# Gini

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## Stub Draft

The public wiki is not an appropriate place for unofficial estimation of Gini for those countries for which it isn't currrently available but the following are of interest:

1. Cuba - Should be very low
2. DPRK - Low or High? This and prior one should be informed by the existing stats for China.
3. Iraq - Obvious political interest
4. Saudi Arabia - Odd no data given wealth

I also want to update my comments on the English bookmarks page and say something about the global value.

## Overview

The Gini coefficient is a measure of statistical dispersion developed by the Italian statistician Corrado Gini and published in his 1912 paper "Variability and Mutability" (Template:Lang-it). It is commonly used as a measure of inequality of income or wealth. It has, however, also found application in the study of inequalities in disciplines as diverse as health science, ecology, and chemistry.

## Definition Graphical representation of the Gini coefficient.

The graph shows that while the Gini is technically equal to the area marked 'A' divided by the sum of the areas marked 'A' and 'B' (that is, Gini = A/(A+B)), it is also equal to 2*A, since A+B = 0.5 since the axes scale from 0 to 1, and the total surface of the graph therefore equals 1.

The Gini coefficient is usually defined mathematically based on the Lorenz curve (below). It can 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).

The Gini coefficient can range from 0 to 1; it is sometimes multiplied by 100 to range between 0 and 100. A low Gini coefficient indicates a more equal distribution, with 0 corresponding to perfect equality, while higher Gini coefficients indicate more unequal distribution, with 1 corresponding to perfect inequality. To be validly computed, no negative goods can be distributed. Thus, if the Gini coefficient is being used to describe household income inequality, then no household can have a negative income. When used as a measure of income inequality, 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); and the most equal society will be one in which every person receives the same percentage of the total income (G=0).

Some find it more intuitive (and it is mathematically equivalent) to think of the Gini coefficient as half of the Relative mean difference. 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. Worldwide, Gini coefficients for income range from approximately 0.230 in Sweden to 0.707 in Namibia although not every country has been assessed.

As a mathematical measure of inequality, the Gini coefficient carries no moral judgement about whether a particular level of (in)equality is good or bad.

## Different 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. In health, it has been used as a measure of the inequality of health related quality of life in a population. In chemistry it has been used to to express the selectivity of protein kinase inhibitors against a panel of kinases.

## Calculation

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 is A/(A+B). Since A+B = 0.5, the Gini index, G = A/(0.5) = 2A = 1-2B. If the Lorenz curve is represented by the function Y = L(X), the value of B can be found with integration and:

$G = 1 - 2\,\int_0^1 L(X) dX.$

In some cases, this equation can be applied to calculate the Gini coefficient without direct reference to the Lorenz curve. For example:

• For a population uniform on the values yi, i = 1 to n, indexed in non-decreasing order ( yiyi+1):
$G = \frac{1}{n}\left ( n+1 - 2 \left ( \frac{\Sigma_{i=1}^n \; (n+1-i)y_i}{\Sigma_{i=1}^n y_i} \right ) \right )$
This may be simplified to:
$G = \frac{2 \Sigma_{i=1}^n \; i y_i}{n \Sigma_{i=1}^n y_i} -\frac{n+1}{n}$
• For a discrete probability function f(y), where yi, i = 1 to n, are the points with nonzero probabilities and which are indexed in increasing order ( yi < yi+1):
$G = 1 - \frac{\Sigma_{i=1}^n \; f(y_i)(S_{i-1}+S_i)}{S_n}$
where
$S_i = \Sigma_{j=1}^i \; f(y_j)\,y_j\,$ and $S_0 = 0\,$
$G = 1 - \frac{1}{\mu}\int_0^\infty (1-F(y))^2dy = \frac{1}{\mu}\int_0^\infty F(y)(1-F(y))dy$
• 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 yi, i = 1 to n, that are indexed in non-decreasing order ( yiyi+1), the statistic:
$G(S) = \frac{1}{n-1}\left (n+1 - 2 \left ( \frac{\Sigma_{i=1}^n \; (n+1-i)y_i}{\Sigma_{i=1}^n y_i}\right ) \right )$
is a consistent estimator of the population Gini coefficient, but is not, in general, unbiased. Like, G, G(S) has a simpler form:
$G(S) = 1 - \frac{2}{n-1}\left ( n - \frac{\Sigma_{i=1}^n \; iy_i}{\Sigma_{i=1}^n y_i}\right )$.

