Economic Development – Classification of Economies (Cambridge 9708 – Topics 11.3 – 11.5)
Classifying economies enables us to compare living standards, analyse structural change and choose appropriate policy responses. The Cambridge syllabus recognises three inter‑related approaches:
- Income‑based classification – uses national‑income measures (GDP or GNI per‑capita, usually in US$ PPP).
- Human‑development‑based classification – combines health, education and income (the UNDP Human Development Index, HDI).
- Traditional / functional labels – “developed”, “developing”, “least‑developed”, “emerging market” and “transition economy”.
1. Income‑Based Classification (World Bank GNI pc)
The World Bank groups are the standard A‑Level framework. They are based on GNI per‑capita (current US$) PPP and are updated annually. Memorise the 2023 thresholds – they are frequently asked in data‑response questions.
| Income Group |
2023 GNI pc (US$ PPP) |
Typical Economic Structure |
Representative Countries |
| Low‑income economies |
≤ 1 085 |
Predominantly agricultural, limited industry, high poverty. |
Ethiopia, Haiti, Nepal |
| Lower‑middle‑income economies |
1 086 – 4 255 |
Growing manufacturing, improving infrastructure, moderate poverty. |
India, Vietnam, Kenya |
| Upper‑middle‑income economies |
4 256 – 13 205 |
Diversified industry & services, rising living standards. |
China, Brazil, South Africa |
| High‑income economies |
> 13 205 |
Advanced technology, high per‑capita consumption, strong institutions. |
United Kingdom, Japan, United States |
Key exam points
- Distinguish GDP per‑capita (output) from GNI per‑capita (income). The World Bank uses GNI.
- Explain why PPP adjustment is essential for cross‑country comparison.
- Remember the four threshold values – they often appear in data‑response questions.
2. Human‑Development‑Based Classification (UNDP HDI)
The Human Development Index combines three equally weighted dimensions:
- Health – life expectancy at birth.
- Education – mean years of schooling (adults) + expected years of schooling (children).
- Standard of living – GNI per‑capita (PPP, log‑scaled).
The index ranges from 0 to 1. The 2022 categories are:
| HDI Category |
HDI Range (2022) |
Typical Features |
Representative Countries |
| Very High Human Development |
0.800 – 1.000 |
High life expectancy, universal secondary/tertiary education, high income. |
Germany, Australia, Singapore |
| High Human Development |
0.700 – 0.799 |
Improving health & education, moderate‑high income. |
Mexico, Thailand, South Africa |
| Medium Human Development |
0.550 – 0.699 |
Significant gaps in health/education, lower income. |
India, Nigeria, Philippines |
| Low Human Development |
< 0.550 |
High mortality, low schooling, very low income. |
Afghanistan, Chad, Yemen |
3. Traditional & Functional Labels
- Developed (Advanced) Economies – High‑income, very high HDI, diversified industrial & service sectors, strong institutions. Examples: United States, Japan, Norway.
- Developing Economies – Encompasses lower‑ and upper‑middle‑income countries; economies in transition from agriculture to industry/services. Examples: Brazil, Malaysia, Kenya.
- Least‑Developed Countries (LDCs) – Extremely low income, weak institutions, high vulnerability; classified by the UN Committee on LDCs. Examples: Haiti, Mozambique, Yemen.
- Emerging Markets – Upper‑middle‑income economies that are rapidly industrialising and integrating into global financial markets. Examples: China, India, Brazil.
- Transition Economies – Countries moving from centrally‑planned to market‑oriented systems; usually former Soviet or Eastern‑European states. Examples: Poland, Kazakhstan, Romania.
4. Indicators Used to Assess Development
For the A‑Level exam you should be able to name, define and interpret the following variables.
| Indicator |
Formula / Description |
What it Shows |
| GDP per‑capita (real, PPP) |
GDP ÷ Population |
Average output per person; basic measure of material welfare. |
| GNI per‑capita (PPP) |
GNI ÷ Population |
Average income received by residents; used for World Bank groups. |
| Human Development Index (HDI) |
Geometric mean of life‑expectancy, education and GNI indices |
Composite welfare measure. |
| Life expectancy at birth |
Average years a newborn is expected to live |
Health dimension of development. |
| Mean & expected years of schooling |
Average years of education for adults + projected years for children |
Education dimension of development. |
| Employment structure |
Share of workforce in agriculture, industry, services |
Stage of structural transformation. |
| Population dynamics |
Birth & death rates, net migration, urbanisation rate |
Demographic pressures and labour‑force growth. |
| Income distribution |
Gini coefficient (0 = perfect equality, 1 = perfect inequality); Lorenz curve |
How evenly income is shared – essential for interpreting “high‑income” labels. |
| Poverty rate |
% of population living below $5.50 a day (PPP) or national poverty line |
Depth of deprivation beyond average income. |
| Gender Gap Index (GGI) |
Composite of health, education, economic participation, political empowerment |
Gender‑related development dimension. |
| Environmental sustainability |
Ecological footprint, CO₂ emissions per‑capita |
Shows whether development is ecologically viable. |
5. Characteristics of Countries at Different Development Levels (Syllabus 11.4)
- Low‑income / LDCs
- High birth rates, low death rates → rapid population growth; low urbanisation.
- Gini often high, but data may be unreliable.
- >80 % of workforce in agriculture; limited manufacturing.
- Poverty rates > 40 %; life expectancy < 60 years.
- Lower‑middle‑income
- Demographic transition underway – falling fertility, rising life expectancy.
- Gini typically 0.30‑0.45 (moderate inequality).
- Agriculture still important (≈40 %); industry expanding (≈20‑30 %).
- Urban population growing fast (often > 30 % per decade).
- Upper‑middle‑income / Emerging Markets
- Fertility near replacement; ageing population beginning in some countries.
