Cambridge International AS and A Level Economics: Countries at different levels of development (9708)

This guide covers section 11.4 (Characteristics of countries at different levels of development), outcomes 11.4.1 to 11.4.3, of the Cambridge International AS and A Level Economics 9708 syllabus for examination in 2026, 2027 and 2028. Topic 11 is A Level content only and is not examined at AS. Every country, firm and figure below is invented for illustration and is not a real statistic.

Start from the Cambridge A Level Economics hub or the printable checklist. After this guide, use the revision notes and the practice questions. Try the free diagnostics to find weak spots.

What this guide covers

Outcome	What you must be able to do
11.4.1	Measure and explain changes in birth rate, death rate, infant mortality and net migration; explain optimum population; explain the level of urbanisation
11.4.2	Calculate the Gini coefficient and analyse Lorenz curves
11.4.3	Describe employment across primary, secondary and tertiary sectors, and the pattern of trade, at different levels of development

Papers 3 and 4 test this content. Paper 4 essays are unstructured and show evidence of evaluation, so judgements are flagged below.

11.4.1 Population growth and structure

Measuring change

Measure	Definition
Birth rate	Live births in a year for every 1,000 people in the population
Death rate	Deaths in a year for every 1,000 people in the population
Natural increase	Birth rate minus death rate
Infant mortality rate	Deaths of children under one year old per 1,000 live births
Net migration	Immigrants minus emigrants

Note that the infant mortality rate uses live births as its base, not the whole population. Population growth = natural increase + net migration.

Worked example (Country A, invented). Population 24.0 million. In one year there are 600,000 births, 192,000 deaths, 36,000 immigrants and 84,000 emigrants. Infant deaths are 31,200.

• Birth rate = 600,000 ÷ 24,000,000 × 1,000 = 25.0 per 1,000.
• Death rate = 192,000 ÷ 24,000,000 × 1,000 = 8.0 per 1,000.
• Natural increase = 25.0 − 8.0 = 17.0 per 1,000, which is 1.7%.
• Net migration = 36,000 − 84,000 = −48,000, which is −2.0 per 1,000.
• Growth = 17.0 − 2.0 = 15.0 per 1,000 = 1.5%, a rise of 360,000 to 24.36 million.
• Infant mortality rate = 31,200 ÷ 600,000 × 1,000 = 52.0 per 1,000 live births.

Causes of the differences

Birth rate tends to be higher at low income. Reasons:

• High infant mortality leads families to have more children as insurance.
• Children provide farm or family labour, and there is no state pension, so children are old-age support.
• Lower female education and fewer job opportunities for women, and limited access to contraception.

As income rises, the opportunity cost of children rises, education spreads, and birth rates fall.

Death rate falls as clean water, sanitation, nutrition and healthcare improve. It often falls before the birth rate does, so natural increase rises during the early stages of development. A high-income country can still have a death rate near or above its birth rate, because an old age structure means more people are in the age groups where death is common (Country B below).

Infant mortality is high where there is poor sanitation, little access to clinics, malnutrition and low maternal education. It reflects health, nutrition, income and education at once.

Net migration reflects push factors (low pay, joblessness, war) and pull factors (better pay, available work, security). Low-income countries often have net emigration of younger workers, which can cost them skills (brain drain) but bring remittances. High-income countries often have net immigration.

Worked example (Country B, invented). Birth rate 10, death rate 11, net migration +5 per 1,000. Natural change is −1 and total growth is −1 + 5 = +4 per 1,000, or 0.4%. On a population of 30 million that is 120,000 extra people, all because of migration.

Structure. High birth rates give a young population with many dependants per worker. Low birth rates and longer lives give an ageing one.

Optimum population

The optimum population is the population that, given the available resources, capital and technology, produces the highest output per head (or the highest living standard). Overpopulation means too many people for the resources, so output per head is below its possible maximum. Underpopulation means resources are under-used and more workers would raise output per head.

Worked example (Country C, invented).

Population (millions)	10	20	30	40	50
Total output (bn)	200	520	840	1,000	1,050
Output per head	20.0	26.0	28.0	25.0	21.0

Output per head peaks at 28.0 with 30 million people, so that is the optimum. At 20 million the country is underpopulated: each extra 10 million people adds 320 bn, which is 32 per head and above the average. Beyond 30 million each extra 10 million adds 160 bn or less (16 per head or less), so the average falls and the country is overpopulated at 50 million.

Now suppose better technology raises total output to 230, 620, 1,020, 1,400 and 1,450. Output per head becomes 23, 31, 34, 35 and 29, so the optimum moves to 40 million. The optimum is not fixed. It shifts with technology, capital, skills and resources.

Evaluation: the optimum is hard to measure, depends on the objective (output per head, not distribution or the environment), and can rise after investment.

Urbanisation

Urbanisation is a rise in the share of the population living in towns and cities. The level is urban population divided by total population.

Worked example (Country D, invented). Population 36.0 million, of which 14.4 million is urban: 14.4 ÷ 36.0 = 40%. Later, population 42.0 million, urban 21.0 million: 21.0 ÷ 42.0 = 50%. Urban population grew by 45.8%, total population by 16.7%, and the rural population fell from 21.6 to 21.0 million (−2.8%).

Urbanisation usually accompanies development as workers move from low-productivity farming to industry and services. Rural-urban migration is driven by push factors (low farm incomes, land shortage) and pull factors (jobs, schools, hospitals). Costs can include congestion, slums, pollution and pressure on housing and public services.

