull (USA) |
|---|---|
| ~36–44% below national poverty line | Labour demand: agriculture, construction, services |
| Wage gap: ~$15/day (Mexican min.) vs ~$58+/day (US) | Wages 5–10× higher |
| Cartel violence: 350,000+ homicides since 2006 | Established diaspora → chain migration |
| Rural landlessness; northern water scarcity | Family reunification; schools; rule of law |
Todaro's expected-income logic fits: even with the risk and cost of an undocumented crossing, the expected US wage dwarfs rural Mexican income. Stark's NELM fits the remittance behaviour — households send a member north to diversify income, and the money flows home reliably.
On Mexico (source): the dominant benefit is remittances — ~$63.3 billion in 2023, making Mexico the world's second-largest recipient and exceeding foreign direct investment; these fund housing, schooling and small business in sending regions. Against this: brain drain of the young and skilled, remittance dependency in some states (where transfers exceed local wages), family separation, rural depopulation and male-skewed gender imbalance.
On the USA (host): migrants fill shortages in agriculture (where they are a majority of field labour), construction and services, raise output and tax revenue, and partly offset population ageing. Costs are concentrated and contested: pressure on schooling and healthcare in border states, debated wage effects in low-skill sectors (most economists find small effects), and political and social tension. Enforcement is itself a major cost — US Customs and Border Protection ran a budget of ~$18 billion in 2023.
Policy has oscillated between enforcement (border-wall extension, deportations, workplace raids), legal channels (H-2A/H-2B seasonal visas; DACA protection for those brought as children) and development-through-trade (NAFTA/USMCA, on the theory that Mexican growth reduces the push). Evaluation: enforcement raised the cost of crossing without removing the expected-income incentive, so it shifted migration from circular to permanent and pushed flows into more dangerous routes — arguably the clearest real-world demonstration that suppressing symptoms without addressing drivers backfires. The flow ultimately fell most when Mexican development and US recession narrowed the wage gap — vindicating Todaro over the wall.
When Poland and seven other states (the A8) joined the EU in May 2004, the UK was one of only three members to grant immediate labour-market access. The result was one of the largest peacetime flows in European history: the Polish-born UK population rose from ~95,000 (2003) to a peak of ~911,000 (2017, ONS), making Polish the most common non-British nationality in England and Wales. Peak years were 2005–08.
| Push (Poland) | Pull (UK) |
|---|---|
| Unemployment ~19% in 2004 (highest in EU) | Strong economy, labour shortages |
| Average wage ~€600/month | UK minimum wage 3–4× Polish equivalent |
| Few graduate openings | English widely spoken; WWII-era diaspora |
| — | Immediate access (the decisive institutional pull) |
The Polish case is the textbook demonstration that institutions, not just wages, govern migration: the wage gap with Poland was no larger than with several non-EU states, but freedom of movement plus the UK's open-door choice is what unlocked the flow at that scale and moment — a point gravity and Todaro models, which omit policy, cannot capture.
On Poland (source): unemployment fell from ~19% to ~5% by 2018 (emigration relieved the labour surplus); remittances of ~£1bn+ a year flowed home; returnees brought skills and capital. Costs: labour shortages later emerged in construction and healthcare, emigration of the young accelerated ageing, and some regions depopulated.
On the UK (host): Polish workers filled shortages in agriculture, food processing, construction and hospitality; a UCL study (Dustmann & Frattini, 2014) found A8 migrants made a net positive fiscal contribution (paying more in tax than they drew in benefits), partly because they were young, working and educated. Costs were localised: pressure on housing, schools and GP surgeries in areas of rapid settlement (e.g. Boston, Lincolnshire), contested wage effects at the bottom, and social tension that fed Euroscepticism — Boston recorded the highest Leave vote (75.6%) in the 2016 referendum.
After 2017 net Polish migration turned negative — Brexit uncertainty, a stronger Polish economy, the falling pound and the end of free movement reduced the UK's pull. Under the EU Settlement Scheme ~1 million Polish nationals applied for settled status, but flows reversed, and shortages re-emerged in the very sectors (food processing, agriculture, care) that had relied on the flow — a natural experiment confirming how dependent those sectors had become.
