How the Gini coefficient uses the Lorenz curve to measure income inequality.

Let’s talk about a tiny chart that carries a big idea: how money actually ends up distributed in a country. You’ve probably heard about the Lorenz curve and the line of absolute equality, but what they do together matters for something specific: the Gini coefficient. If you’re studying IB Economics HL, this is one of those concepts that shows how a neat visual can translate into a single number with real meaning.

What the Lorenz curve is really saying

Imagine lining up every person in a country from poorest to richest. Now, for each share of the population, you plot how much of total income they hold. If everyone earned the same, that line would be a perfect 45-degree diagonal—the line of absolute equality.

But reality usually looks different. The Lorenz curve bows below that diagonal: the bottom 20% might hold a chunk of income, but not 20% of the total; the top sliver tends to hold a lot more. The curve isn’t just a line; it’s a snapshot of distribution. The more the curve sags away from the diagonal, the more inequality there is.

The line of absolute equality is the baseline

That straight line you see on every Lorenz-curve graph—the line of absolute equality—represents a perfectly equal world. If the Lorenz curve hugs this line, wealth or income is shared very fairly. If the curve is far removed from it, gaps grow wide.

Where the Gini coefficient comes in

Here’s the thing: you don’t have to stare at a curve forever to understand inequality. The Gini coefficient boils the whole story down to a single number. It measures the area between the Lorenz curve and the line of equality, then scales that area against the total area under the line of equality. In simple terms, Gini = the gap area divided by the total possible area.

• If the Lorenz curve is almost on the diagonal, that gap is small. The Gini coefficient heads toward zero, signaling relatively equal income distribution.

• If the curve bows way out, the gap is large. The Gini climbs toward one, indicating high inequality.

Why this matters for IB Economics HL

The Gini coefficient is a practical tool for comparing countries, regions, or time periods. It’s a compact way to summarize a complex distribution. In HL studies, you’ll often see questions that ask you to interpret a Gini figure, relate it to policy levers (like taxation or transfers), or compare two countries’ inequality profiles. The idea is less about memorizing a number and more about understanding what the number tells you about how income sits across a population.

A quick side-by-side with related ideas

• Lorenz curve: a graphical representation of the actual income distribution. It’s qualitative in itself, but high-level patterns (like how far the curve bows) hint at inequality levels.

• Line of absolute equality: the reference line. It’s the “perfectly fair” benchmark.

• Laffer curve: this is a different beast. It maps tax rates to tax revenue, not income distribution. It’s a tool for fiscal policy debates, but it doesn’t measure who gets what in society.

• Tariffs: these are about trade policy, not income distribution directly. They can have repercussions for welfare, but they don’t form a metric of inequality like the Gini does.

A simple, concrete way to picture it

Think of a pizza divided unequally. If you slice it so that the bottom half of people get almost all the toppings, the Lorenz curve will hug the bottom and the top sliver will look hungry. The gap between that actual slicing pattern and the perfectly even cut—the line of equality—grows. If you then imagine shading in the gap, the shaded area grows with inequality. The Gini coefficient is just a ratio: shaded area divided by the whole area under the equal-split line. Neat, right? One number to summarize a whole lot of distribution.

What the numbers imply in the real world

• A low Gini (close to zero) suggests a country with a fairly even spread of income. Social programs, strong education systems, and universal health care often help keep inequality in check.

• A high Gini (close to one) points to a sharp concentration of income. In these cases, policy chatter might focus on tax progressivity, transfers, or measures aimed at expanding opportunity.

But be aware of limitations

No single figure can capture every facet of inequality. The Gini has its quirks:

• It’s sensitive to population structure. If you change who’s included (for example, including or excluding certain age groups), the number can move.

• It doesn’t distinguish between the gains of the rich and the losses of the poor. Two countries could have the same Gini, but the actual distributions feel very different.

• It lumps together income and wealth in one sense, but real discussions often care about both. Wealth concentration can look very different from income inequality.

A glimpse of intuition you can carry into exam-style questions

If you see a graph with a Lorenz curve and you’re asked to identify which measure uses the area between the curve and the line of equality, that’s the Gini coefficient. One quick mental check: the vertex of the question isn’t about where the line sits or how curved the Lorenz line is by itself; it’s about the relative gap between the two lines. The bigger that gap, the bigger the Gini.

Two small thought experiments to ground the idea

• Imagine a policy that taxes high incomes a bit more and subsidizes lower incomes a bit more. If those transfers are well-targeted, the Lorenz curve would bow closer to the line of equality, and the Gini would fall. The story is simple in numbers, but the effect can be powerful in people’s lives.

• Now think about a country with a booming market that creates enormous wealth for a tiny elite while many people see little change. The Lorenz curve would bow more, the gap grows, and the Gini climbs. The same economy might still grow, but inequality has widened—a distinction policymakers wrestle with.

A few practical takeaways you can carry forward

• The Gini coefficient is a compact way to quantify inequality, but it’s not the full story. Use it alongside qualitative insights about who gains and who loses.

• Compare Gini figures with care. Look at the underlying population, whether wealth or income is measured, and the time period. A single number can hide meaningful shifts inside the distribution.

• Remember the distinction between inequality and mobility. A country can have a high Gini today but strong chances for people to move up over generations, or vice versa. The Gini doesn’t tell you about mobility by itself.

A friendly, exam-style recap (without the exam vibe)

• What it measures: inequality in income or wealth, via the area between the Lorenz curve and the line of absolute equality.

• The range: from 0 (perfect equality) to 1 (maximal inequality, in theory).

• Key interpretation: small gap means more equal; large gap means more unequal.

• What it’s not: Laffer curve (tax revenue vs tax rate) and Tariffs (trade policy). And it’s not the Lorenz curve itself, which is the graph, not a single number.

• Real-world use: quick cross-country or cross-time comparisons, policy discussion, and a starting point for deeper questions about welfare, efficiency, and growth.

A couple of practical notes for your notes

• If you’re asked to explain the Gini in simple terms, you can say: “The Gini coefficient is the fraction of the total possible gap that actually exists between how income is distributed and how it would be if everyone got the same.”

• If you’re given a graph, point out the area you’d shade as A and relate it to the Gini. That helps show you grasp the connection between the picture and the number.

Let me explain the bigger picture

The Gini coefficient sits at a sweet spot in economic analysis: it’s simple enough to be taught alongside richer concepts, yet it carries real weight. It helps us translate a complex distribution into something a policymakers, a student, or a citizen can talk about with concrete language. It also reminds us to ask the right questions: Who benefits from current arrangements? How might transfers, taxes, or investments in education and health shift the curve? And what does that mean for opportunity, social cohesion, and long-run growth?

If you’re building a mental toolkit for HL studies, keep the three characters in mind: Lorenz curve (the distribution picture), line of absolute equality (the judging line), and the Gini coefficient (the scorecard). Together, they form a clean, interpretable story about income inequality that you can carry from classroom debates to real-world policy discussions.

In the end, the Gini coefficient isn’t about pinning a blame label on a country. It’s about understanding how income sits across a population and what kinds of policy moves could nudge that balance toward fairness—without losing sight of the bigger economic picture. And that’s a surprisingly elegant way to connect theory, data, and the world we live in.