Economic Development and Globalisation: Question 3

Worked solution

Part (a): Cumulative income share of the poorest 40%

The poorest 40% of the population is made up of the poorest fifth and the second fifth combined.

For Kolvara: $4\% + 8\% = 12\%$

For Estenia: $10\% + 14\% = 24\%$

So the poorest 40% of the population receive only 12% of national income in Kolvara, but 24% in Estenia. Twice as large a share, even though it is the same-sized group of the population in both cases.

Part (b): Constructing and comparing Lorenz curves

A Lorenz curve plots the cumulative percentage of the population (on the horizontal axis: 0%, 20%, 40%, 60%, 80%, 100%) against the cumulative percentage of national income received by that share of the population (on the vertical axis). Cumulative income shares are built up by adding each quintile’s share in turn. For example, Kolvara’s cumulative shares are $4\%, 12\%, 26\%, 48\%, 100\%$ (at $20\%, 40\%, 60\%, 80\%, 100\%$ of the population respectively), and Estenia’s are $10\%, 24\%, 42\%, 64\%, 100\%$. Plotting and joining these points for each country traces out its Lorenz curve.

This is compared with the line of perfect equality. The 45-degree diagonal along which the cumulative population share always exactly equals the cumulative income share (e.g. the poorest 40% of people always receive exactly 40% of income). The further a country’s Lorenz curve bows away (down and to the right) from this diagonal, the more unequal its income distribution.

Since Estenia’s cumulative income shares are consistently closer to the corresponding population shares than Kolvara’s (e.g. 24% versus 12% for the poorest 40%, and its curve would similarly stay closer to the diagonal at every other point), Estenia’s Lorenz curve would lie closer to the line of perfect equality.

Part (c): Comparing Gini coefficients

The Gini coefficient summarises a Lorenz curve as a single number: the area between the Lorenz curve and the line of perfect equality, divided by the total area under the line of perfect equality. It ranges from 0 (perfect equality) to 1 (perfect inequality), the further the Lorenz curve bows away from the diagonal, the larger this area, and the higher the Gini coefficient.

Since Kolvara’s Lorenz curve bows further away from the line of perfect equality than Estenia’s (as shown in part (b)), Kolvara would have the higher Gini coefficient (indicating a more unequal distribution of income. This is confirmed directly by the table: Kolvara’s poorest fifth receives only 4% of income (compared with Estenia’s 10%), while Kolvara’s richest fifth takes 52% (compared with Estenia’s 36%)) income in Kolvara is far more concentrated among the richest, and far more scarce among the poorest, than in Estenia.

Part (d): A limitation of income-distribution data

Income-distribution measures, including the Gini coefficient, describe only how income is shared out (they say nothing about the overall level of income or living standards. It would be entirely possible for a very poor country, where almost everyone has a very low income, to have a low Gini coefficient (income shared out fairly equally) despite widespread poverty and low living standards; conversely, a much richer country could have a higher Gini coefficient yet still have a poorer, absolutely-speaking, population have a higher standard of living than the “equal” poor country. To judge a country’s overall level of economic development, distribution data therefore needs to be combined with other indicators) such as real GNI per capita to capture the average level of income, and non-monetary indicators such as life expectancy and education to capture aspects of development that income data (distributed evenly or not) cannot show on its own.

Final answers

• (a) Poorest 40% receive 12% of income in Kolvara and 24% in Estenia
• (b) Estenia’s Lorenz curve lies closer to the line of perfect equality
• (c) Kolvara has the higher Gini coefficient (more unequal distribution)
• (d) Income-distribution data says nothing about the overall LEVEL of income/living standards, so it must be combined with other indicators to assess development