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[[File:GINI Index SVG.svg|thumb|right|upright=1.50|Gini coefficient of national income distribution around the world. This is based on 1989 to 2009 data, estimated by the CIA. Some are pre-tax and transfer, others post-tax income.]]


The '''Gini coefficient''' (also known as the '''Gini index''' or '''Gini ratio''') ({{IPAc-en|dʒ|i|n|i}}) is a [[Statistical dispersion#Measures of statistical dispersion|measure of statistical dispersion]] intended to represent the income distribution of a nation's residents. It was developed by the [[Italian people|Italian]] [[statistics|statistician]] and [[sociology|sociologist]] [[Corrado Gini]] and published in his 1912 paper "Variability and Mutability" ({{lang-it|Variabilità e mutabilità}}).<ref>Gini, C. (1912). "''{{lang-it|Variabilità e mutabilità}}''" 'Variability and Mutability', C. Cuppini, Bologna, 156 pages. Reprinted in ''Memorie di metodologica statistica'' (Ed. Pizetti E, Salvemini, T). Rome: Libreria Eredi Virgilio Veschi (1955).
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</ref><ref>Gini, C. (1909). "Concentration and dependency ratios" (in Italian). English translation in ''Rivista di Politica Economica'', '''87''' (1997), 769–789.</ref>
 
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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).<ref name="US Census Bureau">{{cite web|title=Current Population Survey (CPS) – Definitions and Explanations|url=http://www.census.gov/population/www/cps/cpsdef.html|publisher=US Census Bureau}}</ref><ref>Note: Gini coefficient becomes 1, only in a large population where one person has all the income. In the special case of just two people, where one has no income and the other has all the income, the Gini coefficient is 0.5. For 5 people set, where 4 have no income and the fifth has all the income, the Gini coefficient is 0.8. See: [http://www.fao.org/docs/up/easypol/329/gini_index_040en.pdf FAO, United Nations – Inequality Analysis, The Gini Index Module] (PDF format), fao.org.</ref> However, a value greater than one may occur if some persons have negative income or wealth. For larger groups, values close to or above 1 are very unlikely in practice.
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Gini coefficient is commonly used as a measure of [[social inequality|inequality]] of [[income inequality metrics|income]] or [[Wealth concentration|wealth]].<ref>Gini, C. (1936). "On the Measure of Concentration with Special Reference to Income and Statistics", Colorado College Publication, General Series No. 208, 73–79.</ref> For [[Organisation for Economic Co-operation and Development|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.<ref name=OECD1>{{cite web|title=Income distribution – Inequality: Income distribution – Inequality – Country tables|publisher=OECD|year=2012|url=http://stats.oecd.org/Index.aspx?QueryId=26068}}</ref> The countries in Africa had the highest pre-tax Gini coefficients in 2008–2009, with [[South Africa]] the world's highest at 0.7.<ref>{{cite web|title=South Africa Overview|publisher=The World Bank|year=2011|url=http://www.worldbank.org/en/country/southafrica/overview}}</ref>{{Dead link|date=January 2014}}<ref>{{cite web|title=Poverty reduction in Africa: Challenges and policy options|author=Ali, Mwabu and Gesami|publisher=African Economic Research Consortium, Nairobi|date=March 2002|url=http://www.africa4all-project.eu/workshop/documents/SP36.pdf|format=PDF}}</ref> {{Failed verification|date=January 2014}} 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.<ref name=fao2009>{{cite web|title=Poverty, Growth, and Inequality over the Next 50 Years|author=Evan Hillebrand|publisher=FAO, United Nations – Economic and Social Development Department|date=June 2009|url=ftp://ftp.fao.org/docrep/fao/012/ak968e/ak968e00.pdf|format=PDF}}</ref><ref name=undp10/>
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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 pre-tax Gini coefficient even if real income distribution for working adults remains constant. Scholars have devised over a dozen variants of the Gini coefficient.<ref>{{cite journal|title=More than a Dozen Alternative Ways of Spelling Gini|author=Shlomo Yitzhaki|journal=Economic Inequality|volume=8|year=1998|pages=13–30|url=http://siteresources.worldbank.org/INTDECINEQ/Resources/morethan2002.pdf}}</ref><ref>{{cite journal|title=Population Aging, Mobility of Quarterly Incomes, and Annual Income Inequality: Theoretical Discussion and Empirical Findings|author=Myung Jae Sung|date=August 2010|url=https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=IIPF66&paper_id=222}}</ref><ref name=blomq81/>
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[[File:Economics Gini coefficient2.svg|thumb|right|upright=1.40|Graphical representation of the Gini coefficient<br /><br />The graph shows that the Gini coefficient is equal to the area marked ''A'' divided by the sum of the areas marked ''A'' and ''B''. that is, {{nobreak|Gini {{=}} ''A'' / (''A'' + ''B'')}}. It is also equal to {{nobreak| 2*''A''}} due to the fact that {{nobreak|''A'' + ''B'' {{=}} 0.5}} (since the axes scale from 0 to 1).]]
 
