Measuring Income Inequality: Methods and Indicators

Every year, headlines announce India’s GDP growth rate, but growth alone doesn’t tell you who actually benefits from it. A country can grow richer while the gains pile up almost entirely at the top. To capture that gap, economists rely on two classic tools: the Lorenz Curve and the Gini Coefficient. Together, they turn a messy, real-world income distribution into a picture and a number you can actually compare across time and countries.

Why measuring inequality matters 🔗

Income inequality isn’t just an academic curiosity. It shapes consumption patterns, savings, access to education and healthcare, and even political stability. Two countries can have identical average incomes yet very different lived realities, depending on how that income is spread across the population. Before policymakers can design welfare schemes, tax structures, or minimum wage laws, they need a reliable way to measure how unequal the distribution actually is. That is exactly the gap the Lorenz Curve and Gini Coefficient fill.

The Lorenz curve: mapping who gets what 🔗

The Lorenz Curve, developed by American economist Max O. Lorenz in 1905, is a graphical way of showing how income (or wealth) is distributed across a population. It plots the cumulative percentage of the population, ranked from poorest to richest, on the horizontal axis against the cumulative percentage of income they hold on the vertical axis, as explained by Economics Help.

How the curve is constructed 🔗

To build a Lorenz Curve, the population is usually divided into quintiles (groups of 20%) or deciles (groups of 10%). For each group, you calculate what share of total national income it holds, then plot the cumulative totals. A simplified example might look like this:

Reading the curve 🔗

Plotted against this data is a straight 45-degree diagonal line, called the line of perfect equality. On this line, the bottom 20% of the population would hold exactly 20% of income, the bottom 60% would hold 60%, and so on. In reality, the actual Lorenz Curve almost always sags below this diagonal, because income is rarely spread evenly. The further the curve bows away from the diagonal, the greater the inequality. If, hypothetically, one person held all the income and everyone else held none, the curve would collapse into a right angle along the bottom and right edges of the graph, representing complete inequality.

The Lorenz Curve is also useful for comparisons over time. If a country’s curve shifts further away from the diagonal across a decade, that’s a visual signal that inequality is rising, even before you calculate a single number.

The Gini coefficient: one number for the whole picture 🔗

While the Lorenz Curve is great for visualising inequality, it’s hard to compare curves at a glance, especially across many countries or years. That’s where the Gini Coefficient comes in. Developed by Italian statistician Corrado Gini in 1912, it condenses the entire Lorenz Curve into a single figure, as noted by Drishti IAS.

The formula behind the number 🔗

The Gini Coefficient is calculated as the ratio of the area between the line of perfect equality and the actual Lorenz Curve (commonly labelled Area A), to the total area under the line of perfect equality (Area A plus Area B). In simple terms, it measures how much the actual distribution deviates from perfect equality, expressed as a proportion. The Press Information Bureau describes it as the gap between the Lorenz Curve and the line of absolute equality, expressed as a percentage of the maximum possible area under that line.

What the scale means 🔗

The Gini Coefficient ranges from 0 to 1 (or 0 to 100 when expressed as a percentage):

• 0 (or 0%) represents perfect equality, where every individual earns exactly the same income.
• 1 (or 100%) represents complete inequality, where a single individual holds all the income and everyone else has none.

In practice, no real economy sits at either extreme. Most countries fall somewhere between 0.25 and 0.65, with lower values generally associated with stronger welfare systems and more progressive taxation.

What the numbers actually say about India 🔗

India’s inequality numbers currently present something of a puzzle, and it’s worth understanding why. According to World Bank data, India’s consumption-based Gini Index stood at 25.5 in 2022, placing it among the more equal societies globally by this particular measure. That figure is drawn from household consumption expenditure surveys rather than direct income data.

However, income-based estimates tell a starkly different story. Research from the World Inequality Lab found that the top 1% of Indian earners captured 22.6% of national income in 2022-23, the highest share recorded since data collection began in 1922, higher even than during colonial rule. The same period saw the top 1% hold roughly 40% of the country’s total wealth. Reporting on the World Inequality Report 2026 in the Deccan Herald noted that the income gap between the top 10% and bottom 50% of Indians has remained wide and largely unchanged over the past decade.

So which is it: relatively equal, or deeply unequal? The honest answer is that both figures are technically correct, but they’re measuring different things. Consumption-based Gini figures tend to understate true inequality because they miss how the very rich actually spend and invest, while income and wealth-based figures from tax and survey data capture the concentration at the top more accurately. This is a key reason economists increasingly look beyond a single Gini figure when assessing a country’s economic fairness.

Other ways economists track inequality 🔗

The Lorenz Curve and Gini Coefficient are the most widely taught measures, but they aren’t the only ones. A few complementary indicators are commonly used alongside them:

• Income share of top percentiles: Directly tracks what proportion of national income goes to groups like the top 1% or top 10%, which is particularly useful for spotting concentration at the very top that the Gini Coefficient can sometimes smooth over.
• Palma ratio: Compares the income share of the richest 10% to that of the poorest 40%, focusing attention on the two ends of the distribution rather than the middle.
• Decile dispersion ratio: Compares the average income of the richest 10% of households to the poorest 10%, giving a simple multiple that’s easy to communicate.

These measures don’t replace the Gini Coefficient; they add context that a single number can miss.

The limits of these measures 🔗

Both tools have well-documented shortcomings, especially in a country like India. Household surveys often underreport the incomes of both the very rich, who are harder to survey accurately, and informal sector workers, whose earnings are irregular and undocumented. Since a large share of India’s workforce operates informally, this creates blind spots in the data. The Gini Coefficient is also silent on non-monetary dimensions of inequality, such as unequal access to education, healthcare, or digital connectivity, which shape real living standards just as much as income does. It also doesn’t distinguish between inequality driven by hard work and enterprise versus inequality driven by inherited wealth or market power, a distinction that matters a great deal for policy design.

Why these numbers matter beyond the classroom 🔗

These aren’t just textbook concepts. Gini and Lorenz-based analysis feeds directly into how governments design redistribution policies, from progressive income tax slabs to targeted subsidies and direct benefit transfers. When inequality measures rise, it typically prompts a policy response: expanding financial inclusion programmes, strengthening social security nets, or revisiting tax structures on wealth and capital gains. Understanding how these numbers are built also makes you a more critical reader of the inequality debates that regularly show up in Indian budget discussions and economic surveys.

What do you think? Given that consumption-based and income-based measures of inequality can paint such different pictures of the same economy, which one do you think policymakers should prioritise when designing welfare schemes? And do you think a single number like the Gini Coefficient can ever fully capture something as complex as economic fairness?

References

1. https://www.economicshelp.org/blog/glossary/lorenz-curve/
2. https://www.drishtiias.com/daily-updates/daily-news-analysis/india-becomes-4th-most-equal-country-globally
3. https://www.pib.gov.in/PressNoteDetails.aspx?NoteId=154837&ModuleId=3&reg=48&lang=2
4. https://data.worldbank.org/indicator/SI.POV.GINI?locations=IN
5. https://wid.world/www-site/uploads/2024/03/WorldInequalityLab_WP2024_09_Income-and-Wealth-Inequality-in-India-1922-2023_Final.pdf
6. https://www.deccanherald.com/business/india-now-more-unequal-than-in-raj-era-2945653