The Gini index or Gini coefficient is a statistical measure of the distribution of wealth developed by the Italian statistician Corrado Gini in 1912. It is frequently used to take a look at economic inequality, measuring the distribution of income or, less commonly, the distribution of wealth within a population.
The coefficient ranges from 0 (or 0%) to 1 (or 100%), where 0 represents perfect equality and 1 represents perfect inequality. Values above 1 are theoretically possible due to negative wealth or income.
Understanding the Gini index
A country in which all residents have the same amounts of income would have an income Gini coefficient equal to 0. A country in which one resident earns all the income, where everyone else earns nothing, would have an income Gini coefficient equal to 1.
The same analysis can be applied to the distribution of wealth (The Gini coefficient of wealth), but since wealth is more difficult to measure than income, the Gini coefficient usually refers to income and is usually referred to as the “Gini coefficient.” Gini” or “Gini index”, without specifying that these refer to income. Gini coefficients for wealth tend to be much higher than those for income.
The Gini coefficient is an important tool for analyzing wealth or income distribution within a country or region, but it should not be treated as an absolute measure or wealth. A high-income country and a low-income country can have the same Gini coefficient, to the extent that income is distributed similarly within the citizens of each country: For example, Turkey and the United States both had a Gini coefficient of income of around 0.39 and 040 in 2016, according to the OECD, although the GDP per capita in Turkey was less than half the GDP per capita of the United States (in US dollars).
Graphic representation of the Gini index
To graphically represent the Gini index and the distribution of income, economists generally use the Lorenz curve.
The Lorenz curve is a way of showing the distribution of income (or wealth) within an economy. It was developed by Max O. Lorenz in 1905 to represent the distribution of wealth.
The Lorenz curve shows the cumulative share of income of different sectors of the population.
If there were perfect equality, if everyone had the same salary, the poorest 20% of the population would earn 20% of the total income. The poorest 60% of the population would get 60% of the income.
Lorenz curve for Latin America. The straight line represents perfect equality.
The Lorenz Curve and the Gini index
The Lorenz curve can be used to calculate the Gini coefficient, another measure of inequality.
The Gini coefficient is the area A/A + B
The closer the Lorenz curve is to the line of equality, the smaller the area A. And the Gini coefficient will be low.
If there is a high degree of inequality, then area A will be a larger percentage of the total area.
An increase in the Gini coefficient shows an increase in inequality: it shows that the Lorenz curve is further from the line of equality.
Gini index measurement
Mathematically, the Gini coefficient is defined based on the Lorenz curve. The Lorenz curve plots population percentiles on the horizontal axis of the graph according to income or wealth, whichever is being measured. The accumulated income or wealth of the population is represented on the vertical axis.
While the Gini coefficient is a useful tool for analyzing the distribution of wealth or income in a country, it should not be used as an indicator of a country’s overall wealth or income. Some of the poorest countries in the world, such as the Central African Republic, have some of the highest Gini coefficients (0.61 in this case). A high-income country and a low-income country can have the same Gini coefficients. Additionally, due to limitations such as reliable GDP and income data, the Gini index may exaggerate income inequality and be inaccurate.
Income inequality has political and economic impacts, such as slower GDP growth, reduced income mobility, higher household debt, political polarization, and higher poverty rates.
According to the World Bank’s 2016 report on poverty and shared prosperity, the global Gini coefficient experienced sustained growth during the 19th and 20th centuries. In 1820, the Gini coefficient was 0.50 and in 1980 and 1992, the figure was 0.657. That is, today we live in more unequal societies.
The countries with the highest Gini coefficients are:
Lesotho (0.632)
South Africa (0.625)
Haiti (0.608)
Botswana (0.605)
Namibia (0.597)
Zambia (0.575)
Comoros (0.559)
Hong Kong (0.539)
Guatemala (0.530)
Paraguay (0.517)
South Africa is one of the most unequal countries in the world, with a Gini coefficient of 0.625. In 2005, the Gini coefficient was even higher at 0.650. In South Africa, the richest 10% own 71% of the wealth, while the poorest 60% own only 7% of the wealth. Additionally, more than half of South Africa’s population, approximately 55.5%, live in poverty, earning less than $83 per month.
The United States has a Gini coefficient of 0.485, the highest in 50 years according to the United States Census Bureau. In 2015, the top 1% of workers in the United States averaged 40 times more income than the bottom 90%. Like other countries with higher Gini coefficients, poverty is a growing problem. In the United States, about 33 million workers earn less than $10 an hour, putting a family of four below the poverty level. Many of these low-wage workers do not have paid sick days, pensions or health insurance.
Many European countries have some of the lowest Gini coefficients, including Slovakia, Slovenia, Sweden, Ukraine, Belgium and Norway. Inequality is generally lower in European nations than in non-European nations. The Nordic countries and the countries of Central and Eastern Europe are among the most egalitarian countries.
The Gini index around the world
Christoph Lakner of the World Bank and Branko Milanovic of City University of New York estimated that the Gini index of global income was 0.705 in 2008, down from 0.722 in 1988. The figures vary considerably, but Delta economists, François Bourguignon and Christian Morrisson, estimated that the figure was 0.657 for both 1980 and 1992.
