How Inequality is Measured: Gini Coefficient, Palma Ratio, and More

When economists, policymakers, and researchers talk about inequality, they rarely rely on gut feeling. They turn to specific statistical tools that translate complex realities of who earns what, who owns what, and who is left behind into measurable numbers. These tools shape headlines, influence budgets, and even decide where welfare schemes get directed. But not all inequality measures tell the same story. Some focus on the average gap across society, others zoom in on the extremes, and a few dig into differences between regions or social groups. Understanding how inequality is measured is the first step to understanding what those alarming or reassuring statistics in news reports actually mean.

Why measuring inequality matters 🔗

Inequality is not just an economic statistic; it is a measure of social fairness, opportunity, and stability. A country may show impressive GDP growth while its poorest citizens see no improvement in their daily lives. Without reliable tools to quantify how income or wealth is distributed, governments cannot design effective redistribution policies, and citizens cannot hold leaders accountable. The United Nations Department of Economic and Social Affairs notes that measurement methods range from simple ratios to complex statistical indices, each capturing a different dimension of disparity.

In a country like India, where over 90% of the workforce is employed in the informal sector and tax data covers only a fraction of adults, choosing the right measure becomes especially critical. A single number can either expose or hide the real picture.

The Gini coefficient: the most famous yardstick 🔗

The Gini coefficient, developed by Italian statistician Corrado Gini in 1912, is by far the most widely used measure of inequality. It compresses an entire income distribution into a single number between 0 and 1, where 0 represents perfect equality (every person earns exactly the same) and 1 represents perfect inequality (one person holds all the income).

How it is calculated 🔗

The Gini coefficient is derived from the Lorenz curve, a graph that plots the cumulative percentage of the population (from poorest to richest) on the horizontal axis against the cumulative share of income they receive on the vertical axis. A 45-degree diagonal line on this graph represents perfect equality. The further the actual Lorenz curve sags below this diagonal, the more unequal the society. The Gini coefficient measures the area between the equality line and the Lorenz curve, divided by the total area below the diagonal.

Generally, a Gini value below 0.4 is considered tolerable, while values approaching 0.5 or higher signal serious inequality. The coefficient can be calculated using two main methods: one based on pre-tax (market) income and another based on disposable income after taxes and social spending. The difference between these two values, as explained in economic literature on the Gini coefficient, indicates how effective a government’s fiscal policies are at redistributing wealth.

Limitations of the Gini coefficient 🔗

Despite its popularity, the Gini coefficient has several blind spots. It tells you how unequal a society is, but not where the inequality lies. Two countries can have the same Gini score but vastly different realities, one with a poor bottom and stable middle, the other with a stagnant middle and an extremely rich top.

For India, the issue becomes even sharper. A recent analysis noted that India’s Gini Index of 25.5, ranking it as one of the most equal countries, was based on consumption data rather than income or wealth. Consumption tends to look more equal because the wealthy save more, and savings do not appear in consumption surveys. When measured by income, India’s inequality looks dramatically worse. Additionally, the Gini coefficient does not capture wealth concentration, regional differences, or the informal economy where most Indians earn a living.

The Palma ratio: focusing on the extremes 🔗

Frustrated by the limitations of the Gini coefficient, economists Alex Cobham and Andy Sumner proposed the Palma ratio in 2013, building on the work of Chilean economist José Gabriel Palma. The Palma ratio is based on a striking observation: the middle 50% of the population, those between the 5th and 9th income deciles, consistently capture about half of the national income across most countries and time periods.

If the middle is so stable, then real inequality is essentially a tug-of-war between the top and bottom. The Palma ratio is calculated by dividing the share of gross national income held by the richest 10% by the share held by the poorest 40%.

How to interpret the Palma ratio 🔗

A Palma ratio of 1 means the top 10% and bottom 40% earn equal shares of national income. A ratio of 5 means the richest 10% earn five times more than the poorest 40% combined. As Our World in Data explains, higher values indicate higher inequality, and the measure is especially useful because it focuses directly on the gap between the rich and the poor, the parts of the distribution that matter most for policy debates around poverty, taxation, and welfare.

Why some economists prefer it 🔗

The Palma ratio is more intuitive than the Gini coefficient. Saying “the top 10% earn four times more than the bottom 40%” is far easier to grasp than “the Gini coefficient is 0.42”. It is also more politically relevant, because most redistribution debates revolve around the relationship between the wealthy elite and the struggling poor, not the comfortable middle class.

