Gini, Lorenz and economic equality

February 2026 by Ana Carrisso

For over a century, the Gini coefficient, specifically created by the Italian scientist and statistician Corrado Gini in 1912, has been used as the main indicator to measure a country's inequality levels based on the income of its inhabitants. At first glance, Gini's thesis seems simple: It establishes a number between 0 and 1, where 0 represents perfect equality (all members of society receive the same level of income) and 1 represents perfect inequality (one person has all the income and the rest have none). The Gini index is simply the Gini coefficient multiplied by 100, and is what is commonly known as the inequality index.

According to the World Bank, Portugal had an index of 33.9% in 2023. And, according to data from the National Institute of Statistics (INE), the coefficient continued to decrease, reaching 30.9% in 2024. In summary, Portugal ranks among the European countries with the best scores in economic equality.

Why is income used as a benchmark to determine the level of equality in a population? Disposable income is calculated by adding together the net incomes (after taxes) of the members of a household. This calculation includes the wages of employed workers and the profits or losses of self-employed workers, cash social benefits (such as pension payments), income from private pension schemes (such as private pension plans), capital and property income (for example, rent received from a rented apartment), and transfers from other households. Thus, we can state that income is the main variable that determines the level of economic prosperity of individuals, which is why it is taken as a reference point for calculating economic equality or inequality.

Thanks to its simplicity, the Gini coefficient has served to clarify problems related to the calculation of income distributions and associated social inequalities. However, it also has limitations; Therefore, as standards of social welfare evolved, it became necessary to combine this measure with other indicators to obtain a more accurate picture of a nation's levels of equality. One of the indicators that has traditionally been combined with the Gini index is the Lorenz curve.

The Lorenz curve

First developed by the American economist Max Otto Lorenz in 1905, it is a graphical representation used to show the relative distribution of a variable in a given domain; In this case, the variable would be income earned, and the domain would be the population of a country or the set of household aggregates of a country. Taking these two references, the curve would be drawn considering that the horizontal axis is the cumulative percentage of people or households and the vertical axis is the cumulative percentage of income. Under conditions of perfect inequality (which would correspond to a Gini coefficient of 1), the curve would lie close to the horizontal axis. If we were to take Portugal's score as a reference, the curve would be located at an intermediate point between these two extremes, although closer to the vertical axis. What's interesting about this metric is that it allows for the incorporation of various variables, making it useful, for example, for establishing a comparison between different countries.

Survey on Living Conditions and Income

Similarly, there are other metrics that can be used to measure economic equality. For example, the Survey on Living Conditions and Income that the INE (National Institute of Statistics) conducts annually collects data on the risk of poverty, which is also an important indicator; In 2024, 15.4% of Portuguese people were at risk of poverty, 1.2% less than in 2023. In this case, the INE (National Institute of Statistics) defines the risk of poverty as the perception of net annual monetary income per adult of less than 8,679 euros, or 723 euros per month.

This brief review of indicators that were conceived more than a hundred years ago is a sample of the power of data: Understanding how inequality is measured is a first step towards reading the economy critically, distinguishing between headlines and reality, and moving towards better financial literacy based on evidence and not just perceptions.