- Gini often high (≥ 0.45) → inequality a policy focus.
- Services dominate employment (> 50 %); manufacturing remains significant.
- Rapid capital‑flow integration; vulnerability to external shocks.
- High‑income / Developed
- Low fertility, high life expectancy; population growth near zero or negative.
- Gini moderate (0.30‑0.40) with strong redistributive policies.
- Services > 70 % of employment; high R&D intensity.
- Well‑developed institutions, high institutional quality.
6. The Kuznets Curve (Linking Income & Inequality)
- Proposed by Simon Kuznets (1955): as an economy develops, inequality first rises (industrialisation draws labour from low‑productivity agriculture) and later falls (wider education, progressive taxation, social welfare).
- Typical shape: inverted “U”.
- Empirical evidence is mixed – some emerging markets (e.g., Brazil, South Africa) remain highly unequal at upper‑middle‑income levels, highlighting a limitation of the simple curve.
7. Limitations of Classification Systems (Syllabus 11.5)
- Income‑only measures – ignore inequality, informal sector, non‑market activities, and environmental degradation.
- HDI shortcomings
- Equal weighting of health, education and income is arbitrary.
- No direct measure of inequality, gender gaps or sustainability.
- Data quality varies, especially for schooling and health in low‑income states.
- Traditional labels – often vague, can be politically loaded, and do not reflect rapid structural change (e.g., “emerging market” vs. “developing”).
- Static thresholds – arbitrary cut‑offs may misclassify fast‑growing economies (e.g., South Korea in the 1970s).
- Data reliability – gaps in statistical capacity, revisions, exchange‑rate volatility, and differing PPP methodologies affect comparability.
To mitigate these problems, examiners expect you to:
- Combine more than one indicator (e.g., GNI pc + Gini + HDI).
- Discuss data‑quality issues when using tables or graphs.
- Reference supplementary indices (Gender Gap Index, Ecological Footprint, Poverty Gap).
8. Suggested Revision Diagrams
- Pyramid of World Bank Income Groups – flags of representative countries at each level.
- Scatter plot: GDP pc (PPP) vs. HDI – highlights convergence, divergence and outliers (e.g., Qatar, Nigeria).
- Employment‑structure pie charts for a low‑income, a middle‑income and a high‑income country.
- Kuznets‑curve sketch – label axes (GNI pc on x‑axis, Gini coefficient on y‑axis) and annotate real‑world examples.
9. Quick Revision Checklist
- Memorise the 2023 GNI pc thresholds for the four World Bank groups.
- Know the HDI range for each category and be able to place a country correctly.
- Recall at least two examples of countries in every income group, HDI category, and functional label.
- Be able to explain the difference between income‑based and human‑development‑based classification.
- List three limitations of each classification system and suggest a way to address each.
- State why “emerging markets” are treated separately from the generic “developing economies”.
- Describe the Kuznets curve and give one country that appears to follow, and one that deviates from, the pattern.
- Identify the key demographic and inequality indicators that accompany each development level.
10. Practice Questions
- Data‑response – Using the table below, classify Country Y:
| Indicator | Value |
|---|
| GNI per‑capita (US$ PPP) | 3 800 |
| HDI | 0.682 |
| Life expectancy | 68 years |
| Mean years of schooling | 6.5 years |
| Employment structure | Agriculture 45 % – Industry 30 % – Services 25 % |
| Gini coefficient | 0.42 |
| Urbanisation rate | 35 % |
(a) Identify the World Bank income group.
(b) Identify the HDI category.
(c) Using the traditional/functional labels, state the most appropriate label and justify your choice (refer to structure, income, and inequality).
(d) Sketch a brief Kuznets‑curve comment – does Country Y appear to be on the upward or downward leg?
- Essay – Discuss the advantages and disadvantages of using the Human Development Index rather than GNI per‑capita alone to classify economies. In your answer, refer to at least two real‑world examples and comment on how the HDI’s limitations can be mitigated.
11. Model Answers
-
(a) World Bank income group: Lower‑middle‑income (GNI pc $1 086 – $4 255).
(b) HDI category: Medium Human Development (0.550 – 0.699).
(c) Traditional/functional label: Developing economy. The GNI pc is below the high‑income threshold, the HDI is only medium, and the employment structure shows a large agricultural sector with a still‑significant share of industry – characteristic of a country in structural transition.
(d) Kuznets‑curve comment: With a Gini of 0.42 and a relatively low GNI pc, Country Y is likely on the upward‑sloping part of the Kuznets curve, where rising income is accompanied by increasing inequality.
-
Advantages of the HDI
- Captures non‑monetary dimensions of welfare – health and education – that income alone cannot reveal (e.g., Rwanda’s GNI pc is low, but its HDI is higher than expected because of strong health outcomes).
- Allows comparison of policy performance among countries with similar incomes (e.g., Chile and Malaysia have comparable GNI pc, yet Chile’s higher HDI reflects better education and health provision).
Disadvantages of the HDI
- Equal weighting of the three components is arbitrary; a country with high income but poor health can still achieve a “high” HDI (e.g., oil‑rich Qatar).
- Ignores income distribution, gender disparities and environmental sustainability – Brazil’s HDI is “high” despite large regional inequality and deforestation.
- Relies on data that may be incomplete or outdated in low‑income states, potentially distorting rankings.
Mitigation
- Use the HDI together with the Gini coefficient and gender‑gap indices to capture inequality and gender issues.
- Supplement with environmental indicators (ecological footprint) when assessing sustainability.
- Reference data‑quality notes in exam answers to acknowledge possible uncertainties.
Overall, the HDI provides a broader picture of development than GNI pc alone, but a balanced assessment should combine income, human‑development and distributional/environmental measures.