Gini coefficient
The Gini coefficient (also known as the Gini index or Gini ratio) ( ) is a measure of statistical dispersion intended to represent the income distribution of a nation's residents, and is the most commonly used measure of inequality. It was developed by the Italian statistician and sociologist Corrado Gini and published in his 1912 paper "Variability and Mutability" (Italian: Variabilità e mutabilità).^{[3]}^{[4]}
The Gini coefficient measures the inequality among values of a frequency distribution (for example levels of income). A Gini coefficient of zero expresses perfect equality, where all values are the same (for example, where everyone has the same income). A Gini coefficient of one (or 100%) expresses maximal inequality among values (for example where only one person has all the income or consumption, and all others have none).^{[5]}^{[6]} However, a value greater than one may occur if some persons represent negative contribution to the total (e.g., have negative income or wealth). For larger groups, values close to or above 1 are very unlikely in practice.
The Gini coefficient was proposed by Gini as a measure of OECD countries, in the late 2000s, considering the effect of taxes and transfer payments, the income Gini coefficient ranged between 0.24 to 0.49, with Slovenia the lowest and Chile the highest.^{[8]} The countries in Africa had the highest pretax Gini coefficients in 2008–2009, with South Africa the world's highest, variously estimated to be between 0.63 to 0.7,^{[9]}^{[10]} although the figure for South Africa drops to 0.52 after social assistance is taken into account and drops again to 0.47 after taxation.^{[11]} The global income inequality Gini coefficient in 2005, for all human beings taken together, has been estimated to be between 0.61 and 0.68 by various sources.^{[12]}^{[13]}
There are some issues in interpreting a Gini coefficient. The same value may result from many different distribution curves. The demographic structure should be taken into account. Countries with an aging population, or with a baby boom, experience an increasing pretax Gini coefficient even if real income distribution for working adults remains constant. Scholars have devised over a dozen variants of the Gini coefficient.^{[14]}^{[15]}^{[16]}
Contents
 Definition 1

Calculation 2
 Based on just two levels of income 2.1
 Known distibution function 2.2
 General case 2.3
 Generalized inequality indices 3

Gini coefficients of income distributions 4
 US income Gini indices over time 4.1
 Regional income Gini indices 4.2
 World income Gini index since 1800s 4.3

Gini coefficients of social development 5
 Gini coefficient of education 5.1
 Gini coefficient of opportunity 5.2
 Gini coefficients and income mobility 5.3
 Features of Gini coefficient 6
 Countries by Gini Index 7
 Limitations of Gini coefficient 8
 Alternatives to Gini coefficient 9
 Relation to other statistical measures 10
 Other uses 11
 See also 12
 References 13
 External links 14
Definition
The Gini coefficient is usually defined mathematically based on the Lorenz curve, which plots the proportion of the total income of the population (y axis) that is cumulatively earned by the bottom x% of the population (see diagram). The line at 45 degrees thus represents perfect equality of incomes. The Gini coefficient can then be thought of as the ratio of the area that lies between the line of equality and the Lorenz curve (marked A in the diagram) over the total area under the line of equality (marked A and B in the diagram); i.e., G = A / (A + B).
If all people have nonnegative income (or wealth, as the case may be), the Gini coefficient can theoretically range from 0 (complete equality) to 1 (complete inequality); it is sometimes expressed as a percentage ranging between 0 and 100. In practice, both extreme values are not quite reached. If negative values are possible (such as the negative wealth of people with debts), then the Gini coefficient could theoretically be more than 1. Normally the mean (or total) is assumed positive, which rules out a Gini coefficient less than zero.
An alternative approach would be to consider the Gini coefficient as half of the relative mean difference, which is a mathematical equivalence. The mean difference is the average absolute difference between two items selected randomly from a population, and the relative mean difference is the mean difference divided by the average, to normalize for scale.
Calculation
Based on just two levels of income
The most equal society will be one in which every person receives the same income (G = 0); the most unequal society will be one in which a single person receives 100% of the total income and the remaining people receive none (G = 1−1/N).
While the income distribution of any particular country need not follow simple functions, these functions give a qualitative understanding of the income distribution in a nation given the Gini coefficient. The effects of minimum income policy due to redistribution can be seen in the linear relationships.
An informative simplified case just distinguishes two levels of income, low and high. If the high income group is u % of the population and earns a fraction f % of all income, then the Gini coefficient is f − u. An actual more graded distribution with these same values u and f will always have a higher Gini coefficient than f − u.
The proverbial case where the richest 20% have 80% of all income would lead to an income Gini coefficient of at least 60%.
An often cited case that 1% of all the world's population owns 50% of all wealth, means a wealth Gini coefficient of at least 49%.