However, the Palma ratio has its own drawbacks. By focusing only on the extremes, it ignores changes happening within the middle 50%, which may still matter for social mobility and economic policy. It also does not capture variations within the top 10% itself, where a tiny ultra-rich elite can hold a disproportionate share even among the wealthy.

The Theil index: breaking inequality into pieces 🔗

While the Gini coefficient and Palma ratio give a single snapshot, the Theil index goes deeper. Developed by Dutch econometrician Henri Theil in 1967, it belongs to the family of generalised entropy measures and is rooted in information theory. Although the math is more complex, its real strength lies in one powerful feature: decomposability.

What decomposability means 🔗

The Theil index can split total inequality into two parts, inequality within a group and inequality between groups. As the Theil index methodology shows, this is something the Gini coefficient simply cannot do. For example, in India, researchers can use it to ask: how much of national income inequality is due to differences between states (say, Kerala versus Bihar), and how much is due to inequality within each state?

This distinction is crucial for policymakers. If most inequality is between regions, the answer might be regional investment or migration support. If most inequality is within regions, the answer might be progressive taxation or local welfare programs. According to empirical findings, at least three-quarters of inequality in most countries comes from within-group inequality, suggesting that uniform national policies often miss the deeper roots of disparity.

Limitations of the Theil index 🔗

The Theil index lacks the intuitive appeal of the Gini coefficient or Palma ratio. It is harder to communicate to a non-specialist audience, and its values do not have a straightforward interpretation like “0 to 1” or “richest divided by poorest”. It also requires detailed subgroup data that is not always available, especially in lower-income countries with weaker statistical systems.

The decile dispersion ratio: simplicity at its best 🔗

If the Theil index is the most sophisticated, the decile dispersion ratio is the simplest. It compares the average income of one segment of the population to another, typically the richest 10% versus the poorest 10% (known as the P90/P10 ratio).

As the World Bank’s poverty and equity team describes, this ratio expresses how many times richer the top earners are compared to the bottom earners. A P90/P10 value of 10 means the top decile earns ten times more than the bottom decile. Variations include the P75/P25 ratio for measuring inequality in the middle, or the P90/P50 ratio for the gap between the rich and the median household.

Strengths and weaknesses 🔗

The decile dispersion ratio is easy to calculate, easy to explain, and useful for quick comparisons across countries or time periods. But it ignores everything that happens between the chosen percentiles. A country could see massive shifts in middle-class income, and a P90/P10 ratio would not detect it. It also does not capture the distribution within the top or bottom deciles, missing the ultra-rich versus moderately rich divide that matters in many economies.

Other measures worth knowing 🔗

Beyond these four, several other tools are used in inequality research. The Atkinson index explicitly incorporates society’s aversion to inequality, expressing the percentage of income people would be willing to forgo for a more equal distribution. The coefficient of variation measures the dispersion of incomes around the mean. The Lorenz curve itself, even without converting into a Gini number, provides a powerful visual of income distribution. Health and education inequalities are increasingly measured using concentration indices, which adapt the Gini methodology to non-monetary outcomes.

Choosing the right measure 🔗

No single measure is perfect. The Gini coefficient is comprehensive but blurry. The Palma ratio is intuitive but narrow. The Theil index is analytically rich but technically demanding. The decile dispersion ratio is simple but selective. Researchers often use them in combination to get a fuller picture.

For India, where official statistics frequently show low inequality based on consumption while income and wealth data suggest the opposite, using multiple measures is not just academic, it is essential. A policy designed on the basis of the wrong indicator can entrench inequality rather than reduce it. Welfare schemes may bypass the truly poor, tax reforms may stall, and the gap between rhetoric and reality may widen.

What do you think? If you had to convince a policymaker to use just one inequality measure to design India’s next welfare scheme, which one would you choose and why? And do you believe a country can ever be considered “developed” while a large share of its citizens remains at the bottom of the income distribution?

References

1. https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/publication/dsp_policy_02.pdf
2. https://byjus.com/free-ias-prep/gini-co-efficient/
3. https://www.ensureias.com/blog/current-affairs/gini-coefficient-misreading-india-s-inequality
4. https://en.wikipedia.org/wiki/Income_inequality_metrics
5. https://ourworldindata.org/grapher/palma-ratio-s90s40-ratio
6. https://en.wikipedia.org/wiki/Theil_index
7. https://gsdrc.org/topic-guides/poverty-and-inequality/measuring-and-analysing-poverty-and-inequality/1-3-measures-of-inequality/
8. https://www.worldbank.org/en/topic/poverty/lac-equity-lab1/income-inequality/inequality-trends