• The Gini index (or Gini coefficient) is a common summary measure of income (or wealth) inequality within a population. It ranges from 0 (perfect equality — everybody has the same income) to 1 (perfect inequality — one person has all the income). It is widely used by economists, policymakers and researchers to compare inequality across countries and over time. (Source: Investopedia; World Bank)
Key takeaways
– Definition: Gini = 0 means perfect equality; Gini = 1 means perfect inequality. Most published values are reported as percentages (0–100). (Investopedia)
– Interpretation: A higher Gini implies greater inequality, but it does not tell you where in the income distribution inequality is concentrated. (Investopedia)
– Global context: Long-run estimates show global inequality rose substantially from the 19th century into the late 20th century; modern estimates place the global Gini near 0.67 (67%) in 2020. (World Inequality Lab; Investopedia)
– Recent shocks: Pandemics such as COVID-19 tend to increase Gini measures; the World Bank estimates COVID-19 raised global Gini by about 0.5 percentage points from 2019 to 2020 and that major epidemics can raise Gini by ~1.5 points over five years. (World Bank; Investopedia)
– Limitations: Gini depends on data quality and can hide distributional detail (different distributions can share the same Gini). Wealth Ginis are usually higher than income Ginis. (Investopedia)
How the Gini index works (intuition and formula)
– Intuition: The Gini compares the cumulative share of income received by cumulative population percentiles. If everyone receives exactly the same income, the cumulative income line follows the 45-degree “line of perfect equality.” Deviations from that line reflect inequality.
– Lorenz curve: Plot population percentiles on the x-axis and cumulative income share on the y-axis. The Lorenz curve shows the actual distribution. The further the Lorenz curve is below the line of perfect equality, the higher the Gini.
– Basic formula (conceptual):
• Gini = area between line of equality and the Lorenz curve divided by the total area under the line of equality.
• Algebraically, for discrete income shares: G = 1 − 2 * (area under the Lorenz curve), or computed from sorted income data using standard summation formulas.
– Practical computation steps (simple, discrete dataset):
1. Sort individuals/households by income from lowest to highest.
2. Compute cumulative population shares and cumulative income shares.
3. Use trapezoidal integration on the Lorenz curve to estimate the area under the curve, then apply Gini = 1 − 2 * area.
4. Many statistical packages (Stata, R, Python’s inequality packages) compute Gini directly and provide standard errors for survey data. (Investopedia)
Visualizing the Gini index: Understanding the Lorenz curve
– The Lorenz curve provides the full shape of inequality: you can see whether inequality is driven by a large share at the top, by a long low-income tail, or both.
– Example reading:
• If the bottom 50% of the population earns 20% of total income, while the top 10% earns 40%, the Lorenz curve will bend markedly below the equality line.
– Use the Lorenz curve with the Gini but not instead of it: the curve shows distributional nuances that a single-number Gini cannot.
A global perspective on the Gini index
– Historical trend: Global Gini estimates suggest inequality rose in the 19th and 20th centuries (e.g., ~0.50 in 1820 to ~0.657 in 1980–1992), and recent work estimates a global Gini of about 0.67 in 2020. (World Inequality Lab; Investopedia)
– Country variation: Some of the world’s poorest countries show very high Ginis, while many developed European countries show relatively low Ginis. But GDP per capita and Gini are not tightly coupled — a middle-income country can have similar Gini to a rich country. (Investopedia; OECD)
Global Gini — headline numbers
– World (long-run): ~0.50 in 1820; ~0.657 in 1980/1992; ~0.67 in 2020 (World Inequality Lab).
– Effect of pandemics: World Bank estimates a ~0.5 percentage-point rise globally from 2019 to 2020 due to COVID-19; major epidemics can raise Gini ~1.5 points over five years. (World Bank; Investopedia)
Country-by-country Gini coefficient insights
– Highest reported: South Africa is commonly reported as having the highest income inequality among countries with good data — about 63.0% (0.63) per World Bank figures. Causes cited include racial and geographic disparities and labor market segmentation. (World Bank; World Population Review; Investopedia)
– Lowest reported: Some Nordic countries (e.g., Norway) frequently report low Ginis; Investopedia notes Norway around 22.7% in referenced data.
– United States: The U.S. has a Gini around 39.8% (0.398) per the World Bank — high for a developed economy. Explanations commonly include technological change, globalization, decline of unions, and stagnant real value of the minimum wage. (World Bank; Investopedia)
Frequently asked questions
– What country has the highest Gini index?