Known distibution function
Income Distribution Function  Gini Coefficient 

y = 1 for all x  0.0 
y = x^{⅓}  0.143 
y = x^{½}  0.200 
y = x + b (b = 10% of max)  0.273 
y = x + b (b = 5% of max)  0.302 
y = x  0.333 
y = x^{2}  0.500 
y = x^{3}  0.600 
y = x^{p}, p > 0  p/(p + 2) 
Given the normalization of both the cumulative population and the cumulative share of income used to calculate the Gini coefficient, the measure is not overly sensitive to the specifics of the income distribution, but rather only on how incomes vary relative to the other members of a population. The exception to this is in the redistribution of wealth resulting in a minimum income for all people. When the population is sorted, if their income distribution were to approximate a well known function, then some representative values could be calculated. Some representative values of the Gini coefficient for income distributions approximated by some simple functions are seen in the table.
General case
The Gini index is defined as a ratio of the areas on the Lorenz curve diagram. If the area between the line of perfect equality and the Lorenz curve is A, and the area under the Lorenz curve is B, then the Gini index G = A / (A + B). Since A + B = 0.5, the Gini index is G = 2 A, or G = 1 – 2 B.
If the Lorenz curve is represented by the function Y = L (X), the value of B can be found with integration and:
 B = \int_0^1 L(X) dX.
In some cases, this equation can be applied to calculate the Gini coefficient without direct reference to the Lorenz curve. For example (taking y to mean the income or wealth of a person or household):
 For a population uniform on the values y_{i}, i = 1 to n, indexed in nondecreasing order (y_{i} ≤ y_{i+1}):
 G = \frac{1}{n}\left ( n+1  2 \left ( \frac{\sum\limits_{i=1}^n \; (n+1i)y_i}{\sum\limits_{i=1}^n y_i} \right ) \right )
 This may be simplified to:
 G = \frac{2 \Sigma_{i=1}^n \; i y_i}{n \Sigma_{i=1}^n y_i} \frac{n+1}{n}
 This formula actually applies to any real population, since each person can be assigned his or her own y_{i}.^{[17]}
 For a discrete probability distribution with probability mass function f(y), where y_{i}, i = 1 to n, are the points with nonzero probabilities and which are indexed in increasing order (y_{i} < y_{i+1}):
 G = 1  \frac{\Sigma_{i=1}^n \; f(y_i)(S_{i1}+S_i)}{S_n}
 where
 S_i = \Sigma_{j=1}^i \; f(y_j)\,y_j\, and S_0 = 0\,. This formula is also applicable in the limit as n\rightarrow\infty.
 For a continuous probability distribution with cumulative distribution function F(y) that has a mean μ and is zero for all negative values of y:
 G = 1  \frac{1}{\mu}\int_0^\infty (1F(y))^2dy = \frac{1}{\mu}\int_0^\infty F(y)(1F(y))dy
 (This formula can be applied when there are negative values if the integration is taken from minus infinity to plus infinity.)
 Since the Gini coefficient is half the relative mean difference, it can also be calculated using formulas for the relative mean difference. For a random sample S consisting of values y_{i}, i = 1 to n, that are indexed in nondecreasing order (y_{i} ≤ y_{i+1}), the statistic:
 G(S) = \frac{1}{n1}\left (n+1  2 \left ( \frac{\Sigma_{i=1}^n \; (n+1i)y_i}{\Sigma_{i=1}^n y_i}\right ) \right )
 is a consistent estimator of the population Gini coefficient, but is not, in general, unbiased. Like G, G (S) has a simpler form:
 G(S) = 1  \frac{2}{n1}\left ( n  \frac{\Sigma_{i=1}^n \; iy_i}{\Sigma_{i=1}^n y_i}\right ) .
There does not exist a sample statistic that is in general an unbiased estimator of the population Gini coefficient, like the relative mean difference.
For some functional forms, the Gini index can be calculated explicitly. For example, if y follows a lognormal distribution with the standard deviation of logs equal to \sigma, then G = \mathrm{erf}(\sigma/2) where \mathrm{erf} is the error function.
Sometimes the entire Lorenz curve is not known, and only values at certain intervals are given. In that case, the Gini coefficient can be approximated by using various techniques for interpolating the missing values of the Lorenz curve. If (X_{k}, Y_{k}) are the known points on the Lorenz curve, with the X_{k} indexed in increasing order (X_{k – 1} < X_{k}), so that:
 X_{k} is the cumulated proportion of the population variable, for k = 0,...,n, with X_{0} = 0, X_{n} = 1.
 Y_{k} is the cumulated proportion of the income variable, for k = 0,...,n, with Y_{0} = 0, Y_{n} = 1.
 Y_{k} should be indexed in nondecreasing order (Y_{k} > Y_{k – 1})
If the Lorenz curve is approximated on each interval as a line between consecutive points, then the area B can be approximated with trapezoids and:
 G_1 = 1  \sum_{k=1}^{n} (X_{k}  X_{k1}) (Y_{k} + Y_{k1})
is the resulting approximation for G. More accurate results can be obtained using other methods to approximate the area B, such as approximating the Lorenz curve with a quadratic function across pairs of intervals, or building an appropriately smooth approximation to the underlying distribution function that matches the known data. If the population mean and boundary values for each interval are also known, these can also often be used to improve the accuracy of the approximation.
