Name

GINI INDEX OF INCOME INEQUALITY

Brief definition

A summary measure of the extent to which the actual distribution of income, consumption expenditure, or a related variable, differs from a hypothetical distribution in which each person receives an identical share.

Unit of measure

A dimensionless index scaled to vary from a minimum of zero to a maximum of one; zero representing no inequality and one representing the maximum possible degree of inequality.

Type of Indicator

State

Objective

The Gini Index provides a measure of income or resource inequality within a population. It is the most popular measure of income inequality.

Importance with respect to desertification

This indicator is particularly relevant to the equity component of sustainable development. Income or resource distribution have direct consequences on the poverty rate of a country or region, that affects the capacity of community to respond with respect to desertification. From the equity perspective and for the success of the measures to combat against desertification it is important assess their impacts on the most vulnerable part of the community.

International Conventions and agreements

The UNCCD emphasizes the importance of equity in combating desertification.

Secondary objectives of the indicator

The Gini index represents a fundamental indicator for national decision- makers.

Definitions and basic concepts

The Gini Index measures the area between the Lorenz Curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line of perfect equality (see Figure 1). The Gini Index is defined as one half of the average value of the absolute differences between all possible pairs of "incomes".

Benchmarks Indication of the values/ranges of value

Methods of measurement

The Lorenz Curve plots the cumulative percentages of total income received (on the vertical axis) against the cumulative percentage of recipients, starting with the poorest individual or household (see Figure 1).

Figure 1: The Lorenz Curve and Gini Index of Income

There are a number of choices about data, which can influence the precise value of the Gini Index obtained. For example, a Gini Index for consumption expenditure will typically be lower in value than one for income, even within the same population. This is because households smooth their consumption over time in response to income changes. At any one date, there will be some households with unusually low incomes and others with unusually high ones; with some opportunities for saving and/or borrowing. Thus, household consumption will be less unequal.

It is important how "income" is measured, for example whether it is total household income or per capita household income, or income per equivalent adult. In addition, it matters whether or not the incomes are weighted by household size, since households with lower income per person tend to be larger. Thus, the income share of the poorest 20% of households will be higher than the income share of the poorest 20% of persons.

The World Bank, for example, prefers to weight by household size and calculate the shares held by persons rather than households for most purposes. As a general rule, the Bank also considers consumption expenditure to be the more reliable indicator of welfare than income, which can be excessively variable over time, and is also more difficult to measure accurately, particularly in developing countries. Looking at the sample of 112 countries for which Gini indices of income are reported in the World Bank's 2000 World Development Indicators, this coefficient ranges from a low of 19.5% to a high value of 62.9%.

There are a number of ways of estimating the Gini Index of income, and the choice depends in part on the type of data available. Distributional data are often available in grouped form, such as the income share of the lowest decile of households, where households are ranked by income per person. To estimate the Lorenz Curve, and thus the Gini Index, from such data, the World Bank often uses a software package called POVCAL. Having specified the type of data, the program calculates both the General Quadratic specification for the Lorenz Curve and the Beta specification. It then calculates the Gini Index and various other statistics, including poverty measures for each Lorenz Curve. The program also advises which is the better specification for the Lorenz Curve for the specific data used.

Limits of the indicator

The Gini Index is not a very discriminating indicator. Two very different distributions - one having more inequality amongst the poor, the other having more amongst the rich can have exactly the same Gini Index. Measurement errors in data sets are thought to be greater for incomes compared to consumption expenditure, which will add to measured inequality. Differences between countries in the measured Gini index may thus reflect in part differences in the welfare measures used. While the Gini Index of income (in common with most other measures of inequality) captures information on the pattern of relative levels of wellbeing in the population, it is independent of any considerations of absolute living standards. So there is nothing to guarantee that a lower Gini Index of income entails higher social welfare in any agreed sense, since the mean income may have also fallen. The Gini Index is at best a partial indicator, and other measures will be needed to complete the picture of how levels of economic welfare are evolving in a society. It should be noted that there are several comparability problems across countries in the use of data from household surveys. These problems are diminishing over time as survey methodologies are improving and becoming more standardized, but they remain. There are many other measures of inequality, with various strengths and weaknesses. These are discussed in Sen (1973)

Linkages with other indicators

This indicator is linked to several other sustainable development measures, including the poverty indicators, Gender Equality Indicators, GDP per capita, population dynamics in rural areas and sustainable development strategies.

Data required to calculate the indicator

Statistics of household size and total household income or per capita household income or income per equivalent adult.

Data sources

The most important source of data on living standards is household surveys. The results of these surveys can be obtained from government statistical agencies, often via published reports. About two thirds of the developing countries have done sample household surveys which are representative nationally, and some (but certainly not all) of these provide high quality data on living standards.

Availability of data from national and international sources

Data can also be obtained from government statistical agencies and international agencies such as The World Bank and from the Statistical Office of the European Union (Eurostat), the Luxembourg Income Study, or the Organisation for Economic Co-operation and Development (OECD).

Main institutions responsible

The lead agency is the World Bank (WB).

The contact point is the World Development Indicators Team, Development Data Group, the World Bank; fax no. (1 202) 522-1785.

Other contributing organizations

Universities of Lisbon, Murcia, Basilicata, Athens, Amsterdam, Leeds

Bibliography

The World Bank. World Development Indicators. Draft Report. 1996.

Other references

Chen, S., G. Datt, M. Ravallion. POVCAL: A Program for Calculating Poverty Measures from Grouped Data. Poverty and Human Resources Division, Policy Research Department, Washington DC: World Bank. 1992.

Ravallion, M., and S. Chen. What Can New Survey Data Tell Us About Recent Changes in Living Standards in Developing and Transitional Economies?. Working Paper 1. Research Project on Social and Environmental Consequences of Growth-Oriented Policies, Washington DC: World Bank.

Sen, A. On Economic Inequality. Oxford: Oxford University Press. 1973.

Contacts Name and address

University of Basilicata
Prof Giovanni Quaranta
email: quaranta@unibas.it