• South Africa is frequently cited as the country with the highest income Gini (about 63.0% in the World Bank’s reporting). (World Bank; Investopedia)
– What does a Gini index of 50 mean?
• A Gini of 50 (0.50) is a high level of inequality — roughly a midpoint of the 0–100 scale and in practice indicates that income is unequally distributed. As of 2024, only a minority of countries (about 14) have a national Gini ≥ 50. (Investopedia)
– Is the U.S. Gini coefficient high or low?
• The U.S. Gini (~39.8%) is high relative to most other advanced economies. It is lower than the very highest global values but higher than many European peers. (World Bank; Investopedia)
Understanding the limitations of the Gini index
– Data quality: Gini depends on accurate income or wealth data. Informal economies, under-reporting, and tax havens can bias estimates (often overstating inequality when large informal incomes are missed at the low end, or understating wealth concentration when top wealth is hidden). (Investopedia)
– Income vs. wealth: Most Ginis measure income; wealth Ginis are typically far higher and harder to measure. (Investopedia)
– Loss of detail: The Gini reduces a complex distribution to one number. Two countries with different distribution shapes can share the same Gini. The Lorenz curve and other measures (e.g., top 1% share, Palma ratio, Theil index) are complementary. (Investopedia)
– Demographics and policy context: Age composition (large retired populations), household size, tax and transfer systems, and data definitions (pre- vs post-tax, equivalized incomes) materially affect comparability. (Investopedia)
Practical steps — For policymakers
1. Measure well and transparently
• Use high-quality household surveys and administrative tax data. Publish pre- and post-tax/transfer Ginis. Adjust for household size (equivalence scales). (Investopedia)
2. Disaggregate the analysis
• Report Gini by region, urban/rural, age group, gender, ethnicity, and income source to identify where inequality is concentrated. (Investopedia)
3. Use multiple indicators
• Combine Gini with top-income shares, poverty rates, Palma ratio, and Theil index to get a fuller picture. (Investopedia)
4. Design targeted policy levers
• Progressive taxation and closing tax loopholes; targeted cash transfers; strengthening minimum wages and collective bargaining; investing in education and job-training; expanding access to quality healthcare and childcare. (Investopedia)
5. Track effects and adjust
• Evaluate policies’ distributional impacts and update policies based on observed effects. Consider short-term shocks (pandemics) and build resilience into social safety nets. (World Bank; Investopedia)
Practical steps — For analysts & researchers
1. Choose the right income concept
• Decide pre-tax vs post-tax income, market income vs disposable income, and whether to use equivalized household income. (Investopedia)
2. Use robust statistical methods
• Apply sampling weights, bootstrap standard errors, and account for top-income undercoverage (e.g., combine survey and tax data). (Investopedia)
3. Decompose changes
• Use decomposition methods to attribute Gini changes to factors (labor income, capital income, demographic change, taxes/transfers). (Investopedia)
4. Report uncertainty and comparability
• Provide confidence intervals and document data sources and definitions when comparing across countries or over time. (Investopedia)
Practical steps — For advocates and the public
1. Interpret Gini carefully
• Understand that Gini is a starting point: ask who wins and loses, where inequality is rising, and what policy levers exist. (Investopedia)
2. Use data to advocate
• Push for transparent, frequent reporting of inequality measures; support policies shown to reduce inequality where appropriate (e.g., targeted social programs). (Investopedia)
3. Follow complementary metrics
• Monitor poverty rates, median vs mean income, and top-income shares to get a broader view. (Investopedia)
The bottom line
– The Gini index is a useful, widely used single-number summary of income or wealth inequality, but it must be interpreted with caution. It provides a convenient way to compare inequality across countries and over time, and to monitor trends after policy changes or shocks (like pandemics). However, data limitations and the loss of distributional detail mean the Gini should be used alongside other indicators and disaggregated analysis to inform policy and public debate. (Investopedia; World Bank; World Inequality Lab)
Sources and further reading
– Investopedia: “Gini Index” (Ellen Lindner) — explanatory overview and examples.
– World Bank — country Gini data, analyses of pandemic impacts on inequality.
– World Inequality Lab — global inequality estimates (long-run series and 2020 estimates).
– CIA World Factbook — country-level Gini data compilations.
– World Population Review — commentary on country-specific inequality drivers.
Editor’s note: The following topics are reserved for upcoming updates and will be expanded with detailed examples and datasets.