The Gini coefficient calculated from a sample is a statistic and its standard error, or confidence intervals for the population Gini coefficient, should be reported. These can be calculated using bootstrap techniques but those proposed have been mathematically complicated and computationally onerous even in an era of fast computers. Ogwang (2000) made the process more efficient by setting up a “trick regression model” in which respective income variables in the sample are ranked with the lowest income being allocated rank 1. The model then expresses the rank (dependent variable) as the sum of a constant A and a normal error term whose variance is inversely proportional to y_{k};
 k = A + \ N(0, s^{2}/y_k)
Ogwang showed that G can be expressed as a function of the weighted least squares estimate of the constant A and that this can be used to speed up the calculation of the jackknife estimate for the standard error. Giles (2004) argued that the standard error of the estimate of A can be used to derive that of the estimate of G directly without using a jackknife at all. This method only requires the use of ordinary least squares regression after ordering the sample data. The results compare favorably with the estimates from the jackknife with agreement improving with increasing sample size. The paper describing this method can be found here: http://web.uvic.ca/econ/ewp0202.pdf
However it has since been argued that this is dependent on the model’s assumptions about the error distributions (Ogwang 2004) and the independence of error terms (Reza & Gastwirth 2006) and that these assumptions are often not valid for real data sets. It may therefore be better to stick with jackknife methods such as those proposed by Yitzhaki (1991) and Karagiannis and Kovacevic (2000). The debate continues.
Guillermina Jasso (1979) ^{[18]} and Angus Deaton (1997, 139) independently proposed the following formula for the Gini coefficient:
 G = \frac{N+1}{N1}\frac{2}{N(N1)\mu}(\Sigma_{i=1}^n \; P_iX_i)
where \mu is mean income of the population, P_{i} is the income rank P of person i, with income X, such that the richest person receives a rank of 1 and the poorest a rank of N. This effectively gives higher weight to poorer people in the income distribution, which allows the Gini to meet the Transfer Principle. Note that the JassoDeaton formula rescales the coefficient so that its value is 1 if all the X_i are zero except one.
Generalized inequality indices
The Gini coefficient and other standard inequality indices reduce to a common form. Perfect equality—the absence of inequality—exists when and only when the inequality ratio, r_j = x_j / \overline{x}, equals 1 for all j units in some population (for example, there is perfect income equality when everyone’s income x_j equals the mean income \overline{x}, so that r_j=1 for everyone). Measures of inequality, then, are measures of the average deviations of the r_j=1 from 1; the greater the average deviation, the greater the inequality. Based on these observations the inequality indices have this common form:^{[19]}
 \mathrm{Inequality} = \Sigma_j \, p_j \, f(r_j)\, ,
where p_{j} weights the units by their population share, and f(r_{j}) is a function of the deviation of each unit’s r_{j} from 1, the point of equality. The insight of this generalised inequality index is that inequality indices differ because they employ different functions of the distance of the inequality ratios (the r_{j}) from 1.
Gini coefficients of income distributions
Gini coefficients of income are calculated on market income as well as disposable income basis. The Gini coefficient on market income—sometimes referred to as pretax Gini index—is calculated on income before taxes and transfers, and it measures inequality in income without considering the effect of taxes and social spending already in place in a country. The Gini coefficient on disposable income—sometimes referred to as aftertax Gini index—is calculated on income after taxes and transfers, and it measures inequality in income after considering the effect of taxes and social spending already in place in a country.^{[8]}^{[20]}^{[21]}
The difference in Gini indices between OECD countries, on aftertaxes and transfers basis, is significantly narrower.^{[21]} For OECD countries, over 2008–2009 period, Gini coefficient on pretaxes and transfers basis for total population ranged between 0.34 to 0.53, with South Korea the lowest and Italy the highest. Gini coefficient on aftertaxes and transfers basis for total population ranged between 0.25 to 0.48, with Denmark the lowest and Mexico the highest. For United States, the country with the largest population in OECD countries, the pretax Gini index was 0.49, and aftertax Gini index was 0.38, in 2008–2009. The OECD averages for total population in OECD countries was 0.46 for pretax income Gini index and 0.31 for aftertax income Gini Index.^{[8]}^{[22]} Taxes and social spending that were in place in 2008–2009 period in OECD countries significantly lowered effective income inequality, and in general, "European countries—especially Nordic and Continental welfare states—achieve lower levels of income inequality than other countries."^{[23]}
Using the Gini can help quantify differences in welfare and compensation policies and philosophies. However it should be borne in mind that the Gini coefficient can be misleading when used to make political comparisons between large and small countries or those with different immigration policies (see limitations of Gini coefficient section).