11.4.2 Income distribution: Gini coefficient and Lorenz curve

A Lorenz curve plots the cumulative share of income (vertical) against the cumulative share of the population, ordered from poorest to richest (horizontal). The 45-degree line of equality shows perfect equality. The further the curve sags below it, the more unequal the distribution.

cumulative income share
100% |                  /*
     |               /  *
     |            /   *      line of equality: /
     |         /   *         Lorenz curve: *
     |      /   *
     |   /  *
     |/ *
     +---------------------- population share

Let A be the area between the line of equality and the Lorenz curve, and B the area below the Lorenz curve. Then:

Gini coefficient = A ÷ (A + B). Because A + B = 0.5 when both axes run from 0 to 1, this equals 1 − 2B.

A Gini of 0 is perfect equality and 1 is one person having everything.

Calculating from grouped data. Take cumulative shares as fractions. Find B by adding the trapezium under each segment: width × (y at start + y at end) ÷ 2.

Worked example 1 (Country A, quintiles, invented). Income shares of the five groups, poorest first: 5, 10, 15, 25, 45 per cent. Cumulative: 5, 15, 30, 55, 100. Each group has width 0.2.

• B = 0.2 × [(0 + 0.05) + (0.05 + 0.15) + (0.15 + 0.30) + (0.30 + 0.55) + (0.55 + 1.00)] ÷ 2
• = 0.1 × (0.05 + 0.20 + 0.45 + 0.85 + 1.55) = 0.1 × 3.10 = 0.31
• Gini = 1 − 2 × 0.31 = 0.38. (Check: A = 0.19 and 0.19 ÷ 0.50 = 0.38.)

Worked example 2 (unequal groups, invented). The poorest 50% of households get 20% of income, the next 40% get 40%, the richest 10% get 40%. Cumulative points: (0.5, 0.2), (0.9, 0.6), (1.0, 1.0).

• Trapezia: 0.5 × (0 + 0.2) ÷ 2 = 0.050; 0.4 × (0.2 + 0.6) ÷ 2 = 0.160; 0.1 × (0.6 + 1.0) ÷ 2 = 0.080.
• B = 0.290, so Gini = 1 − 0.580 = 0.42.

Lorenz analysis after a policy. Suppose a progressive tax and transfer system changes Country A’s shares to 8, 12, 17, 25, 38. Cumulative: 8, 20, 37, 62, 100. The poorest 40% now get 20% rather than 15%. The pairs sum to 0.08 + 0.28 + 0.57 + 0.99 + 1.62 = 3.54, so B = 0.354 and Gini = 1 − 0.708 = 0.29. The Lorenz curve has shifted towards the line of equality.

What to say when comparing curves:

• A curve wholly nearer the line of equality means clearly more equal.
• If two curves cross, their Gini coefficients can still be equal but the distributions differ, one being fairer to the poorest and the other to the middle. Say where they cross.
• Limits: the Gini ignores the level of income (a poor, equal country can have a low Gini), may miss informal incomes, and depends on whether income, wealth, before or after tax is measured.

11.4.3 Economic structure

Employment by sector

The primary sector gets raw materials from land and sea (for example agriculture and mining). The secondary sector turns them into goods and structures. The tertiary sector sells services. At low incomes, most workers are in the primary sector. As income rises, the secondary share grows, and then the tertiary share becomes the largest.

Worked example (Country E, invented). Year 1: 8.0 million employed, shares 60%, 15%, 25%. Year 20: 12.0 million employed, shares 30%, 25%, 45%.

Sector	Year 1 (millions)	Year 20 (millions)
Primary	4.8	3.6
Secondary	1.2	3.0
Tertiary	2.0	5.4

Primary employment fell in absolute terms by 1.2 million, while the share fell by half. If the sectors’ shares of output in year 20 are 12%, 30% and 58%, the output share divided by employment share is 0.40, 1.20 and 1.29. Output per worker in the primary sector is about 40% of the national average, so moving workers out raises national productivity.

Reasons for the shift: rising farm productivity releases labour, demand for manufactures and services grows faster than demand for food as income rises (food has low income elasticity), and investment favours industry.

Evaluation: a move out of agriculture is not automatically progress. If workers move into informal, low-paid services, productivity gains are small. A high-income economy may also see the secondary share fall as services expand.

Pattern of trade

General tendencies, which vary by country:

• Low income: exports mostly primary products, imports manufactures, machinery and fuel.
• Middle income: a growing share of manufactured exports, imports of capital goods and components.
• High income: exports manufactures and a large share of services, and much trade is in similar goods with other advanced economies.

Worked example (invented). Country L exports 4.0 bn, 85% of it primary: 3.4 bn primary and 0.6 bn other. Country H exports 100 bn, 10% primary: 10 bn primary and 90 bn other. A 20% fall in primary prices, with volumes unchanged, cuts:

• L: 3.4 × 0.80 = 2.72, so exports become 2.72 + 0.60 = 3.32 bn, a fall of 17.0%.
• H: 10 × 0.80 = 8.0, so exports become 98 bn, a fall of 2.0%.

Dependence on a few primary exports exposes a country to volatile prices. Diversification reduces that risk.

Common errors

• Using the whole population as the base for infant mortality instead of live births.
• Treating optimum population as the largest population or as a fixed number.
• Calling a high death rate proof of poor healthcare, when age structure can explain it.
• Reading the Gini as 0 to 100 on a 0 to 1 scale, or stopping at B without doing 1 − 2B.
• Forgetting that the horizontal axis must be cumulative population ordered poorest first.

Official syllabus

Cambridge International AS & A Level Economics 9708 syllabus for examination in 2026, 2027 and 2028, published by Cambridge University Press & Assessment (Cambridge International), section 11.4.