The Gini coefficient is usually defined [[mathematics|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., {{nobreak|G {{=}} ''A'' / (''A'' + ''B'')}}.
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If all people have non-negative income (or wealth, as the case may be), the Gini coefficient can theoretically range from 0 to 1; 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.
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A low Gini coefficient indicates a more equal distribution, with 0 corresponding to complete equality, while higher Gini coefficients indicate more unequal distribution, with 1 corresponding to complete inequality. When used as a measure of income inequality, the most unequal society (assuming no negative incomes) will be one in which a single person receives 100% of the total income and the remaining people receive none ({{nobreak|''G'' {{=}} 1−1/''N''}}); and the most equal society will be one in which every person receives the same income ({{nobreak|''G'' {{=}} 0}}).
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An alternative approach would be to consider the Gini coefficient as half of the [[Mean difference|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.
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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 {{nowrap|''A'' / (''A'' + ''B'')}}. Since {{nowrap|''A'' + ''B'' {{=}} 0.5}}, the Gini index is {{nowrap|''G'' {{=}} 2 * A }} or {{nowrap|''G'' {{=}} 1 – 2 ''B''}}.
 
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If the Lorenz curve is represented by the function {{nowrap|''Y'' {{=}} ''L'' (''X'')}}, the value of ''B'' can be found with [[integral|integration]] and:
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:<math>B = \int_0^1 L(X) dX. </math>
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''<sub>''i''</sub>, ''i'' = 1 to ''n'', indexed in non-decreasing order (''y''<sub>''i''</sub> ≤ ''y''<sub>''i''+1</sub>):
:<math>G = \frac{1}{n}\left ( n+1 - 2 \left ( \frac{\sum\limits_{i=1}^n \; (n+1-i)y_i}{\sum\limits_{i=1}^n y_i} \right ) \right ) </math>
:This may be simplified to:
:<math>G = \frac{2 \Sigma_{i=1}^n \; i y_i}{n \Sigma_{i=1}^n y_i} -\frac{n+1}{n}</math>
:This formula actually applies to any real population, since each person can be assigned his or her own ''y''<sub>''i''</sub>.<ref name="Wolfram Mathworld">{{cite web|title=Gini Coefficient|url=http://mathworld.wolfram.com/GiniCoefficient.html|publisher=Wolfram Mathworld}}</ref>
* For a [[Discrete probability distribution|discrete probability function]] ''f''(''y''), where ''y''<sub>''i''</sub>, ''i'' = 1 to ''n'', are the points with nonzero probabilities and which are indexed in increasing order (''y''<sub>''i''</sub> &lt; ''y''<sub>''i''+1</sub>):
:<math>G = 1 - \frac{\Sigma_{i=1}^n \; f(y_i)(S_{i-1}+S_i)}{S_n}</math>
:where
:<math>S_i = \Sigma_{j=1}^i \; f(y_j)\,y_j\,</math> and <math>S_0 = 0\,</math>
* For a cumulative distribution function ''F''(''y'') that has a [[mean]] μ and is zero for all negative values of ''y'':
:<math>G = 1 - \frac{1}{\mu}\int_0^\infty (1-F(y))^2dy = \frac{1}{\mu}\int_0^\infty F(y)(1-F(y))dy</math>
:(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''<sub>''i''</sub>, ''i'' = 1 to ''n'', that are indexed in non-decreasing order (''y''<sub>''i''</sub> ≤ ''y''<sub>''i''+1</sub>), the statistic:
:<math>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 )</math>


:is a [[consistent estimator]] of the population Gini coefficient, but is not, in general, [[estimator#Point estimators|unbiased]]. Like ''G'', {{nobreak|''G'' (''S'')}} has a simpler form:
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:<math>G(S) = 1 - \frac{2}{n-1}\left ( n - \frac{\Sigma_{i=1}^n \; iy_i}{\Sigma_{i=1}^n y_i}\right ) </math>.
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There does not exist a sample statistic that is in general an unbiased estimator of the population Gini coefficient, like the [[relative mean difference]].
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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 <math>\sigma</math>, then <math>G = \mathrm{erf}(\sigma/2)</math> where <math>\mathrm{erf}</math> is the [[error function]].
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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 [[interpolation|interpolating]] the missing values of the Lorenz curve. If (''X''<sub>''k''</sub>, ''Y''<sub>''k''</sub>) are the known points on the Lorenz curve, with the ''X''<sub>''k''</sub> indexed in increasing order (''X''<sub>''k'' – 1</sub> &lt; ''X''<sub>''k''</sub>), so that:
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* ''X''<sub>''k''</sub> is the cumulated proportion of the population variable, for ''k'' = 0,...,''n'', with ''X''<sub>0</sub> = 0, ''X''<sub>''n''</sub> = 1.
 