The Gini index for the entire world has been estimated by various parties to be between 0.61 and 0.68.^{[12]}^{[13]}^{[24]} The graph shows the values expressed as a percentage, in their historical development for a number of countries.
US income Gini indices over time
Gini indices – before and after taxes between 1980 and 2010^{[8]}  

Taxes and social spending in most countries have significant moderating effect on income inequality Gini indices.

For the late 2000s, the United States had the 4th highest measure of income inequality out of the 34 OECD countries measured, after taxes and transfers had been taken into account.^{[25]}
The table below presents the Gini indices for household income, without including the effect of taxes and transfers, for the United States at various times, according to the US Census Bureau.^{[26]}^{[27]}^{[28]}^{[29]} The Gini values are a national composite, with significant variations in Gini between the states. The states of Utah, Alaska and Wyoming have a pretax income inequality Gini coefficient that is 10% lower than the U.S. average, while Washington D.C. and Puerto Rico 10% higher. After including the effects of federal and state taxes, the U.S. Federal Reserve estimates 34 states in the USA have a Gini coefficient between 0.30 and 0.35, with the state of Maine the lowest.^{[30]} At the county and municipality levels, the pretax Gini index ranged from 0.21 to 0.65 in 2010 across the United States, according to Census Bureau estimates.^{[31]}Year  pretax Gini  Comments 

1947  0.413  (estimated) 
1967  0.397  (first year reported) 
1968  0.386  
1970  0.394  
1980  0.403  
1990  0.428  
2000  0.462  
2005  0.469  
2006  0.470  
2007  0.463  
2008  0.467  
2009  0.468  
2010  0.469  
2011  0.477 
Regional income Gini indices
According to UNICEF, Latin America and the Caribbean region had the highest net income Gini index in the world at 48.3, on unweighted average basis in 2008. The remaining regional averages were: subSaharan Africa (44.2), Asia (40.4), Middle East and North Africa (39.2), Eastern Europe and Central Asia (35.4), and Highincome Countries (30.9). Using the same method, the United States is claimed to have a Gini index of 36, while South Africa had the highest income Gini index score of 67.8.^{[32]}
World income Gini index since 1800s
The table below presents the estimated world income Gini index over the last 200 years, as calculated by Milanovic.^{[34]} Taking income distribution of all human beings, the worldwide income inequality has been constantly increasing since the early 19th century. There was a steady increase in global income inequality Gini score from 1820 to 2002, with a significant increase between 1980 and 2002. This trend appears to have peaked and begun a reversal with rapid economic growth in emerging economies, particularly in the large populations of BRIC countries.^{[35]}
Year  World Gini index^{[12]}^{[32]}^{[36]} 

1820  0.43 
1850  0.53 
1870  0.56 
1913  0.61 
1929  0.62 
1950  0.64 
1960  0.64 
1980  0.66 
2002  0.71 
2005  0.68 
Gini coefficients of social development
Gini coefficient is widely used in fields as diverse as sociology, economics, health science, ecology, engineering and agriculture.^{[37]} For example, in social sciences and economics, in addition to income Gini coefficients, scholars have published education Gini coefficients and opportunity Gini coefficients.
Gini coefficient of education
Education Gini index estimates the inequality in education for a given population.^{[38]} It is used to discern trends in social development through educational attainment over time. From a study of 85 countries, Thomas, et al. estimate Mali had the highest education Gini index of 0.92 in 1990 (implying very high inequality in education attainment across the population), while the United States had the lowest education inequality Gini index of 0.14. Between 1960 and 1990, South Korea, China and India had the fastest drop in education inequality Gini Index. They also claim education Gini index for the United States slightly increased over the 1980–1990 period.
Gini coefficient of opportunity
Similar in concept to income Gini coefficient, opportunity Gini coefficient measures inequality of opportunity.^{[39]}^{[40]}^{[41]} The concept builds on Amartya Sen's suggestion^{[42]} that inequality coefficients of social development should be premised on the process of enlarging people’s choices and enhancing their capabilities, rather than process of reducing income inequality. Kovacevic in a review of opportunity Gini coefficient explains that the coefficient estimates how well a society enables its citizens to achieve success in life where the success is based on a person’s choices, efforts and talents, not his background defined by a set of predetermined circumstances at birth, such as, gender, race, place of birth, parent's income and circumstances beyond the control of that individual.
In 2003, Roemer^{[39]}^{[43]} reported Italy and Spain exhibited the largest opportunity inequality Gini index amongst advanced economies.
Gini coefficients and income mobility
In 1978, Anthony Shorrocks introduced a measure based on income Gini coefficients to estimate income mobility.^{[44]} This measure, generalized by Maasoumi and Zandvakili,^{[45]} is now generally referred to as Shorrocks index, sometimes as Shorrocks mobility index or Shorrocks rigidity index. It attempts to estimate whether the income inequality Gini coefficient is permanent or temporary, and to what extent a country or region enables economic mobility to its people so that they can move from one (e.g. bottom 20%) income quantile to another (e.g. middle 20%) over time. In other words, Shorrocks index compares inequality of shortterm earnings such as annual income of households, to inequality of longterm earnings such as 5year or 10year total income for same households.
