Characteristics of countries at different levels of development

Income inequality describes how unevenly income is distributed across households. Two related tools measure it: the Lorenz curve (a diagram) and the Gini coefficient (a number).

The Lorenz curve plots the cumulative percentage of total income (vertical axis) against the cumulative percentage of households, ranked from poorest to richest (horizontal axis). The 45° line is the line of perfect equality: along it, the poorest 20% of households receive 20% of income, the poorest 50% receive 50%, and so on. In reality, the poorest households earn a smaller share, so the actual Lorenz curve sags below the 45° line. The further the curve bows away from the line of equality, the more unequal the distribution; a country whose curve lies closer to the 45° line has a more equal distribution. The Lorenz curve therefore gives a clear visual comparison of inequality between countries or over time.

The Gini coefficient turns the Lorenz curve into a single number from 0 to 1. It is calculated as the area between the line of equality and the Lorenz curve (A) divided by the whole area beneath the line of equality (A + B): Gini = A ÷ (A + B). A Gini of 0 means perfect equality (the Lorenz curve lies on the 45° line, so A = 0); a Gini approaching 1 means near-perfect inequality (one household has almost all income). A higher Gini therefore means greater inequality. For example, if A = 0.18 and B = 0.32, the Gini is 0.18 ÷ 0.50 = 0.36.

Used together, the Lorenz curve shows the shape of the distribution and the Gini coefficient summarises it in one comparable figure — allowing inequality to be compared between countries and over time. This matters for development because two countries with the same average income can have very different distributions, and rising GDP per head can coincide with rising inequality.

Developed and developing economies differ across several characteristics — income, population, income distribution and economic structure — though the differences are matters of degree, and emerging economies (e.g. the BRICS) lie between them.

Income and living standards. Developed economies have high real GNI per head (PPP) and high HDI (long life expectancy, high education); developing economies have lower income per head and HDI, with more absolute poverty. This is the most obvious difference, but income alone is incomplete.

Population. Developing economies typically have higher birth rates and higher infant mortality, giving young, fast-growing populations (a wide-based age pyramid) that strain education, healthcare and job creation. Developed economies have low birth and death rates and ageing populations, raising the dependency ratio and pension/healthcare costs, and often experience net immigration (including skilled workers, while some developing economies suffer a 'brain drain').

Income distribution. Inequality, shown by the Lorenz curve and Gini coefficient, is often higher in many developing economies (a Gini further toward 1), though some developed economies also have significant inequality. Distribution matters because high inequality means average income overstates how most people live.

Economic structure and trade. Developing economies have a large primary sector (much of the workforce in agriculture) and often export primary commodities — exposing them to price volatility and income-inelastic demand. Emerging economies have a growing secondary (manufacturing) sector, and developed economies are dominated by the tertiary (services) sector and export manufactures and services (higher value-added, more stable demand). Other differences include infrastructure, institutions, access to capital/credit and technology, all stronger in developed economies.

Judgement. The characteristics are interlinked — low income, high population growth, unequal distribution and a primary-based structure tend to occur together, and the reverse for developed economies. But the distinction is one of degree, not a sharp divide: emerging economies show that countries move along a spectrum, and a single characteristic (e.g. high oil income) can be misleading. The most defensible conclusion is that developed and developing economies differ across a range of interlinked characteristics — income, HDI, population structure, distribution and economic structure — and any comparison should use several of these rather than income alone, recognising that economies form a continuum from least-developed through emerging to developed.

The data show two changes happening together: real GDP per head doubled (rapid economic growth) while the Gini coefficient rose from 0.30 to 0.42, meaning income inequality increased (the Gini scale runs from 0 = equal to 1 = unequal, so a rise toward 1 is more unequal — the Lorenz curve has bowed further from the line of equality).

This suggests that, although average material living standards have risen strongly (more output and income per person), the gains have been unevenly shared: a richer minority has captured a larger share, so the average overstates the improvement for poorer households. Development — which concerns living standards for the population as a whole, not just the average — may therefore have improved by less than the doubling of GDP per head implies.

This pattern is consistent with the rising portion of the Kuznets curve, where inequality increases in the early stages of rapid growth before (potentially) falling later. A full judgement on development would also need non-monetary indicators (health, education) and a measure of poverty (e.g. the MPI), since rising average income with rising inequality can leave the poorest little better off.

Urbanisation — the rising share of people living in towns and cities, driven by rural-to-urban migration — typically accompanies development, but whether it is beneficial depends on how well cities can absorb the inflow.

The case that urbanisation is beneficial. Moving labour from low-productivity agriculture into urban industry and services raises productivity and incomes, supporting the structural shift from primary to secondary/tertiary that development requires. Cities offer agglomeration economies — firms benefit from a large labour pool, shared infrastructure and proximity to suppliers and customers — and concentrate access to education, healthcare and services. Higher urban wages can also raise living standards and, via remittances, support rural families. In this sense urbanisation is part of how economies grow and develop.

The case against / the risks. If urbanisation is too rapid for infrastructure and job creation to keep pace, the benefits can be outweighed by costs: slums and inadequate housing, sanitation and clean water; congestion and pollution; and urban unemployment and underemployment as migrants arrive faster than formal jobs are created. Rapid rural-to-urban migration can also leave agriculture short of labour and widen rural-urban inequality. So the same process can reduce welfare if cities cannot cope.

Judgement. Urbanisation is potentially beneficial — it raises productivity and concentrates services, and is a normal feature of development. But it is not automatically beneficial: the outcome depends on whether the government provides the infrastructure, housing and jobs to absorb the inflow. The most defensible conclusion is that well-managed urbanisation supports development, whereas rapid, unplanned urbanisation can create slums, congestion and unemployment that offset the gains — so the benefit depends on the pace of urbanisation and the policy response.