* ''Y''<sub>''k''</sub> is the cumulated proportion of the income variable, for ''k'' = 0,...,''n'', with ''Y''<sub>0</sub> = 0, ''Y''<sub>''n''</sub> = 1.
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* ''Y''<sub>''k''</sub> should be indexed in non-decreasing order (''Y''<sub>''k''</sub> > ''Y''<sub>''k'' – 1</sub>)
 
If the Lorenz curve is approximated on each interval as a line between consecutive points, then the area B can be approximated with [[Trapezoidal rule|trapezoids]] and:
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:<math>G_1 = 1 - \sum_{k=1}^{n} (X_{k} - X_{k-1}) (Y_{k} + Y_{k-1})</math>
 
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is the resulting approximation for G. More accurate results can be obtained using other methods to [[Numerical integration|approximate the area]] B, such as approximating the Lorenz curve with a [[Simpson's rule|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.
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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 [[Resampling (statistics)#Bootstrap|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 distribution|normal]] error term whose variance is inversely proportional to ''y''<sub>''k''</sub>;
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:<math>k = A + \ N(0, s^{2}/y_k) </math>
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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 [[Resampling (statistics)#Jackknife|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
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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 [[Shlomo Yitzhaki (economics)|Yitzhaki]] (1991) and Karagiannis and Kovacevic (2000). The debate continues{{citation needed|date=October 2012}}.
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[[Princeton University|Princeton]] [[development economics|development economist]] [[Angus Deaton]] (1997, 139) redefined the Gini coefficient using the formula:
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:<math>G = \frac{N+1}{N-1}-\frac{2}{N(N-1)u}(\Sigma_{i=1}^n \; P_iX_i)</math>
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where u is mean income of the population, P<sub>i</sub> 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 [[Income inequality metrics#Transfer principle|Transfer Principle]]. Note that the Deaton formulation rescales the coefficient so that its value is 1 if all the <math>X_i</math> are zero except one.
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==Gini coefficients of representative income distributions==
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{| class="wikitable" style="float: right; margin-left: 1em;"
 
|-
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! Income Distribution Function !! Gini Coefficient (rounded)
 
|-
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| ''y'' = 1 for all x || 0.0
 
|-
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| ''y'' = ''x''<sup>⅓</sup> || 0.143<!--1/7-->
 
|-
</ul>
| ''y'' = ''x''<sup>½</sup> || 0.200<!--1/5-->
|-
| ''y'' = ''x'' + ''b'' (''b'' = 10% of max income) || 0.273<!--3/11-->
|-
| ''y'' = ''x'' + ''b'' (''b'' = 5% of max income) || 0.302<!--(21-2)/(21*3)-->
|-
| ''y'' = ''x'' || 0.333
|-
| ''y'' = ''x''<sup>2</sup> || 0.500
|-
| ''y'' = ''x''<sup>3</sup> || 0.600
|-
| ''y'' = ''x''<sup>p</sup>, 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 tabulated below.
 
While the income distribution of any particular country need not follow such 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 above.
 
==Generalized inequality index==
{{See also|Generalized entropy index}}
 
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, <math>r_j = x_j / \overline{x}</math>, equals 1 for all j units in some population (for example, there is perfect income equality when everyone’s income <math>x_j</math> equals the mean income <math>\overline{x}</math>, so that <math>r_j=1</math> for everyone). Measures of inequality, then, are measures of the average deviations of the <math>r_j=1</math> from 1; the greater the average deviation, the greater the inequality. Based on these observations the inequality indices have this common form:<ref>{{Cite journal |last=Firebaugh |first=Glenn |year=1999 |title=Empirics of World Income Inequality |journal=American Journal of Sociology |volume=104 |issue=6 |pages=1597–1630 |doi=10.1086/210218 }}. See also {{Cite book |chapter=Inequality: What it is and how it is measured |first=Glenn |last=Firebaugh |authormask=3 |title=The New Geography of Global Income Inequality |location=Cambridge, MA |publisher=Harvard University Press |year=2003 |isbn=0-674-01067-1}}</ref>
 
:<math>\mathrm{Inequality} = \Sigma_j \, p_j \, f(r_j)\, , </math>
 
where ''p''<sub>''j''</sub> weights the units by their population share, and ''f''(''r''<sub>''j''</sub>) is a function of the deviation of each unit’s ''r''<sub>''j''</sub> 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''<sub>''j''</sub>) from 1.''
 
==Gini coefficient of income distributions==
{{see also|List of countries by income equality}}
 
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 pre-tax 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 after-tax 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.<ref name=OECD1/><ref>{{cite journal|title=Applications of Lorenz Curves in Economic Analysis|author=N. C. Kakwani|journal=Econometrica|volume=45|date=April 1977|pages= 719–728|jstor=1911684|issue=3|doi=10.2307/1911684}}</ref><ref name=imf2000>{{cite web|title=Income Distribution and Tax and Government Social Spending Policies in Developing Countries|publisher=International Monetary Fund|date=March 2000|author=Chu, Davoodi, Gupta|url=http://www.imf.org/external/pubs/ft/wp/2000/wp0062.pdf}}</ref>
 