There does not exist a sample statistic that is in general an unbiased estimator of the population Gini coefficient, like the relative mean difference.

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 , Yk ) are the known points on the Lorenz curve, with the X k indexed in increasing order ( X k - 1 < X k ), so that:

• Xk is the cumulated proportion of the population variable, for k = 0,...,n, with X0 = 0, Xn = 1.
• Yk is the cumulated proportion of the income variable, for k = 0,...,n, with Y0 = 0, Yn = 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:

$G_1 = 1 - \sum_{k=1}^{n} (X_{k} - X_{k-1}) (Y_{k} + Y_{k-1})$

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 the incomes 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 yk;

$k = A + \ N(0, s^{2}/y_k)$

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.

The Gini coefficient can be calculated if you know the mean of a distribution, the number of people (or percentiles), and the income of each person (or percentile). Princeton development economist Angus Deaton (1997, 139) simplified the Gini calculation to one easy formula:

$G = \frac{N+1}{N-1}-\frac{2}{N(N-1)u}(\Sigma_{i=1}^n \; P_iX_i)$

where u is mean income of the population, Pi 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.

## Income Gini indices in the world

A complete listing is in list of countries by income equality; the article economic inequality discusses the social and policy aspects of income and asset inequality.

While most developed European nations and Canada tend to have Gini indices between 24 and 36, the United States' and Mexico's Gini indices are both above 40, indicating that the United States and Mexico have greater inequality. 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 (see criticisms section).

The Gini index for the entire world has been estimated by various parties to be between 56 and 66.

### US income Gini indices over time

Gini indices for the United States at various times, according to the US Census Bureau:

• 1929: 45.0 (estimated)
• 1947: 37.6 (estimated)
• 1967: 39.7 (first year reported)
• 1968: 38.6 (lowest index reported)
• 1970: 39.4
• 1980: 40.3
• 1990: 42.8
• 2000: 46.2
• 2005: 46.9
• 2006: 47.0 (highest index reported)
• 2007: 46.3 

### EU gini index

In 2005 the Gini index for the EU was estimated at 31.. This is surprisingly low, since the EU has virtually no interstate income redistribution power (the EU budget is only ~1% of the total GDP, there are no EU taxes, there is no EU social policy, and no EU treasury); moreover, a number of poorer new member states joined in 2004.

## Advantages of Gini coefficient as a measure of inequality

• It can be used to compare income distributions across different population sectors as well as countries, for example the Gini coefficient for urban areas differs from that of rural areas in many countries (though the United States' urban and rural Gini coefficients are nearly identical).
• It is sufficiently simple that it can be compared across countries and be easily interpreted. GDP statistics are often criticized as they do not represent changes for the whole population; the Gini coefficient demonstrates how income has changed for poor and rich. If the Gini coefficient is rising as well as GDP, poverty may not be improving for the majority of the population.
• The Gini coefficient can be used to indicate how the distribution of income has changed within a country over a period of time, thus it is possible to see if inequality is increasing or decreasing.
• The Gini coefficient satisfies four importantTemplate:Who principles:
• 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.

## Disadvantages of Gini coefficient as a measure of inequality

• The Gini coefficient of different sets of people cannot be averaged to obtain the Gini coefficient of all the people in the sets: if a Gini coefficient were to be calculated for each person it would always be zero. For a large, economically diverse country, a much higher coefficient will be calculated for the country as a whole than will be calculated for each of its regions. (The coefficient is usually applied to measurable nominal income rather than local purchasing power, tending to increase the calculated coefficient across larger areas.)
For this reason, the scores calculated for individual countries within the EU are difficult to compare with the score of the entire US: the overall value for the EU should be used in that case, 31.3, which is still much lower than the United States', 45. Using decomposable inequality measures (e.g. the Theil index $T$ converted by $1-{e^{-T}}$ into a inequality coefficient) averts such problems.
• The Lorenz curve may understate the actual amount of inequality if richer households are able to use income more efficiently than lower income households or vice versa. From another point of view, measured inequality may be the result of more or less efficient use of household incomes.
• Economies with similar incomes and Gini coefficients can still have very different income distributions. This is because the Lorenz curves can have different shapes and yet still yield the same Gini coefficient.
• It measures current income rather than lifetime income. A society in which everyone earned the same over a lifetime would appear unequal because of people at different stages in their life; a society in which students study rather than save can never have a coefficient of 0. However, Gini coefficient can also be calculated for any kind of distribution, e.g. for wealth. 