Shorrocks index is calculated in number of different ways, a common approach being from the ratio of income Gini coefficients between shortterm and longterm for the same region or country.^{[46]}
A 2010 study using social security income data for the United States since 1937 and Ginibased Shorrocks indices concludes that income mobility in the United States has had a complicated history, primarily due to mass influx of women into the American labor force after World War II. Income inequality and income mobility trends have been different for men and women workers between 1937 and the 2000s. When men and women are considered together, the Gini coefficientbased Shorrocks index trends imply longterm income inequality has been substantially reduced among all workers, in recent decades for the United States.^{[46]} Other scholars, using just 1990s data or other short periods have come to different conclusions.^{[47]} For example, Sastre and Ayala, conclude from their study of income Gini coefficient data between 1993 and 1998 for six developed economies, that France had the least income mobility, Italy the highest, and the United States and Germany intermediate levels of income mobility over those 5 years.^{[48]}
Features of Gini coefficient
The Gini coefficient has features that make it useful as a measure of dispersion in a population, and inequalities in particular.^{[49]} It is a ratio analysis method making it easier to interpret. It also avoids references to a statistical average or position unrepresentative of most of the population, such as per capita income or gross domestic product. For a given time interval, Gini coefficient can therefore be used to compare diverse countries and different regions or groups within a country; for example states, counties, urban versus rural areas, gender and ethnic groups. Gini coefficients can be used to compare income distribution over time, thus it is possible to see if inequality is increasing or decreasing independent of absolute incomes.
Other useful features of the Gini coefficient include:^{[50]}^{[51]}^{[52]}
 Anonymity: it does not matter who the high and low earners are.
 Scale independence: 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.
 Population independence: it does not matter how large the population of the country is.
 Transfer principle: if income (less than the difference), is transferred from a rich person to a poor person the resulting distribution is more equal.
Countries by Gini Index
A Gini coefficient above 50 is consider high, in this category we can find countries like Chile, Bolivia, Mexico and Central America countries. A Gini coefficient of 30 or above is considere medium, in this category we find countries like USA, Venezuela. A Gini coefficient lower than 30 is considered low, in this category we find countries like Austria and Denmark.^{[53]}
Limitations of Gini coefficient
The Gini coefficient is a relative measure. Its proper use and interpretation is controversial.^{[54]} Mellor explains^{[55]} it is possible for the Gini coefficient of a developing country to rise (due to increasing inequality of income) while the number of people in absolute poverty decreases. This is because the Gini coefficient measures relative, not absolute, wealth. Kwok concludes^{[56]} that changing income inequality, measured by Gini coefficients, can be due to structural changes in a society such as growing population (baby booms, aging populations, increased divorce rates, extended family households splitting into nuclear families, emigration, immigration) and income mobility. Gini coefficients are simple, and this simplicity can lead to oversights and can confuse the comparison of different populations; for example, while both Bangladesh (per capita income of $1,693) and the Netherlands (per capita income of $42,183) had an income Gini index of 0.31 in 2010,^{[57]} the quality of life, economic opportunity and absolute income in these countries are very different, i.e. countries may have identical Gini coefficients, but differ greatly in wealth. Basic necessities may be available to all in a developed economy, while in an undeveloped economy with the same Gini coefficient, basic necessities may be unavailable to most or unequally available, due to lower absolute wealth.
Household Group 
Country A Annual Income ($) 
Country B Annual Income ($) 

1  20,000  9,000 
2  30,000  40,000 
3  40,000  48,000 
4  50,000  48,000 
5  60,000  55,000 
Total Income  $200,000  $200,000 
Country's Gini  0.2  0.2 
 Different income distributions with the same Gini coefficient
Even when the total income of a population is the same, in certain situations two countries with different income distributions can have the same Gini index (e.g. cases when income Lorenz Curves cross).^{[49]} Table A illustrates one such situation. Both countries have a Gini index of 0.2, but the average income distributions for household groups are different. As another example, in a population where the lowest 50% of individuals have no income and the other 50% have equal income, the Gini coefficient is 0.5; whereas for another population where the lowest 75% of people have 25% of income and the top 25% have 75% of the income, the Gini index is also 0.5. Economies with similar incomes and Gini coefficients can have very different income distributions. Bellù and Liberati claim that to rank income inequality between two different populations based on their Gini indices is sometimes not possible, or misleading.^{[58]}
 Extreme wealth inequality, yet low income Gini coefficient
Household number 
Country A Annual Income ($) 
Household combined number 
Country A combined Annual Income ($) 

1  20,000  1 & 2  50,000 
2  30,000  
3  40,000  3 & 4  90,000 
4  50,000  
5  60,000  5 & 6  130,000 
6  70,000  
7  80,000  7 & 8  170000 
8  90,000  
9  120,000  9 & 10  270000 
10  150,000  
Total Income  $710,000  $710,000  
Country's Gini  0.303  0.293 
 Small sample bias – sparsely populated regions more likely to have low Gini coefficient
Gini index has a downwardbias for small populations.^{[61]} Counties or states or countries with small populations and less diverse economies will tend to report small Gini coefficients. For economically diverse large population groups, a much higher coefficient is expected than for each of its regions. Taking world economy as one, and income distribution for all human beings, for example, different scholars estimate global Gini index to range between 0.61 and 0.68.^{[12]}^{[13]} As with other inequality coefficients, the Gini coefficient is influenced by the granularity of the measurements. For example, five 20% quantiles (low granularity) will usually yield a lower Gini coefficient than twenty 5% quantiles (high granularity) for the same distribution. Philippe Monfort has shown that using inconsistent or unspecified granularity limits the usefulness of Gini coefficient measurements.^{[62]}
The Gini coefficient measure gives different results when applied to individuals instead of households, for the same economy and same income distributions. If household data is used, the measured value of income Gini depends on how the household is defined. When different populations are not measured with consistent definitions, comparison is not meaningful.