The difference in Gini indices between [[Organisation for Economic Co-operation and Development|OECD]] countries, on after-taxes and transfers basis, is significantly narrower.<ref name=imf2000/>{{page needed|date=December 2012}} For OECD countries, over 2008–2009 period, Gini coefficient on pre-taxes 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 after-taxes 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 pre-tax Gini index was 0.49, and after-tax Gini index was 0.38, in 2008–2009. The OECD averages for total population in OECD countries was 0.46 for pre-tax income Gini index and 0.31 for after-tax income Gini Index.<ref name=OECD1/><ref>{{cite web |url=http://www.eurofound.europa.eu/areas/qualityoflife/eurlife/index.php?template=3&radioindic=158&idDomain=3 |title=Monitoring quality of life in Europe – Gini index |work=Eurofound |date=26 August 2009 |postscript=<!-- Bot inserted parameter. Either remove it; or change its value to "." for the cite to end in a ".", as necessary. --> }}</ref> 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."<ref>{{cite journal|title=The redistributive effect of social transfer programmes and taxes: A decomposition across countries|author=Chen Wang, Koen Caminada, and Kees Goudswaard|journal=International Social Security Review|volume=65|issue=3|pages=27–48|date=July–September 2012|doi=10.1111/j.1468-246X.2012.01435.x}}</ref>
 
Using the Gini can help quantify differences in [[Social welfare|welfare]] and [[living wage|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 [[Gini coefficient#Limitations of Gini coefficient|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.<ref name=fao2009/><ref name=undp10>{{cite web|title=The Real Wealth of Nations: Pathways to Human Development, 2010|publisher=United Nations Development Program|year=2011|pages=72–74|isbn= 9780230284456|url=http://hdr.undp.org/en/media/HDR_2010_EN_Complete_reprint.pdf}}</ref><ref>{{cite web|url=http://siteresources.worldbank.org/INTDECINEQ/Resources/PSBSutcliffe.pdf|title=Postscript to the article ‘World inequality and globalization’ (Oxford Review of Economic Policy, Spring 2004)
|author=Bob Sutcliffe|date=April 2007|accessdate=2007-12-13|postscript=<!--None-->}}</ref> The graph shows the values expressed as a percentage, in their historical development for a number of countries.
[[File:Gini since WWII.svg|thumb|center|720px|alt=The change in Gini indices has differed across countries. Some countries have change little over time, such as Belgium, Canada, Germany, Japan, and Sweden. Brazil has oscillated around a steady value. France, Italy, Mexico, and Norway have shown marked declines. China and the US have increased steadily. Australia grew to moderate levels before dropping. India sank before rising again. The UK and Poland stayed at very low levels before rising. Bulgaria had an increase of fits-and-starts. .svg alt text]]
 
===US income Gini indices over time===
{{infobox
|above = Gini indexes – before and after taxes between 1980 and 2010<ref name=OECD1/>
|image =
{{multiple image|align=none
|image1 = GiniPlots UK.png | width1 = 310
|image2 = GiniPlots USA.png| width2 = 310
|caption1 = [[United Kingdom]]
|caption2 = [[United States]]
}}
{{multiple image|align=none
| image1 = GiniPlots Italy.png | width1 = 310
| image2 = GiniPlots Greece.png| width2 = 310
| caption1 = [[Italy]]
| caption2 = [[Greece]]
}}
|caption      = 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 in the United States|income inequality]] out of the 34 OECD countries measured, after taxes and transfers had been taken into account.<ref>Income distribution – Inequality. [http://stats.oecd.org/Index.aspx?DataSetCode=INEQUALITY Gini coefficient after taxes and transfers]. OECD. StatExtracts. Retrieved: 24 December 2012.</ref>
The table below presents the Gini indices for household income, without including the effect of taxes and transfers, for the [[United States of America|United States]] at various times, according to the [[United States Census Bureau|US Census Bureau]].<ref>{{cite web|url= http://www.census.gov/prod/1/pop/p60-191.pdf|title= A brief look at post-war U.S. Income Inequality |publisher= United States Census Bureau|year=1996}}</ref><ref>{{cite web|url=http://www.census.gov/prod/2009pubs/p60-236.pdf |title= Table 3. Income Distribution Measures Using Money Income and Equivalence-Adjusted Income: 2007 and 2008|page = 17 |work= Income, Poverty, and Health Insurance Coverage in the United States: 2008 |publisher= [[United States Census Bureau]]}}</ref><ref>{{cite web|url= http://www.census.gov/newsroom/releases/archives/income_wealth/cb10-144.html|title= Income, Poverty and Health Insurance Coverage in the United States: 2009 |work= Newsroom|publisher= [[United States Census Bureau]]}}</ref><ref>{{cite web |url=http://www.census.gov/newsroom/releases/archives/income_wealth/cb12-172.html |title= Income, Poverty and Health Insurance Coverage in the United States: 2011 |work= Newsroom |publisher= [[United States Census Bureau]] |date=September 12, 2012 |accessdate=January 23, 2013}}</ref> The Gini values are a national composite, with significant [[List of U.S. states by Gini coefficient|variations in Gini between the states]]. The states of Utah, Alaska and Wyoming have a pre-tax 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.<ref>{{cite web|title=Quantifying the Role of Federal and State Taxes in Mitigating Income Inequality|publisher=Federal Reserve, Boston, United States|author=Daniel H. Cooper, Byron F. Lutz, and Michael G. Palumbo|date=September 22, 2011|url=http://www.bos.frb.org/economic/ppdp/2011/ppdp1107.pdf}}</ref> At the county and municipality levels, the pre-tax Gini index ranged from 0.21 to 0.65 in 2010 across the United States, according to Census Bureau estimates.<ref>{{cite web|title=Household Income Inequality Within U.S. Counties: 2006–2010|publisher = Census Bureau, U.S. Department of Commerce|author=Adam Bee|date=February 2012|url= http://www.census.gov/prod/2012pubs/acsbr10-18.pdf}}</ref>
{| class="wikitable"
|+ Income Gini coefficient<BR>United States, 1947–2011
|-
! Year !! pre-tax 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: sub-Saharan Africa (44.2), Asia (40.4), Middle East and North Africa (39.2), Eastern Europe and Central Asia (35.4), and High-income 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.<ref name=unicef2011>{{cite web|title= Global Inequality: Beyond the Bottom Billion |publisher=UNICEF|date=April 2011|author=Isabel Ortiz and Matthew Cummins|page = 26 |url=http://www.unicef.org/socialpolicy/files/Global_Inequality_REVISED_-_5_July.pdf}}</ref>
 