The Syrian civil war produced one of the largest displacement crises since 1945. By 2023 roughly 13.5 million Syrians — over half the pre-war population of ~22 million — had been displaced.
| Category | Number (approx.) |
|---|---|
| Internally displaced (IDPs) | 6.9 million |
| Refugees in neighbouring states | ~5.5 million |
| Refugees who reached Europe | ~1.1 million |
Crucially, the majority stayed within Syria or in neighbouring countries — the popular European focus on the 2015 "crisis" obscures that the regional burden vastly exceeds the European one.
| Host | Syrian refugees | ≈ % of host population |
|---|---|---|
| Türkiye | ~3.4 million | ~4% |
| Lebanon | ~0.8 million (registered) | ~13% (with unregistered, higher) |
| Jordan | ~0.66 million | ~6.5% |
| Germany | ~0.8 million+ | ~1% |
The drivers are non-economic — barrel-bombing, chemical-weapons attacks, siege warfare, persecution by the regime, ISIS and other groups, and economic collapse (GDP fell ~60%, 2010–17). This is precisely the migration that push–pull and Todaro cannot explain: there is no rational wage calculus in fleeing a siege. Impacts on hosts:
The international response exposed the weakness of burden-sharing: UNHCR was chronically underfunded; the EU–Turkey deal (2016) effectively paid Türkiye (€6 billion plus visa promises) to hold returned asylum seekers, reducing Aegean crossings but drawing criticism for externalising protection; and wealthy Gulf states accepted almost no refugees. The Syrian case is the strongest A-Level evidence that the governance of forced migration is failing — the legal architecture (the 1951 Convention) governs recognition but contains no mechanism to share the burden fairly, so geography (who is next door) determines who pays.
China's rural-to-urban migration is the largest in human history: since the 1978 reforms, an estimated 300 million people have moved to cities, and the urban share rose from ~18% (1978) to ~65% (2023). The mobile population — the "floating population" — was ~376 million in 2020.
China's hukou (household-registration) system ties access to schooling, healthcare and housing to one's registered (usually rural) location. Migrants working in cities therefore form a second-class urban tier, lacking the services their taxes help fund — a uniquely institutional driver and constraint with no parallel in the other cases.
| Push (rural) | Pull (urban) |
|---|---|
| Rural income ~⅓ of urban | Manufacturing/construction/services wages |
| Tiny, fragmented farms (<0.5 ha) | Better schools, healthcare, amenities |
| Weak services; land conversion | Booming labour demand |
On rural source areas: remittances fund rural families and investment, but the human cost is severe — an estimated 60 million "left-behind children" are raised by grandparents while parents work in distant cities, rural areas age, and farm productivity and services decline.
On urban destinations: migrants supplied the labour for China's manufacturing and construction boom (the foundation of its growth), but at the cost of hukou-based inequality, "urban village" informal settlements, and severe environmental strain (air pollution, water supply, waste).
Reform has been gradual: hukou restrictions relaxed in smaller cities, the "New-type urbanisation" plan (2014) aimed to grant urban hukou to 100 million people by 2020, and a "rural revitalisation" strategy seeks to reduce the push. Evaluation: hukou reform has eased the burden at the margins, but the largest cities still ration registration, so the two-tier structure persists. The Chinese case shows that internal migration can be the engine of national development while simultaneously producing deep social inequality — and that the state's institutional design, not market forces alone, shapes who benefits.
Remittance dependency. Suppose a Mexican sending-village has 2,000 households, of which 600 receive remittances averaging $4,000/year. Total inflow =600×4,000=2,400,000, i.e. US$2.4 million/year. If the village's total local wage income is US$6 million, remittances add 6.02.4×100=40% to local income — a vivid illustration of both the benefit (a near-doubling of some households' income) and the risk (exposure to a US downturn or tighter border policy). This is Stark's risk-diversification logic made quantitative.
Refugee burden — comparing fairly. Lebanon hosts ~0.8 million Syrians on a population of ~6 million ≈ 13%, while Germany hosts ~0.8 million on ~83 million ≈ 1%. Although the absolute numbers are similar, the relative burden differs by an order of magnitude — the essential AO3 point that absolute figures mislead without normalising to population. A candidate who computes both, and notes that the poorer country bears the heavier relative load, demonstrates exactly the quantitative-with-evaluation skill the top band rewards.
Net migration direction. Polish net migration to the UK was strongly positive 2004–16, then turned negative post-2017. The sign change — not the absolute level — is the analytically important variable, because it marks the moment the institutional pull (free movement) and the wage gap both weakened.
| Aspect | Mexico→USA | Poland→UK | Syria | China internal |
|---|---|---|---|---|
| Type | International; mostly voluntary | International; voluntary | International + internal; forced | Internal; voluntary |
| Primary driver | Wage gap + insecurity | Wage gap + free movement | Conflict/persecution | Wage gap + hukou context |
| Scale | ~10.7m (stock) | ~911k (peak) | ~13.5m displaced | ~300m |
| Best-fit theory | Todaro / Stark | Lee + institutions | None economic — forced | Todaro + institutional |
| Key policy issue | Border control; undocumented status | Free movement; Brexit | Burden-sharing; protection | Hukou reform |
| Remittances | ~$63.3bn (2023) | ~£1bn+/yr | Limited | Large, internal |
Subscribe to continue reading
Get full access to this lesson and all 5 lessons in this course.