How much money you earn often has less to do with how hard you work and more to do with where you were born. A software engineer in Bengaluru and a software engineer in San Francisco may write similar code, yet their incomes can differ by a factor of ten. This simple observation sits at the heart of economic inequality, one of the most studied and debated subjects in development economics. To understand it properly, we need to define what economic inequality actually means and learn the tools economists use to measure it. This post walks through global income disparities, the Gini coefficient and the Lorenz curve, and where India stands when these numbers are applied.
Table of Contents
What economic inequality means
Economic inequality refers to the unequal distribution of income, wealth, or consumption among individuals or groups within a society. It is important to separate these three ideas because they tell different stories. Income is the flow of earnings a person receives over a period, such as wages, business profits, or returns on investments. Wealth is the total stock of assets a person owns, like property, gold, shares, and savings, minus their debts. Consumption is what a person actually spends to meet their needs. A household can have a modest income but high inherited wealth, or a low income but stable consumption thanks to government support. Most measurement tools focus on income or consumption because these are easier to survey than wealth.
The reason economists care so much about measuring inequality is that it shapes the development trajectory of a country. High inequality can limit social mobility, weaken demand in the economy, and create political tension. Measuring it accurately is the first step toward addressing it.
Global disparities in income
When we zoom out from a single country to the whole world, the income gaps become enormous. The economist Branko Milanovic, formerly lead economist at the World Bank, has spent decades documenting this. His work shows that a person’s country of residence is the single largest factor explaining where they fall in the global income distribution. In other words, the lottery of birthplace matters more than effort, education, or talent in determining a person’s global income rank.
Comparing incomes across countries is technically difficult. A person earning a certain number of rupees in India and another earning dollars in the United States cannot be compared directly using market exchange rates, because the cost of living differs sharply. This is why economists convert all incomes into Purchasing Power Parity (PPP) dollars. PPP adjusts for what money can actually buy in each country, so that a basket of goods costs the same number of PPP dollars everywhere. Without this adjustment, the incomes of people in poorer countries would look even smaller than they really are in terms of living standards.
Milanovic’s research on the global income distribution between 1988 and 2008 produced a striking finding. The biggest winners of that period were the global top 1 per cent and the emerging middle classes of countries like China, India, Indonesia and Brazil. The group that gained the least sat around the 80th percentile of global income, which largely included the lower middle classes of rich Western nations. His recalculated estimates using PPP data placed global inequality at around 70 Gini points, far higher than the inequality found within any single country. The richest tenth of the world’s population was found to receive well over half of all global income.
The Gini coefficient and the Lorenz curve
To measure inequality precisely, economists need a single number that captures how income is spread across a population. The two tools that do this job are the Lorenz curve and the Gini coefficient, and they are closely linked.
The Lorenz curve
The Lorenz curve, developed by the American economist Max Lorenz in 1905, is a graph that shows how income is distributed. To build it, you line up everyone in the population from poorest to richest. Then you plot the cumulative share of total income earned against the cumulative share of the population. For example, you ask: what share of total income do the bottom 20 per cent earn? What about the bottom 40 per cent? And so on, until you reach 100 per cent of the population earning 100 per cent of the income.
If income were shared perfectly equally, the bottom 20 per cent of people would earn exactly 20 per cent of income, the bottom 50 per cent would earn 50 per cent, and the graph would be a straight diagonal line at 45 degrees. This is called the line of equality. In reality, the poorest people earn a much smaller share than their numbers suggest, so the actual Lorenz curve sags below this diagonal. The more it sags, the more unequal the society.
The Gini coefficient
The Lorenz curve gives a picture, but policymakers often want a single number. This is where the Gini coefficient comes in, named after the Italian statistician Corrado Gini who developed it in 1912. It is derived directly from the Lorenz curve. Look at the area between the line of equality and the actual Lorenz curve, and call it A. Call the area beneath the Lorenz curve B. The Gini coefficient is the ratio A divided by (A plus B).
The result is a value between 0 and 1, sometimes expressed on a scale of 0 to 100. A Gini of 0 means perfect equality, where everyone earns exactly the same and the Lorenz curve lies right on the line of equality. A Gini of 1 means perfect inequality, where one person holds all the income and everyone else has nothing. The higher the number, the more unequal the distribution. In practice, advanced economies usually record disposable income Gini values between about 0.25 and 0.45, while countries with weaker tax and welfare systems tend to sit higher.