===World income Gini index since 1800s===
[[File:Berg Ostry 2011 Chart 4.gif|thumb|right|upright=1.40|A 10 percentile decrease in inequality (represented by a change in the Gini coefficient from 40 to 37) increases the expected length of a growth spell by 50 percent. Percentage changes in GDP growth spell length are shown as each factor moves from 50th to 60th percentile and all other factors are held constant. '''Income distribution is measured by the Gini coefficient.'''<ref>{{Cite journal |last= Berg |first= Andrew G. |last2= Ostry |first2= Jonathan D. |year= 2011 |title= Equality and Efficiency |journal= Finance and Development |volume= 48 |issue= 3 |publisher= International Monetary Fund |url= http://www.imf.org/external/pubs/ft/fandd/2011/09/berg.htm |accessdate= September 10, 2012}}</ref>]]
 
The table below presents the estimated world income Gini index over the last 200 years, as calculated by Milanovic.<ref>{{Cite web |last= Milanovic |first= Branko |year= 2009 |title= Global Inequality and the Global Inequality Extraction Ratio |publisher=World Bank|url=http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2009/09/09/000158349_20090909092401/Rendered/PDF/WPS5044.pdf}}</ref> 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|BRIC countries]].<ref>{{cite journal|title=More or Less|journal=Finance & Development|date= September 2011|volume= 48|author=Branko Milanovic|publisher=International Monetary Fund|url=http://www.imf.org/external/pubs/ft/fandd/2011/09/milanovic.htm|issue= 3}}</ref>
 
{| class="wikitable" style="text-align: center;"
|+ Income Gini coefficient<BR>World, 1820–2005
|-
! Year !! World Gini index<ref name=fao2009/><ref name=unicef2011/><ref>{{cite web|title=Riding the Elephants: The Evolution of World Economic Growth and Income Distribution at the End of the Twentieth Century (1980–2000)|author=Albert Berry and John Serieux|publisher=United Nations (DESA Working Paper No. 27)|date=September 2006|url=http://www.un.org/esa/desa/papers/2006/wp27_2006.pdf}}</ref>
|-
| 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.<ref name="Sadras 2004 303–310">{{cite journal |last=Sadras |first=V. O. |last2=Bongiovanni |first2=R. |year=2004 |title=Use of Lorenz curves and Gini coefficients to assess yield inequality within paddocks |journal=Field Crops Research |volume=90 |issue=2–3 |pages=303–310 |doi=10.1016/j.fcr.2004.04.003 }}</ref> 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.<ref>{{cite web|title=Measuring education inequality – Gini coefficients of education|author=Thomas, Wang, Fan|date=January 2001|publisher=The World Bank|url=http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2001/02/17/000094946_01020605310354/Rendered/PDF/multi_page.pdf}}</ref> 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.<ref name=roemer06>{{cite web|title=ECONOMIC DEVELOPMENT AS OPPORTUNITY EQUALIZATION|author=John E. Roemer|date=September 2006|publisher=Yale University|url=http://ssrn.com/abstract=931479}}</ref><ref>{{cite journal|title=Generalized Gini Indices of Equality of Opportunity|author=John Weymark|journal=Journal of Economic Inequality|volume=1|year=2003|pages=5–24|doi=10.1023/A:1023923807503|issue=1}}</ref><ref>{{cite web|title=Measurement of Inequality in Human Development – A Review|author=Milorad Kovacevic|publisher=United Nations Development Program|date=November 2010|url=http://hdr.undp.org/en/reports/global/hdr2010/papers/HDRP_2010_35.pdf}}</ref> The concept builds on Amartya Sen's suggestion<ref>{{cite journal|title=The contributions of Amartya Sen to Welfare Economics|author=Anthony Atkinson|journal=Scand. J. Of Economics|volume=101|pages=173–190|year=1999|url=http://ias7.berkeley.edu/academics/courses/center/fall2007/sehnbruch/atkinson%201998%20contributions%20of%20sen%20to%20welf%20economics.pdf|issue=2|doi=10.1111/1467-9442.00151}}</ref> 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<ref name=roemer06/><ref>{{cite journal|author=Roemer|title=To what extent do fiscal regimes equalize opportunities for income acquisition among citizens?|journal=Journal of Public Economics|volume=87|issue=3–4|date=March 2003|pages=539–565|doi=10.1016/S0047-2727(01)00145-1|last2=Aaberge|first2=Rolf|last3=Colombino|first3=Ugo|last4=Fritzell|first4=Johan|last5=Jenkins|first5=Stephen P|last6=Lefranc|first6=Arnaud|last7=Marx|first7=Ive|last8=Page|first8=Marianne|last9=Pommer|first9=Evert|displayauthors=1}}</ref> reported [[Italy]] and [[Spain]] exhibited the largest opportunity inequality Gini index amongst advanced economies.
 