## Problems in using the Gini coefficient

• Gini coefficients do include investment income; however, the Gini coefficient based on net income does not accurately reflect differences wealth - a possible source of misinterpretation. For example, Sweden has a low Gini coefficient for income distribution but a significantly higher Gini coefficient for wealth (still low by international standards, but significantly higher than for income: for instance 77% of the share value owned by households is held by just 5% of Swedish shareholding households ). In other words, the Gini income coefficient should not be interpreted as measuring effective egalitarianism.
• Too often only the Gini coefficient is quoted without describing the proportions of the quantiles used for measurement. 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) taken from the same distribution. This is an often encountered problem with measurements.
• Care should be taken in using the Gini coefficient as a measure of egalitarianism, as it is properly a measure of income dispersion. For example, if two equally egalitarian countries pursue different immigration policies, the country accepting higher proportion of low-income or impoverished migrants will paradoxically be assessed as less equal (gain a higher Gini coefficient).
• The Gini coefficient is point-estimate of equality at a certain time, hence it ignores life-span changes in income. Typically, increases in the proportion of young or old members of a society will drive apparent changes in equality. Because of this, factors such as age distribution within a population and mobility within income classes can create the appearance of differential equality when none exist taking into account epidemiological 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. Essentially, what matters is not just inequality in any particular year, but the composition of the distribution over time.
• Countries can have the same Gini coefficient but have completely different levels of wealth. Similarly, the Gini coefficient as measured over time does not measure growth in incomes.

## General problems of measurement

• Comparing income distributions among countries may be difficult because benefits systems may differ. For example, some countries give benefits in the form of money while others give food stamps, which might not be counted by some economists and researchers as income in the Lorenz curve and therefore not taken into account in the Gini coefficient. The USA counts income before benefits, while France counts it after benefits, making the USA appear slightly more unequal vis-a-vis France than it admittedly is. In another example, USSR appeared to have relatively high income inequality: by some estimates, in the late 70's, Gini coefficient of its urban population was as high as 0.38, which is higher than many Western countries today. This apparent inequality ignored the fact that many benefits received by Soviet citizens were nonmonetary and were afforded regardless of income: these benefits included, among others, free child care for children as young as 2 months, free elementary, secondary and higher education, free cradle-to-grave medical care, free or heavily subsidized housing. In this example, an accurate comparison between the 1970s USSR and Western countries would require one to assign monetary values to such benefits (a difficult task in the absence of free markets). Similar problems arise whenever a comparison between pure free-market economies and partially socialist economies is attempted. Benefits may take various and unexpected forms: for example, major oil producers such as Venezuela and Iran provide indirect benefits to its citizens by subsidizing the retail price of gasoline.
• The measure will give different results when applied to individuals instead of households. When different populations are not measured with consistent definitions, comparison is not meaningful.
• As for all statistics, there may be systematic and random errors in the data. The meaning of the Gini coefficient decreases as the data become less accurate. Also, countries may collect data differently, making it difficult to compare statistics between countries.

As one result of this criticism, in addition to or in competition with the Gini coefficient entropy measures are frequently used (e.g. the Theil Index and the Atkinson 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 non-intelligent particles in a closed system following the laws of statistical physics.

## Credit risk

The Gini coefficient is also commonly used for the measurement of the discriminatory power of rating systems in credit risk management. Since Gini coefficient addresses wealth inequality it may be important to understand what a transformative asset is. Transformative assets increase the Gini coefficient as they provide a family or individual with a wealth advantage over most persons.

The discriminatory power refers to a credit risk model's ability to differentiate between defaulting and non-defaulting clients. The above formula $G_1$ may be used for the final model and also at individual model factor level, to quantify the discriminatory power of individual factors. This is as a result of too many non defaulting clients falling into the lower points scale e.g. factor has a 10 point scale and 30% of non defaulting clients are being assigned the lowest points available e.g. 0 or negative points. This indicates that the factor is behaving in a counter-intuitive manner and would require further investigation at the model development stage.

References: The Analytics of risk model validation