The Gini coefficient is popular because it summarises a whole distribution in one figure that is easy to compare across countries and over time. But it has real limitations. Because it measures relative differences, it can stay unchanged even when the top 1 per cent pulls far ahead, since the curve compresses the very top. It also says nothing about absolute living standards, so a country can keep a stable Gini while poverty worsens. For this reason, the Gini is often read alongside other measures, such as the income share of the top 10 per cent or the ratio between the richest and poorest groups.
Case study: inequality in India
India offers a fascinating and somewhat confusing case, because different measures tell sharply different stories. This is a perfect illustration of why understanding the method behind a number matters as much as the number itself.
The consumption picture
According to the World Bank, India’s Gini Index stands at 25.5, which places it among the most equal countries in the world in relative terms. By this measure India looks more equal than China, which scores around 35.7, and the United States at about 41.8. This figure declined from 28.8 in 2011-12 to 25.5 in 2022-23, suggesting steady improvement.
The catch is that this figure is based on a consumption survey, not an income survey. India has long collected data on household consumption spending rather than household income, a practice dating back to the National Sample Survey work of earlier decades, because measuring income in a largely informal and agricultural economy is extremely difficult. Consumption tends to be far more evenly spread than income. A wealthy family does not eat ten times more food than a poor family even if it earns ten times more, and government welfare schemes, free food grain distribution, and informal support all smooth out spending at the bottom. The World Bank itself notes that India’s consumption Gini may understate inequality due to data limitations, and that consumption-based figures are not directly comparable to the income-based figures most rich countries report.
The income and wealth picture
When you switch to income data, the story reverses completely. The World Inequality Database reports that India’s income Gini rose from 52 in 2005 to around 61 in 2023, indicating sharply rising disparity. The same source notes that the median earnings of the top 10 per cent of workers were about 13 times those of the bottom 10 per cent in 2023-24.
The World Inequality Report 2022, coordinated by economists including Thomas Piketty and Lucas Chancel, painted an even starker picture. It found that the top 10 per cent and top 1 per cent held roughly 57 per cent and 22 per cent of national income respectively, while the bottom 50 per cent’s share had fallen to around 13 per cent. The report described India as a poor and very unequal country with an affluent elite, and linked the surge in inequality to the deregulation and liberalisation policies pursued since the mid-1980s. Wealth is even more concentrated than income, with the richest 1 per cent estimated to hold around 40 per cent of the country’s total wealth.
Reading the two numbers together
So which number is right? Both are, because they measure different things. The consumption Gini of 25.5 tells us that basic spending is distributed fairly evenly, partly because of welfare programmes and the modest spending capacity of most households. The income Gini of around 61 tells us that the underlying capacity to earn is deeply unequal, with a small elite capturing a large share of national income. The gap between these two figures is itself a finding. It reveals an economy where redistribution and subsidies keep consumption from diverging as sharply as income, but where the income gap continues to widen at the extremes. Anyone studying inequality should treat a single Gini figure with caution and always ask what it is measuring and how the data was collected.
Why these measures matter
Understanding the meaning and measures of economic inequality is not an academic exercise. Policymakers use the Gini coefficient to track whether growth is being shared, to design tax and welfare policy, and to compare progress against other nations. Journalists and citizens use these numbers to hold governments accountable. But as the Indian case shows, a number is only as useful as your understanding of the method behind it. A consumption-based Gini and an income-based Gini can point in opposite directions for the same country in the same year. The careful student of development learns to read the footnotes, not just the headline figure.
What do you think? If consumption inequality in India is falling while income inequality is rising, which figure should guide government policy on welfare and taxation? And do you think the lottery of birthplace, rather than individual effort, is a fair explanation for why incomes differ so much across the world?
References
- https://www.imf.org/external/pubs/ft/fandd/2011/09/milanovic.htm
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1478814
- https://ourworldindata.org/what-is-the-gini-coefficient
- https://www.pib.gov.in/PressNoteDetails.aspx?NoteId=154837&ModuleId=3®=3&lang=2
- https://documents1.worldbank.org/curated/en/099722104222534584/pdf/IDU-25f34333-d3a3-44ae-8268-86830e3bc5a5.pdf
- https://en.wikipedia.org/wiki/Income_inequality_in_India
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