===Gini coefficients and income mobility===
In 1978, [[Anthony Shorrocks|A. Shorrocks]] introduced a measure based on income Gini coefficients to estimate income mobility.<ref>{{cite journal|title=Income Inequality and Income Mobility|journal=Journal of Economic Theory|date= December 1978|volume=19|issue=2|pages= 376–393|doi=10.1016/0022-0531(78)90101-1|last1=Shorrocks|first1=Anthony}}</ref> This measure, generalized by Maasoumi and Zandvakili,<ref>{{cite journal|author=Maasoumi and Zanvakili| year=1986|title=A class of generalized measures of mobility with applications|journal=Economic Letters|volume= 22|pages= 97–102|doi=10.1016/0165-1765(86)90150-3|last2=Zandvakili|first2=Sourushe}}</ref> 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 short-term earnings such as annual income of households, to inequality of long-term earnings such as 5-year or 10-year 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 short-term and long-term for the same region or country.<ref name=kss2010>{{cite journal|title=Earnings Inequality and Mobility in the United States: Evidence from Social Security Data Since 1937|author=Wojciech Kopczuk, Emmanuel Saez and Jae Song|journal=The Quarterly Journal of Economics|year=2010|volume= 125|issue=1|pages= 91–128|doi=10.1162/qjec.2010.125.1.91|url=http://emlab.berkeley.edu/~saez/kopczuk-saez-songQJE09SSA.pdf}}</ref>
 
A 2010 study using social security income data for the United States since 1937 and Gini-based Shorrocks indexes concludes that its income mobility has had a complicated history, primarily due to mass influx of women into the country's 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 coefficient-based Shorrocks Index trends imply long-term income inequality has been substantially reduced among all workers, in recent decades for the United States.<ref name=kss2010/> Other scholars, using just 1990s data or other short periods have come to different conclusions.<ref>{{cite journal|title=CROSS-NATIONAL DIFFERENCES IN INCOME MOBILITY: EVIDENCE FROM CANADA, THE UNITED STATES, GREAT BRITAIN AND GERMANY|author=Wen-Hao Chen|journal=Review of Income and Wealth|volume=55|issue=1|pages=75–100|date= March 2009|doi=10.1111/j.1475-4991.2008.00307.x}}</ref> 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.<ref>{{cite web|title=Europe vs. The United States: Is There a Trade-Off Between Mobility and Inequality?|author=Mercedes Sastre and Luis Ayala|publisher=Institute for Social and Economic Research, University of Essex|url=http://www.ucm.es/info/econeuro/documentos/documentos/dt192002.pdf|year=2002}}</ref>
 
==Features of Gini coefficient==
 
Gini coefficient has features that make it useful as a measure of dispersion in a population, and inequalities in particular.<ref name=faogini>{{cite web|title= Inequality Analysis – The Gini Index |publisher=Food and Agriculture Organization, United Nations|author=Lorenzo Giovanni Bellù and Paolo Liberati|year=2006 |url=http://www.fao.org/docs/up/easypol/329/gini_index_040EN.pdf}}</ref> 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 Gini coefficient include:<ref>{{cite web|title=Inequality: Methods and Tools |author= Julie A. Litchfield|date= March 1999|publisher=The World Bank|url=http://siteresources.worldbank.org/INTPGI/Resources/Inequality/litchfie.pdf }}</ref><ref>{{cite journal|title=Measurement of the Bipolarization Events|author=Stefan V. Stefanescu|journal=World Academy of Science, Engineering and Technology|volume=57|year=2009|pages=929–936}}</ref><ref name="Development Economics, Debraj Ray">{{Cite book |last=Ray |first=Debraj |title=Development Economics |location=Princeton, NJ |publisher=Princeton University Press |year=1998 |page=188 |isbn=0-691-01706-9}}</ref>
* ''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.
 
