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Spec mapping (AQA 7037): Paper 2, §3.2.1 Global Systems — the consequences of globalisation for inequality and the patterns of inequalities at a range of scales; unequal development; the measurement of development and inequality; injustices and the role of global governance. This lesson also underpins the wider development thread that runs through the depth study. Synoptic links: trade and the core–periphery hierarchy (Lesson 2), TNCs and value capture (Lesson 3), financial flows and debt (Lesson 4), and governance reform (Lesson 10). It is AO1-rich (the full theory ladder from Rostow to Sen; the indicator suite) with heavy AO3 (Gini/Lorenz interpretation, HDI calculation, convergence/divergence data) and AO2 (applying competing theories to real inequality patterns).
How we measure development is not a neutral technical question — it encodes what we think development is. The depth treatment tracks a conceptual journey from purely economic measures (GDP), through composite human measures (HDI), to multidimensional, distributional and capability-based measures (Gini, MPI, Sen) — and pairs this with the theoretical battle (Rostow vs Frank vs Wallerstein vs Sen) over why inequality exists and whether the world is converging or diverging.
Key Definition: Development is the process of improving people's quality of life and expanding their real freedoms. Amartya Sen reframes it as the expansion of human capabilities — what people are actually able to be and do — rather than merely the growth of income. Development is therefore multidimensional: economic, social, political and environmental.
GDP measures the total value of goods and services produced within a territory; GNI measures the income earned by a country's residents (including abroad, minus foreign earnings at home). GDP/GNI per capita standardises by population, and the PPP (Purchasing Power Parity) adjustment corrects for local price levels — vital when comparing rich and poor economies.
| Country | GDP per capita (nominal, approx. 2023) | GDP per capita (PPP, approx. 2023) |
|---|---|---|
| USA | ~US$76,000 | ~US$76,000 |
| UK | ~US$46,000 | ~US$55,000 |
| China | ~US$12,500 | ~US$23,000 |
| India | ~US$2,600 | ~US$9,000 |
| DR Congo | ~US$580 | ~US$1,100 |
Notice how PPP narrows the gap (India's figure roughly triples), because a dollar buys more in Delhi than in New York — a key AO3 point about why nominal comparisons mislead.
Limitations of GDP are central to the spec's critical stance: it ignores distribution (a high mean can mask extreme inequality), the informal economy (large in the Global South), environmental degradation (deforestation can raise GDP short-term), non-market wellbeing (health, leisure, care work), and the difference between productive and defensive spending (prisons and disaster recovery count). The World Bank uses GNI per capita to classify economies (low / lower-middle / upper-middle / high income), but the thresholds are blunt and conceal vast within-group variation.
The HDI, conceived by Mahbub ul Haq with Amartya Sen and published by the UNDP since 1990, was a deliberate attempt to dethrone GDP by capturing health and education alongside income.
| Dimension | Indicator | Unit |
|---|---|---|
| Health | Life expectancy at birth | Years |
| Education | Mean and expected years of schooling | Years |
| Standard of living | GNI per capita (PPP, log-adjusted) | US$ |
The HDI runs 0 to 1. Indicative values: Switzerland and Norway ~0.96 (Very High); the UK ~0.93; the USA ~0.92; China ~0.79 (High); India ~0.64 (Medium); South Sudan ~0.39 (Low).
Each HDI dimension is normalised using: Dimension index=maximum−minimumactual value−minimum
For health, the UNDP uses a minimum life expectancy of 20 years and a maximum of 85. If a country has a life expectancy of 66 years: Health index=85−2066−20=6546=0.708
The overall HDI is then the geometric mean of the three dimension indices: HDI=3Ihealth×Ieducation×Iincome. The geometric mean (replacing the old arithmetic mean in 2010) matters because it penalises imbalance — a country cannot "buy" a high HDI through income alone if its health and education lag.
Strengths: broadens development beyond income; enables comparison; shows growth ≠ development (e.g. some oil states have high GNI but lower HDI). Limitations: it uses national averages that hide internal (gender, regional, ethnic) inequality — which is why the UNDP added the Inequality-adjusted HDI (IHDI), that discounts the HDI by the level of inequality in each dimension; it omits sustainability, freedom and security; and it still leans on flawed GNI.
The Gini coefficient measures within-country income (or wealth) inequality on a 0–1 scale: 0 = perfect equality, 1 = one person owns everything. It is derived from the Lorenz curve, which plots the cumulative share of income against the cumulative share of population.
| Cumulative % of population (poorest first) | Cumulative % of income — Country A (equal) | Cumulative % of income — Country B (unequal) |
|---|---|---|
| 20% | 14% | 4% |
| 40% | 30% | 11% |
| 60% | 48% | 22% |
| 80% | 70% | 42% |
| 100% | 100% | 100% |
How to read this (AO3): the line of perfect equality is the 45° diagonal (the poorest 20% would earn exactly 20% of income). The Lorenz curve sags below it; the further it bows away, the greater the inequality. The Gini coefficient = the area between the diagonal and the Lorenz curve, divided by the total area under the diagonal. In the table, Country B's poorest 60% earn just 22% of income (versus 48% in Country A), so B's curve bows far more — a much higher Gini. Indicative real values: Denmark ~0.28 (very equal); UK ~0.35; USA ~0.39; Brazil ~0.49; South Africa ~0.63 — among the most unequal on Earth, a legacy of apartheid.
Examiner tip: Describe a Lorenz curve in prose and figures — never sketch ASCII. State the share earned by the poorest X% and compare it to the equality line. Pair the Gini with the reason (e.g. South Africa's structural, racialised inequality).
