A statistical measure of economic inequality in a population
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The Gini coefficient (Gini index or Gini ratio) is a statistical measure of economic inequality in a population. The coefficient measures the dispersion of income or distribution of wealth among the members of a population.
The Gini coefficient is one of the most frequently used measures of economic inequality. The coefficient can take any values between 0 to 1 (or 0% to 100%). A coefficient of zero indicates a perfectly equal distribution of income or wealth within a population. A coefficient of one represents a perfect inequality when one person in a population receives all the income, while other people earn nothing. In addition, in some rare cases, the coefficient can exceed 100%. This may theoretically occur when the income or wealth of a population is negative.
However, the above-mentioned scenarios are extremely rare in the real world. The data shows that the coefficient generally ranges from 24% to 63%.
Please note that the Gini coefficient is not an absolute measure of a country’s income or wealth. The coefficient only measures the dispersion of income or wealth within a population.
Principles of the Gini Coefficient
The Gini coefficient is one of the most utilized measures of economic inequality because it aligns with the following principles:
The coefficient does not disclose the identities of high-income and low-income individuals in a population.
2. Scale of independence
The calculation of the Gini coefficient does not depend on how large the economy is, how it is measured, or how wealthy a country is. For example, both rich and poor countries may show the same coefficient due to similar income distribution.
3. Population independence
The coefficient does not depend on the size of the population.
4. Transfer principle
The coefficient reflects situations when income is transferred from a rich to a poor individual.
Limitations of the Gini Coefficient
Despite its numerous advantages such as universality and scalability, there are still some limitations to the Gini coefficient:
1. Sample bias
The validity of Gini coefficient calculations can be dependent on the size of a sample. For example, small countries or countries with less economic diversity frequently tend to show low coefficients, while large economically diverse countries usually demonstrate high coefficients.
2. Data inaccuracy
The Gini coefficient is prone to systematic and random data errors. Therefore, inaccurate data can distort the validity of the coefficient.
3. Same Gini coefficient but different income distribution
In some cases, the coefficient can be the same for countries with different income distributions but equal levels of income.
4. Does not reflect the structural changes in a population
One of the drawbacks of the coefficient is that it does not take into consideration the structural changes in a population. Such changes can significantly influence the economic inequality in a population. Generally, the situation arises because young people tend to earn less relative to older people.
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