==Limitations of Gini coefficient==
 
The Gini coefficient is a relative measure. Its proper use and interpretation is controversial.<ref>{{cite journal|title=U.S. Income Inequality:  It's Not So Bad|author=Thomas Garrett|publisher=U.S. Federal Reserve, St Louis|date=Spring 2010|volume=14|issue=1|journal=Inside the Vault|url=http://stlouisfed.org/publications/pub_assets/pdf/itv/2010/ITV_sp_10.pdf}}</ref> Mellor explains<ref>{{cite journal|title=Dramatic Poverty Reduction in the Third World: Prospects and Needed Action|author=John W. Mellor|publisher=International Food Policy Research Institute|date=June 2, 1989|pages=18–20|url=http://pdf.usaid.gov/pdf_docs/PNABK503.pdf}}</ref> 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<ref name=Kwok10>{{cite web|title=Income Distribution of Hong Kong and the Gini Coefficient|author=KWOK Kwok Chuen|year=2010|publisher=The Government of Hong Kong, China|url=http://www.eabfu.gov.hk/en/pdf/income.pdf}}</ref> 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 family|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,<ref name=undp2010a>{{cite web|title=The Real Wealth of Nations: Pathways to Human Development (2010 Human Development Report – see Stat Tables)|pages=152–156|publisher=United Nations Development Program|year=2011|url=http://hdr.undp.org/en/reports/global/hdr2010/chapters/}}</ref> 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.
 
{| class="wikitable" style="float: left; margin-right:1em;"
|+ Table A. Different income distributions<BR>with the same Gini Index<ref name=faogini/>
|-
! Household<BR>Group !! Country A<BR>Annual<BR>Income ($) !! Country B<BR>Annual<BR>Income ($)
|-
| 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).<ref name=faogini/> 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.<ref name=Maio2007>{{cite journal|title=Income inequality measures|author=Fernando G De Maio|journal=Journal of Epidemiology and Community Health|year=2007|volume=61|issue=10|pages= 849–852|doi= 10.1136/jech.2006.052969|pmid=17873219|pmc=2652960}}</ref>
 
;Extreme wealth inequality, yet low income Gini coefficient
A Gini index does not contain information about absolute national or personal incomes. Populations can have very low income Gini indices, yet simultaneously very high wealth Gini index. By measuring inequality in income, the Gini ignores the differential efficiency of use of household income. By ignoring wealth (except as it contributes to income) the Gini can create the appearance of inequality when the people compared are at different stages in their life. Wealthy countries such as [[Sweden]] can show a low Gini coefficient for disposable income of 0.31 thereby appearing equal, yet have very high Gini coefficient for wealth of 0.79 to 0.86 thereby suggesting an extremely unequal wealth distribution in its society.<ref>{{cite journal|title=Inequality Trends in Sweden 1978–2004|author=Domeij and Floden|journal= Review of Economic Dynamics|volume=13|year=2010|issue=1|pages= 179–208|doi=10.1016/j.red.2009.10.005|last2=Flodén|first2=Martin}}</ref><ref>{{cite web|title=Accounting for Swedish wealth inequality|author=Domeij and Klein|date=January 2000|url=http://www.econometricsociety.org/meetings/wc00/pdf/0883.pdf}}</ref> These factors are not assessed in income-based Gini.
{| class="wikitable" style="text-align:center; float: right; margin-left:1em;"
|+ Table B. Same income distributions<BR>but different Gini Index
|-
! Household<BR>number !! Country A<BR>Annual<BR>Income ($) !! Household<BR>combined<BR>number !! Country A<BR>combined<BR>Annual<BR>Income ($)
|-
| 1 || 20,000 || rowspan="2"  style="text-align: center;" | 1 & 2 || rowspan="2" style="text-align: center;" | 50,000
|-
| 2 || 30,000
|-
| 3 || 40,000 || rowspan="2"  style="text-align: center;" | 3 & 4 || rowspan="2" style="text-align: center;" | 90,000
|-
| 4 || 50,000
|-
| 5 || 60,000 || rowspan="2"  style="text-align: center;" | 5 & 6 || rowspan="2" style="text-align: center;" | 130,000
|-
| 6 || 70,000
|-
| 7 || 80,000 || rowspan="2" style="text-align: center;" | 7 & 8 || rowspan="2" style="text-align: center;" | 170000
|-
| 8 || 90,000
|-
| 9 || 120,000 || rowspan="2"  style="text-align: center;" | 9 & 10 || rowspan="2"  style="text-align: center;" | 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 downward-bias for small populations.<ref>{{cite journal|title=The Small-Sample Bias of the Gini Coefficient: Results and Implications for Empirical Research|journal=The Review of Economics and Statistics|author=George Deltas|date=February 2003|volume= 85|pages=226–234|doi=10.1162/rest.2003.85.1.226|issue=1}}</ref> 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.<ref name=fao2009/><ref name=undp10/>
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.<ref>{{cite web|title=Convergence of EU regions – Measures and evolution|author=Philippe Monfort|publisher=European Union – Europa|page=6|year=2008|url=http://ec.europa.eu/regional_policy/sources/docgener/work/200801_convergence.pdf}}</ref>
 