Deininger and Squire (1996) show that income Gini coefficient based on individual income, rather than household income, are different. For United States, for example, they find that individual incomebased Gini index was 0.35, while for France they report individual incomebased Gini index to be 0.43. According to their individual focussed method, in the 108 countries they studied, South Africa had the world's highest Gini index at 0.62, Malaysia had Asia's highest Gini index at 0.5, Brazil the highest at 0.57 in Latin America and Caribbean region, and Turkey the highest at 0.5 in OECD countries.^{[63]}
Income bracket (in 2010 adjusted dollars) 
% of Population 1979 
% of Population 2010 

Under $15,000  14.6%  13.7% 
$15,000 – $24,999  11.9%  12.0% 
$25,000 – $34,999  12.1%  10.9% 
$35,000 – $49,999  15.4%  13.9% 
$50,000 – $74,999  22.1%  17.7% 
$75,000 – $99,999  12.4%  11.4% 
$100,000 – $149,999  8.3%  12.1% 
$150,000 – $199,999  2.0%  4.5% 
$200,000 and over  1.2%  3.9% 
Total Households  80,776,000  118,682,000 
United States' Gini on pretax basis 
0.404  0.469 
 Gini coefficient is unable to discern the effects of structural changes in populations^{[56]}
Expanding on the importance of lifespan measures, the Gini coefficient as a pointestimate of equality at a certain time, ignores lifespan changes in income. Typically, increases in the proportion of young or old members of a society will drive apparent changes in equality, simply because people generally have lower incomes and wealth when they are young than when they are old. Because of this, factors such as age distribution within a population and mobility within income classes can create the appearance of inequality when none exist taking into account demographic effects. Thus a given economy may have a higher Gini coefficient at any one point in time compared to another, while the Gini coefficient calculated over individuals' lifetime income is actually lower than the apparently more equal (at a given point in time) economy's.^{[16]} Essentially, what matters is not just inequality in any particular year, but the composition of the distribution over time.
Kwok claims income Gini index for Hong Kong has been high (0.434 in 2010^{[57]}), in part because of structural changes in its population. Over recent decades, Hong Kong has witnessed increasing numbers of small households, elderly households and elderly living alone. The combined income is now split into more households. Many old people are living separately from their children in Hong Kong. These social changes have caused substantial changes in household income distribution. Income Gini coefficient, claims Kwok, does not discern these structural changes in its society.^{[56]} Household money income distribution for the United States, summarized in Table C of this section, confirms that this issue is not limited to just Hong Kong. According to the US Census Bureau, between 1979 and 2010, the population of United States experienced structural changes in overall households, the income for all income brackets increased in inflationadjusted terms, household income distributions shifted into higher income brackets over time, while the income Gini coefficient increased.^{[64]}^{[65]}
Another limitation of Gini coefficient is that it is not a proper measure of egalitarianism, as it is only measures income dispersion. For example, if two equally egalitarian countries pursue different immigration policies, the country accepting a higher proportion of lowincome or impoverished migrants will report a higher Gini coefficient and therefore may appear to exhibit more income inequality.