The work of Bourguignon and Morrisson shows a sustained growth in inequality since 1820, when the Gini coefficient was 0.500. Lakner and Milanovic’s work shows a decline in inequality around the beginning of the 21st century, as a 2005 book by Bourguignon also explains:
Economic expansion in Latin America, Asia and Eastern Europe has been largely responsible for the recent decline in income inequality. While inequality between nations has fallen in recent decades, however, inequality within countries has increased.
Gini within countries
Below are the Gini coefficients for each country for which the United States Central Intelligence Agency (CIA) provides information.
Gini index by country
Dark colors indicate that a country is more unequal, while light colors indicate that a country is more equal.
Some of the poorest countries, such as the Central African Republic, have one of the highest Gini coefficients in the world (61.3 or 0.613), while the richest countries (Denmark, for example) have one of the lowest Gini coefficients (28.8 ). Still, the relationship between income inequality and GDP per capita is not one of perfect correlation, and the relationship has varied over time. Michail Moatsos of Utrecht University and Joery Baten of the University of Tübingen show that from 1820 to 1929, inequality grew slightly, yet GDP per capita increased. From 1950 to 1970, inequality tended to fall while GDP grew above a certain threshold. From 1980 to 2000 inequality fell with much higher GDP per capita.
Index failures
Although useful for analyzing economic inequality, the Gini coefficient has some flaws. The accuracy of the metric is dependent on reliable information on GDP and income. The dark economies and informal economic activity that are present in each country, which makes it difficult to accurately measure people’s income. Informal economic activity tends to represent a larger portion of economic production in developing countries and people in these activities tend to have lower incomes, which causes income distribution to decline within countries. . In both cases that means that the Gini index measure of income will overstate true income inequality. Accurate wealth information is even more difficult to obtain due to the high popularity of tax havens.
Another flaw is that different income distributions can result in identical Gini coefficients. It also does not show information between ethnic groups or demographic variations that take into account race or age. In this sense, understanding demographics is important to understand what a given Gini index represents. For example, a large retired population pushes the Gini coefficient up.
Advantages and Disadvantages of using the Gini coefficient
Advantages
The main advantage of the Gini coefficient is that it is a measure of inequality, not a measure of average income or some other variable that is not representative of the majority of the population.
Gini coefficients can be used to compare income distributions between different sectors of the population and countries, for example, the Gini coefficient for urban areas differs from that for rural areas in many countries.
The Gini coefficient is simple enough to be compared across countries and easily interpreted. GDP statistics are often criticized as they do not represent changes for the entire population, the Gini coefficient demonstrates how incomes have changed for the poor and the rich. If the Gini coefficient increases at the same time as GDP, poverty may not improve for the vast majority of the population.
The Gini coefficient can be used to indicate how the income distribution within a country has changed over a period of time, therefore it is possible to see whether inequality is increasing or decreasing.
The Gini coefficient satisfies four important principles; Anonymity, no matter who the high and low income earners are. Independence scale, the Gini coefficient does not consider the size of the economy, the way it is measured or whether it is a rich or poor country on average, Independence of the population, no matter how large the country’s population is. Transfer principle . This states that if we transfer income from a rich person to a poor person, the resulting distribution is more equitable.
Disadvantages
There is an implication built into the Gini coefficient that a straight line distribution is a desirable outcome, which in the recently evolving long tail economy (where rich people are very rich) may not be the case.
Comparing income distributions between countries can be difficult because benefit systems may be different in different countries. For example, some countries provide benefits in the form of money, others use food stamps, which may or may not be counted as income on the Lorenz curve and are therefore not taken into account in the Gini coefficient.
The Lorenz curve may underestimate the actual amount of inequality if the situation is that richer households can use income more wisely than lower-income households. However, from another point of view, measured inequality can also be the result of more or less prudent use of household income.
As with all statistics, when income data is initially collected, there will always be systematic and random errors. If the data is less precise, then the Gini coefficient has less meaning. Additionally, countries may measure statistics differently, so it is not always possible to compare statistics between countries.
Economies with similar incomes and Gini coefficients can still have very different income distributions. This is because Lorenz curves can have different shapes and still produce the same Gini coefficient.
It is stated that the Gini coefficient is more sensitive to the income of the middle classes than to that of the extremes.
Too often only the Gini coefficient is cited without describing the proportions of the quantiles used for the measurement. As with other inequality coefficients, the Gini coefficient is influenced by the granularity of the measurements . Example: Five 20% quantiles (low granularity) will produce a lower Gini coefficient than twenty 5% quantiles (high granularity).
Gini index in Latin America
According to World Bank data, the most unequal countries in Latin America are Colombia and Brazil, with income Gini indices of 49.7 (0.497) and 53.3 (0.533) respectively.
Conclusions
The Gini index can be a useful measure to measure the development of a country, but due to its flaws it must be viewed in relation to other indicators such as GDP per capita and the human development index, so that political, social and business leaders , may have tools when denouncing inequality and economic growth that does not reach society as a whole, in the same way that it can be useful for the formulation of public policies that attempt to provide solutions to inequality problems.
