The Gini Coefficient: How income inequality is measured and understood.

Income inequality is one of those topics that sneaks up on you. You notice it when you walk through a city and see both luxury cars and empty storefronts, or when you hear about gaps in wages between workers in the same country. But to talk about it sensibly, you need a clear, simple way to measure it. In economics, that measuring stick most people reach for is the Gini coefficient. Let’s unpack what it is, why it matters, and how it fits into the bigger picture of economic policy.

What is the Gini coefficient, in plain language?

Think of a country’s income distribution as a slice of cake. If everyone gets an equal share, the slice is perfectly even. If one person controls almost all the cake and everyone else gets crumbs, the distribution is highly unequal. The Gini coefficient turns that distribution into a single number between 0 and 1 (or, in some countries’ reports, between 0 and 100). A 0 means perfect equality—everyone has the same income. A 1 means maximum inequality—one person has all the income, and everyone else has none.

But numbers don’t come from nowhere. The Gini is usually drawn from the Lorenz curve. Picture two diagonals on a graph: one line shows perfect equality (a 45-degree line from origin to the top right). The Lorenz curve dips below that line as actual income shares accumulate lower down the axis. The Gini coefficient is essentially the area between the line of equality and the Lorenz curve, scaled so it sits neatly between 0 and 1. The bigger that gap, the higher the inequality.

Why is the Gini the go-to measure?

• Simplicity and comparability: It condenses a complex distribution into a single, easy-to-compare number. You can spot whether inequality is higher in one country versus another, or whether it’s rising or falling over time.

• Broad relevance: It captures the overall dispersion of income, not just average levels. That makes it useful for cross-country analyses and for tracking trends that policymakers care about.

• International utility: Because many organizations and researchers use the same metric, it’s straightforward to align data, trends, and policy discussions across borders.

How to interpret the numbers in real life

A Gini around 0 means no one has more than anyone else—quite rare in the real world, but it helps anchor the idea. A Gini near 0.3-0.4 is common in many developed economies; higher values (closer to 0.5 or beyond) pop up in parts of the world with sharper income gaps. But here’s a useful reminder: numbers don’t tell the whole story by themselves. Two countries could have the same Gini, yet the lived experience of inequality feel very different if one country has a small, visible middle class and the other has a large, entrenched bottom group. And a country could have a moderate Gini but a lot of people living on the edge of poverty if the bottom tail is very close to the poverty line. The Gini is a tool, not a complete narrative.

Where the Gini fits into the IB HL frame (without turning this into a study guide)

In HL economics discussions, you’ll see the Gini coefficient used to compare income distribution across nations or to examine how changes in policy or demographics affect inequality over time. It provides a crisp, shareable snapshot that helps explain why debates about taxes, transfers, and social programs matter for fairness as well as growth. The beauty—and the caution—of the Gini is that it invites questions: If inequality shifts, what portion of that shift comes from wage gaps, capital income, or changes in government redistribution? What would a policy do to the line of equality and, consequently, the Lorenz curve?

A quick contrast with the other terms you’ll encounter

Your multiple-choice options included:

• The Laffer curve

• Factor endowment

• Tariffs

These are useful concepts, but they aren’t measures of income inequality.

• The Laffer curve is a thought experiment about tax rates and government revenue: there’s a revenue-maximizing rate, beyond which higher taxes actually shrink revenue. It helps discussions about tax policy, not about how income is distributed.

• Factor endowment is about the resources a country has—land, labor, capital. It explains long-run production possibilities but doesn’t measure how income sits across people.

• Tariffs are taxes on imports designed to protect domestic industries or raise revenue. They’re about trade protection and its effects, not about how evenly income is spread.

If you’re curious about other ways economists measure inequality beyond the Gini, you’ll hear terms like the Theil index or the Palma ratio. Each has its own quirks and what it highlights about the tails of the distribution. The Theil index, for instance, can separate inequality into within-group and between-group components; the Palma ratio focuses on the top 10% versus the bottom 40%. They’re less widely used in quick exams or general policy briefs, but they’re handy when you want a more nuanced picture of where inequality sits in the distribution.

Limitations to keep in mind

• It hides the shape. Two societies can share the same Gini but have very different distributions—one with a long middle class, another with a huge middle and a few extreme outliers. The Gini won’t tell you whether the main issue is the top end, the bottom tail, or the middle.

• It’s blind to poverty directly. You can have a relatively modest Gini and still see a big fraction of the population living on or near the poverty line.

• Population matters. If you split a country into separate regions with different distributions and then combine them, the overall Gini can change even if local distributions stay the same. That’s why demographic shifts can tilt the number without a policy shift in the living rooms of ordinary families.

Presenting data well

When you’re communicating about inequality, visuals matter as much as numbers. A Lorenz curve is a fantastic ally here. If you can show the curve bending away from the line of equality, you’ve got a vivid, intuitive story. If you’re briefing a class or writing a short piece for a broader audience, pair the Gini with a quick interpretation: “Country A has a higher Gini, suggesting more income dispersion than Country B,” and follow with a plain-language note on what that could imply for social cohesion, health outcomes, or educational opportunities. People connect with narrative, and the data becomes meaningful when you place it in everyday terms.

A gentle reminder about context

Inequality isn’t a static number. It shifts with changes in wages, employment, education, taxes, transfers, and even population aging. A rising Gini might signal policy gaps or rapid tech-driven productivity gains that don’t reach everyone. A falling Gini could reflect robust social insurance, successful wage bargaining, or growing access to education. The key is to read the movement, not just the current level, and to ask: what policy levers could help tilt the distribution toward a fairer outcome without undercutting growth?

A few practical takeaways for curious readers

• The Gini is a useful shorthand. It’s not the full story, but it’s a strong starting point for comparing how evenly income is spread.

• It should be interpreted with care. Look at the trend over time, the country’s size and demographics, and what’s happening on the ground in health, education, and living standards.

• When you see a Gini number quoted, ask questions. What about the bottom tail? What if a country has big wage gains for the top 1% but steady gains for everyone else? How does government policy reshape the curve?

Bringing the idea home

If you’ve ever wondered why debates about taxes, social programs, or minimum wages feel so charged, you’re not just dealing with ideology. You’re wrestling with distribution—how economic pie-slices are shared. The Gini coefficient gives us a compact, interpretable lens to gauge that sharing. It’s a bridge between abstract theory and real-world impact, a way to translate numbers into policy questions like: Will this change help more people rise out of poverty, or will it widen the gap between the haves and have-nots?

Final thought

Income inequality is complex, layered, and deeply human. Numbers like the Gini coefficient help keep the conversation grounded, even as the topics stretch into philosophy—what kind of society do we want, and how do we get there? The next time you see a chart with two lines and a shade between them, you’ll know exactly what that shade is trying to tell you: not just how much, but how fairly, people in a country share the rewards of their work.

If you’re curious to explore further, you can check out country profiles from reputable data sources, compare trends over the last decade, or even compare two economies side by side. The more you look, the more you’ll see how a single number can open up a wider conversation about growth, opportunity, and the kind of economy you’d like to live in. And that’s the real value of understanding the Gini coefficient: it helps you think clearly about both policy design and everyday life.