 Gini coefficient falls yet the poor get poorer, Gini coefficient rises yet everyone getting richer
Income bracket 
Year 1 Annual Income ($) 
Year 2 Annual Income ($) 
Year 3 Annual Income ($) 

Bottom 20%  0  500  0 
20% – 40%  1,000  1,200  500 
40% – 60%  2,000  2,200  1,000 
60% – 80%  5,000  5,500  2,000 
Top 20%  7,000  12,000  2,500 
Country's Gini  0.48  0.51  0.43 
Everyone better off 
Everyone poorer 
Arnold describes one limitation of Gini coefficient to be income distribution situations where it misleads. The income of poorest fifth of households can be lower when Gini coefficient is lower, than when the poorest income bracket is earning a larger percentage of all income. Table D illustrates this case, where the lowest income bracket has an average household market income of $500 per year at Gini index of 0.51, and zero income at Gini index of 0.48. This is counterintuitive and Gini coefficient cannot tell what is happening to each income bracket or the absolute income, cautions Arnold.^{[66]}^{[67]}
Feldstein similarly explains one limitation of Gini coefficient as its focus on relative income distribution, rather than real levels of poverty and prosperity in society.^{[68]} He claims Gini coefficient analysis is limited because in many situations it intuitively implies inequality that violate the socalled Pareto improvement principle.
The Pareto improvement principle, named after the Italian economist Vilfredo Pareto, states that a social, economic or income change is good if it makes one or more people better off without making anyone else worse off. Gini coefficient can rise if some or all income brackets experience a rising income. Feldstein’s explanation is summarized in Table D. The table shows that in a growing economy, consistent with Pareto improvement principle, where income of every segment of the population has increased, from one year to next, the income inequality Gini coefficient can rise too. In contrast, in another economy, if everyone gets poorer and is worse off, income inequality is less and Gini coefficient lower.^{[69]}^{[70]}
 Inability to value benefits and income from informal economy affects Gini coefficient accuracy
Some countries distribute benefits that are difficult to value. Countries that provide subsidized housing, medical care, education or other such services are difficult to value objectively, as it depends on quality and extent of the benefit. In absence of free markets, valuing these income transfers as household income is subjective. The theoretical model of Gini coefficient is limited to accepting correct or incorrect subjective assumptions.
In subsistencedriven and informal economies, people may have significant income in other forms than money, for example through subsistence farming or bartering. These income tend to accrue to the segment of population that is belowpoverty line or very poor, in emerging and transitional economy countries such as those in subSaharan Africa, Latin America, Asia and Eastern Europe. Informal economy accounts for over half of global employment and as much as 90 per cent of employment in some of the poorer subSaharan countries with high official Gini inequality coefficients. Schneider et al., in their 2010 study of 162 countries,^{[71]} report about 31.2%, or about $20 trillion, of world's GDP is informal. In developing countries, the informal economy predominates for all income brackets except for the richer, urban upper income bracket populations. Even in developed economies, between 8% (United States) to 27% (Italy) of each nation's GDP is informal, and resulting informal income predominates as a livelihood activity for those in the lowest income brackets.^{[72]} The value and distribution of the incomes from informal or underground economy is difficult to quantify, making true income Gini coefficients estimates difficult.^{[68]}^{[69]} Different assumptions and quantifications of these incomes will yield different Gini coefficients.^{[73]}^{[74]}^{[75]}
Gini has some mathematical limitations as well. It is not additive and different sets of people cannot be averaged to obtain the Gini coefficient of all the people in the sets.
Alternatives to Gini coefficient
Given the limitations of Gini coefficient, other statistical methods are used in combination or as an alternative measure of population dispersity. For example, entropy measures are frequently used (e.g. the Theil Index, the Atkinson index and the generalized entropy index). These measures attempt to compare the distribution of resources by intelligent agents in the market with a maximum entropy random distribution, which would occur if these agents acted like nonintelligent particles in a closed system following the laws of statistical physics.
Relation to other statistical measures
Gini coefficient closely related to the AUC (Area Under receiver operating characteristic Curve) measure of performance.^{[76]} The relation follows the formula AUC = (G+1)/2 Gini coefficient is also closely related to Mann–Whitney U.
Gini index is also related to Pietra index—both of which are a measure of statistical heterogeneity and are derived from Lorenz curve and the diagonal line.^{[77]}^{[78]}
In certain fields such as ecology, Simpson's index is used, which is related to Gini. Simpson index scales as mirror opposite to Gini; that is, with increasing diversity Simpson index takes a smaller value (0 means maximum, 1 means minimum heterogeneity per classic Simpson index). Simpson index is sometimes transformed by subtracting the observed value from the maximum possible value of 1, and then it is known as GiniSimpson Index.^{[79]}
Other uses
Although the Gini coefficient is most popular in economics, it can in theory be applied in any field of science that studies a distribution. For example, in ecology the Gini coefficient has been used as a measure of biodiversity, where the cumulative proportion of species is plotted against cumulative proportion of individuals.^{[80]} In health, it has been used as a measure of the inequality of health related quality of life in a population.^{[81]} In education, it has been used as a measure of the inequality of universities.^{[82]} In chemistry it has been used to express the selectivity of protein kinase inhibitors against a panel of kinases.^{[83]} In engineering, it has been used to evaluate the fairness achieved by Internet routers in scheduling packet transmissions from different flows of traffic.^{[84]} In statistics, building decision trees, it is used to measure the purity of possible child nodes, with the aim of maximising the average purity of two child nodes when splitting, and it has been compared with other equality measures.^{[85]}
The Gini coefficient is sometimes used for the measurement of the discriminatory power of rating systems in credit risk management.^{[86]}
The discriminatory power refers to a credit risk model's ability to differentiate between defaulting and nondefaulting clients. The formula G_1, in calculation section above, may be used for the final model and also at individual model factor level, to quantify the discriminatory power of individual factors. It is related to accuracy ratio in population assessment models.
