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The Gini coefficient, or Gini index, measures the distribution of income across a population. It is one of the most widely used measures of income inequality, and its characteristics make it useful for comparisons over time, between countries, and before or after taxes and benefits. The coefficient ranges from zero (or 0%) to one (or 100%), with higher values representing higher inequality. A value of zero represents perfect equality (i.e., everyone in the population has the same income), and one represents perfect inequality (i.e., one person in the population earns all of the income). Theoretically, values over 1 are possible due to negative income or wealth.
The World Bank and the UN both produce annual statistics on the Gini coefficient, and many governments use it to track income inequality in their country. For example, the US Census Bureau has released a number of reports on the Gini coefficient, and the UK’s Office for National Statistics (ONS) produces an annual report highlighting historical statistics of the Gini coefficient. Global inequality, measured by the Gini coefficient, has increased over recent centuries and spiked during the Covid-19 pandemic. The coefficient showed sustained growth during the nineteenth and twentieth centuries. In 1820, the global Gini coefficient was estimated to be 0.50, and in 1980, it rose to 0.657.
The coefficient was developed by the Italian statistician Corrado Gini in 1912, building on previous work by American economist Max Lorenz in 1905. The Gini coefficient is found by measuring the difference between the Lorenz curve (the observed cumulative income distribution) and the notion of perfectly equal income distribution.
Representation of the Lorenz curve, and perfect equality/inequality.
A similar analysis can be applied to the total distribution of wealth, calculating the "wealth Gini coefficient." However, wealth is more difficult to measure than income; therefore, more focus is placed on income distributions. Wealth Gini coefficients tend to be much higher than the standard Gini coefficient based on income. With Gini coefficients measuring net income and not net worth, a population may have relatively equal income (low Gini coefficient) while wealth is still concentrated in a relatively small number of people. Other limitations mean the Gini may overstate income inequality, and reducing the overall income distribution to a single value can obscure important information. It is also critical to remember it describes the distribution within a population, not absolute wealth. A low- and high-income country may have similar Gini coefficients even though the GDP per person is considerably different.
The Gini coefficient is calculated by comparing the area above the Lorenz curve to the area below it. The Lorenz curve plots the cumulative share of income against the cumulative population. Real-life Lorenz curves lie between two extreme cases (shown in the diagram above):
- Complete equality—total income is shared equally across the population, i.e., everyone has the same income (represented by a Lozenz curve that is a straight diagonal line)
- Complete inequality—total income is earned by only a single person in the population, i.e., one person earns all of the income with the rest earning nothing (represented by a Lorenz curve that follows the horizontal and right-hand axis)
The Gini coefficient for a given population is the area between the Lorenz curve and the diagonal line of complete equality expressed as a percentage of the complete area below the line of equality. It effectively defines where a real-life Lorenz curve falls between the two extreme cases. Therefore, complete equality would produce a Gini coefficient of zero (i.e., there is no area between the Lorenz curve and the line of complete equality), and complete inequality would produce a Gini coefficient of one (i.e., the maximum area between the Lorenz curve and line of complete equality).
The Gini coefficient can be calculated using the following formula:
Where A is the area between the Lorenz curve and the line of equality and B is the area below.
The two areas required to calculate the Gini coefficient, in relation to the line of equality and the Lorenz curve.
The Lorenz curve was developed by American economist Max Lorenz in 1905. It is a graphical representation of income inequality or wealth inequality. Italian statistician Corrado Gini built on this work to develop the Gini coefficient. The coefficient was first demonstrated in his 1912 book published under the Italian name Variabilità e Mutabilità, which translates to Variability and Mutability. Gini proposed no less than thirteen formulations of his coefficient index.
The World Bank tracks Gini coefficients for countries based on primary household survey data from government statistical agencies and World Bank country departments. The World Bank's Poverty and Shared Prosperity 2020 report shows the Gini coefficient increases by around 1.5% in the five years following major epidemics, such as H1N1 (2009), Ebola (2014), and Zika (2016). The effects of the COVID-19 pandemic are being calculated, and estimates predict an increase in Gini coefficient of 1.2-1.9 percentage points per year for 2020 and 2021, signaling an increase in income inequality.
With a Gini coefficient of 63% (latest measurement in 2014), South Africa is the country with the highest income inequality in the world. This number has dropped from a Gini coefficient of 65% in 2005. Further analysis shows the richest 10% in South Africa hold 71% of the wealth, with the poorest 60% holding only 7% of the wealth. Based on the most recent World Bank data available for each country as of 2022, the top ten countries with the highest Gini coefficients are
- South Africa (2014)—63%
- Namibia (2015)—59.1%
- Suriname (1999)—57.9%
- Zambia (2015)—57.1%
- Sao Tome and Principe (2017)—56.3%
- Central African Republic (2008)—56.2%
- Eswatini (2016)—54.6%
- Mozambique (2014)—54%
- Brazil (2019) —53.4%
- Belize (1999)—53.3%
The top 10 countries with the lowest Gini coefficients are
- Slovenia (2018)—24.6%
- Czech Republic (2018)—25% (tied for 2nd)
- Slovakia (2018)—25% (tied for 2nd)
- Belarus (2019)—25.3%
- Moldova (2018)—25.7%
- United Arab Emirates (2018)—26%
- Iceland (2017)—26.1%
- Azerbaijan (2005)—26.6% (tied for 8th)
- Ukraine (2019)—26.6% (tied for 8th)
- Belgium (2018)—27.2%
The countries with the lowest Gini coefficient are dominated by Nordic and Central European countries taking up seven of the top ten. Gini coefficients show, generally, inequality is lower in Europe than elsewhere in the world. The latest data from the World Bank shows the United States with a Gini coefficient of 41.5 (2019).
Global Gini coefficients based on World Bank Data.
The Office for Economic Co-Operation and Development (OECD) tracks the Gini coefficient for its member states.
OECD Gini coefficient data.
The 2019, US census measured the country's Gini coefficient to be 0.4811 +/- 0.0006. The census also calculated the Gini coefficient for each state and region, such as Washington DC and Puerto Rico. The regions with the highest Gini coefficient were found to be
- Puerto Rico—0.55
- New York—0.51
- District of Columbia—0.51
- Connecticut—0.5
- Louisiana—0.5
Despite its universality and scalability, the Gini coefficient has limitations:
- Sample bias—The validity of the final value is dependent on sample size. This can be difficult to find a representative sample for a given country or region's population.
- Oversimplification—The same Gini coefficient can represent very different income distributions or structural changes in a population.