The MPI (Oxford Poverty and Human Development Initiative with UNDP) measures acute poverty across ten indicators in three dimensions — health (nutrition, child mortality), education (years of schooling, attendance) and living standards (cooking fuel, sanitation, water, electricity, housing, assets). A person is multidimensionally poor if deprived in at least one-third (weighted) of indicators. As of 2023, roughly 1.1 billion people across 110 countries were MPI-poor — revealing deprivation that income lines (like US$2.15/day) miss, because it counts overlapping deprivations a household suffers simultaneously.
Simon Kuznets (1955) hypothesised an inverted-U: inequality rises in early industrialisation (some sectors race ahead), peaks at middle income, then falls as education, safety nets and democracy redistribute. Some economies fit (South Korea, Taiwan); others flatly contradict it (US inequality has risen at high income — Thomas Piketty's Capital argues the post-war fall was exceptional, not a law). The ethically loaded implication that inequality is a necessary developmental stage is exactly why it is contested. The Gender Inequality Index (GII) complements all this by measuring gendered disadvantage in reproductive health, empowerment and labour-market participation (0 = equality, 1 = extreme inequality).
W.W. Rostow's "Stages of Economic Growth" models a single, linear path from (1) traditional society → (2) preconditions for take-off → (3) take-off → (4) drive to maturity → (5) age of high mass consumption. It is optimistic and prescriptive (inject capital/aid to trigger "take-off"). Criticisms: it universalises a Western path, ignores colonialism in creating underdevelopment, "blames the victim," and reduces development to economics.
As developed in Lesson 2, Frank's dependency theory inverts Rostow: underdevelopment is produced by the core's exploitation of the periphery ("the development of underdevelopment"). Wallerstein's world-systems theory adds a mobile semi-periphery, explaining how some states (China, Brazil, India) rise as a buffer while the system still concentrates capital in the core.
graph TD
C["CORE<br/>high-value, capital-intensive<br/>USA, Germany, Japan"]
SP["SEMI-PERIPHERY<br/>industrialising, upwardly mobile<br/>China, Brazil, India, Mexico"]
P["PERIPHERY<br/>commodity export, low value<br/>much of Sub-Saharan Africa"]
P -->|cheap commodities, labour, debt service| C
C -->|manufactures, capital, IP rents| P
P <-->|mobility possible: e.g. China periphery to semi-periphery| SP
SP <--> C
Amartya Sen (Development as Freedom, 1999; Nobel laureate) reframes the whole debate: development is the expansion of capabilities and substantive freedoms, not income. Famine, he showed, results not from food shortage but from a collapse in entitlements — a failure of distribution and democracy, not nature. His approach directly inspired the HDI and MPI. Post-development thinkers (Escobar, Sachs, Esteva) go further, rejecting "development" as a Western construct that pathologises non-Western societies; they champion alternatives like Latin American buen vivir and southern African Ubuntu, and Bhutan's Gross National Happiness. The ladder thus runs from income → human development → capabilities → a critique of the very concept.
Sen's reframing is worth unpacking because it is the most influential idea in contemporary development thinking. He distinguishes:
The crucial move is that development is about expanding capabilities (freedom), not just outcomes or income. Two people with the same income may have very different capabilities (a disabled person may need more resources to achieve the same functioning; a woman in a patriarchal society may be denied the freedom to work). This explains why income is an inadequate measure and why the HDI added health and education — and it grounds the idea that development and freedom are inseparable: political liberties, economic facilities, social opportunities, transparency and security are both the ends and the means of development. Sen's framework also reframes inequality: what matters is inequality of capability (real freedom), not merely of income — a more demanding and humane standard, and one that links development directly to justice (Lesson 10).
The global framework for development goals has itself evolved, providing a concrete way to discuss how development is defined and measured in practice.
The shift from MDGs to SDGs mirrors the conceptual journey of this lesson — from a narrow, income-and-basic-needs focus towards a multidimensional, universal, sustainability- and equity-conscious definition of development. It shows the international consensus on what development means broadening over time. Yet the persistent gap between goals and delivery underlines that measuring and proclaiming development is far easier than financing and achieving it — a recurring evaluative theme.
Consider GDP per capita (PPP) growth over a period:
| Country | Start | End | % change |
|---|---|---|---|
| China | ~US$3,000 | ~US$23,000 | +667% |
| USA | ~US$45,000 | ~US$76,000 | +69% |
| DR Congo | ~US$700 | ~US$1,100 | +57% |
Manipulate: China's % change =300023000−3000×100=+667%, vastly outpacing the USA (+69%).
Explain: China's surge means it is converging on the rich world (the absolute gap with the USA narrowed dramatically in relative terms) — driving the headline fall in between-country inequality since 2000. But DR Congo's far slower growth (+57%) means the poorest periphery is diverging further behind — "divergence, big time" (Lant Pritchett) for the bottom. So the global picture is conditional convergence: middle-income emerging economies catch up while the poorest fall back.
Evaluate: % change flatters low bases (China started poor); absolute gaps can widen even as ratios narrow (23,000−3,000 start-gap vs end-gap arithmetic must be checked); and national averages conceal the within-country divergence the World Inequality Report highlights — the global top 10% capture over half of all income and the top 1% around 46% of wealth. Convergence between countries can therefore coincide with rising inequality within them.
The Brandt Line (1980) split a rich "North" from a poor "South." It is now widely seen as too crude: Australia/New Zealand sit in the geographic south but the economic "North," and the rise of China and the BRICS scrambles the binary. Yet real disparities persist in income, health, education and political power (Lessons 4 and 10) — so the framework retains heuristic value while demanding nuance.
The most important spatial development story of the past half-century is the rise of the emerging economies, which has redrawn the global map and complicated the simple core–periphery binary.
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