See also

References
Notes
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Further reading
 Amiel, Y.; Cowell, F.A. (1999). Thinking about Inequality. Cambridge.
 Anand, Sudhir (1983). Inequality and Poverty in Malaysia. New York: Oxford University Press.
 Brown, Malcolm (1994). "Using GiniStyle Indices to Evaluate the Spatial Patterns of Health Practitioners: Theoretical Considerations and an Application Based on Alberta Data". Social Science Medicine 38 (9): 1243–1256.
 Chakravarty, S. R. (1990). Ethical Social Index Numbers. New York: SpringerVerlag.
 Deaton, Angus (1997). Analysis of Household Surveys. Baltimore MD: Johns Hopkins University Press.
 Dixon, PM; Weiner, J.; MitchellOlds, T.; Woodley, R. (1987). "Bootstrapping the Gini coefficient of inequality". Ecology (Ecological Society of America) 68 (5): 1548–1551.
 Dorfman, Robert (1979). "A Formula for the Gini Coefficient". The Review of Economics and Statistics (The MIT Press) 61 (1): 146–149.
 Firebaugh, Glenn (2003). The New Geography of Global Income Inequality. Cambridge MA: Harvard University Press.
 Gastwirth, Joseph L. (1972). "The Estimation of the Lorenz Curve and Gini Index". The Review of Economics and Statistics (The MIT Press) 54 (3): 306–316.
 Giles, David (2004). "Calculating a Standard Error for the Gini Coefficient: Some Further Results". Oxford Bulletin of Economics and Statistics 66 (3): 425–433.
 Gini, Corrado (1912). "Variabilità e mutabilità" Reprinted in Memorie di metodologica statistica (Ed. Pizetti E, Salvemini, T). Rome: Libreria Eredi Virgilio Veschi (1955).
 Gini, Corrado (1921). "Measurement of Inequality of Incomes". The Economic Journal (Blackwell Publishing) 31 (121): 124–126.
 Giorgi, G. M. (1990). A bibliographic portrait of the Gini ratio, Metron, 48, 183–231.
 Karagiannis, E. and Kovacevic, M. (2000). "A Method to Calculate the Jackknife Variance Estimator for the Gini Coefficient". Oxford Bulletin of Economics and Statistics 62: 119–122.
 Mills, Jeffrey A.; Zandvakili, Sourushe (1997). "Statistical Inference via Bootstrapping for Measures of Inequality". Journal of Applied Econometrics 12 (2): 133–150.
 Modarres, Reza and Gastwirth, Joseph L. (2006). "A Cautionary Note on Estimating the Standard Error of the Gini Index of Inequality". Oxford Bulletin of Economics and Statistics 68 (3): 385–390.
 Morgan, James (1962). "The Anatomy of Income Distribution". The Review of Economics and Statistics (The MIT Press) 44 (3): 270–283.
 Ogwang, Tomson (2000). "A Convenient Method of Computing the Gini Index and its Standard Error". Oxford Bulletin of Economics and Statistics 62: 123–129.
 Ogwang, Tomson (2004). "Calculating a Standard Error for the Gini Coefficient: Some Further Results: Reply". Oxford Bulletin of Economics and Statistics 66 (3): 435–437.
 Xu, Kuan (January 2004). "How Has the Literature on Gini's Index Evolved in the Past 80 Years?". Department of Economics, Dalhousie University. Retrieved 20060601. The Chinese version of this paper appears in Xu, Kuan (2003). "How Has the Literature on Gini's Index Evolved in the Past 80 Years?". China Economic Quarterly 2: 757–778.
 Yitzhaki, S. (1991). "Calculating Jackknife Variance Estimators for Parameters of the Gini Method". Journal of Business and Economic Statistics (American Statistical Association) 9 (2): 235–239.
External links
 Deutsche Bundesbank: Do banks diversify loan portfolios?, 2005 (on using e.g. the Gini coefficient for risk evaluation of loan portfolios)
 Forbes Article, In praise of inequality
 Measuring Software Project Risk With The Gini Coefficient, an application of the Gini coefficient to software
 The World Bank: Measuring Inequality
 Travis Hale, University of Texas Inequality Project:The Theoretical Basics of Popular Inequality Measures, online computation of examples: 1A, 1B
 Article from The Guardian analysing inequality in the UK 1974 – 2006
 World Income Inequality Database
 Income Distribution and Poverty in OECD Countries
 Gini Coefficient Calculator
 U.S. Income Distribution: Just How Unequal?
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