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 income-based Gini index was 0.35, while for France they report individual income-based 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.<ref>{{cite journal|title=A New Data Set Measuring Income Inequality|author=Klaus Deininger and Lyn Squire|journal=World Bank Economic Review|year=1996|volume= 10|pages= 565–591|doi= 10.1093/wber/10.3.565|url=http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/1996/09/01/000009265_3961214153426/Rendered/PDF/multi_page.pdf|issue=3}}</ref>
 
{| class="wikitable" style="text-align:center; float: left; margin-right:1em;"
|+ Table C. Household money income<BR>distributions and Gini Index, USA<ref name=uscb2011>{{cite web|title=Income, Poverty, and Health Insurance Coverage in the United States: 2010 (see Table A-2)|date=September 2011|publisher=Census Bureau, Dept of Commerce, United States|url=http://www.census.gov/prod/2011pubs/p60-239.pdf}}</ref>
|-
! Income bracket<BR>(in 2010 adjusted dollars)!! % of Population<BR>1979 !! % of Population<BR>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 State's Gini<BR>on pre-tax basis || '''0.404''' || '''0.469'''
|}
;Gini coefficient is unable to discern the effects of structural changes in populations<ref name=Kwok10/>
Expanding on the importance of life-span measures, the Gini coefficient as a point-estimate of equality at a certain time, 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, 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.<ref name=blomq81>{{Cite journal |first=N. |last=Blomquist |year=1981 |title=A comparison of distributions of annual and lifetime income: Sweden around 1970 |journal=Review of Income and Wealth |volume=27 |issue=3 |pages=243–264 |doi=10.1111/j.1475-4991.1981.tb00227.x}}</ref> 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<ref name=undp2010a/>), 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.<ref name=Kwok10/> 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 inflation-adjusted terms, household income distributions shifted into higher income brackets over time, while the income Gini coefficient increased.<ref name=uscb2011/><ref name=cbo.p.10>[http://www.cbo.gov/doc.cfm?index=12485 Congressional Budget Office: Trends in the Distribution of Household Income Between 1979 and 2007]. October 2011. see p. i–x, with definitions on ii–iii</ref>
 
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 low-income or impoverished migrants will report a higher Gini coefficient and therefore may appear to exhibit more income inequality.
 
;Gini coefficient falls yet the poor gets poorer, Gini coefficient rises yet everyone getting richer
{| class="wikitable" style="float: right; margin-left:1em;"
|+ Table D. Effect of income<BR>changes on Gini Index
|-
! Income bracket!! Year 1<BR>Annual<BR>Income ($) !! Year 2 <BR>Annual<BR>Income ($) !! Year 3<BR>Annual<BR>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<BR>better off || Everyone<BR>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 counter-intuitive and Gini coefficient cannot tell what is happening to each income bracket or the absolute income, cautions Arnold.<ref>{{cite book|title=Economics|author=Roger Arnold|year=2007|isbn=978-0324538014|pages=573–581}}</ref><ref>{{cite journal|title=Inequality decomposition – three bad measures|author=Frank Cowell|journal=Bulletin of Economic Research|volume=40|pages=309–311|url=http://www.ophi.org.uk/wp-content/uploads/Cowell-1988.pdf|issue=4|doi=10.1111/j.1467-8586.1988.tb00274.x|year=2007}}</ref>
 
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.<ref name=mfeld/> He claims Gini coefficient analysis is limited because in many situations it intuitively implies inequality that violate the so-called 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.<ref name=tandw/><ref>{{cite web|title=Income inequality and poverty|author=Martin Feldstein|publisher=National Bureau of Economic Research|year=1998|url=http://www.nber.org/papers/w6770}}</ref>
 
;Inability to value benefits and income from [[Informal sector|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 subsistence-driven and informal economies, people may have significant income in other forms than money, for example through [[subsistence farming]] or [[barter]]ing. These income tend to accrue to the segment of population that is below-poverty line or very poor, in emerging and transitional economy countries such as those in sub-Saharan 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 sub-Saharan countries with high official Gini inequality coefficients. Schneider et al., in their 2010 study of 162 countries,<ref>{{cite journal|author=Friedrich Schneider|year=2010|title= New Estimates for the Shadow Economies all over the World|journal=International Economic Journal|volume=24|pages=443–461|doi= 10.1080/10168737.2010.525974|issue=4|author-separator=|author2=and others|displayauthors=1}}</ref> report about 31.2%, or about $20 trillion, of world's [[Gross domestic product|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.<ref>{{cite book|title=The Informal Economy|publisher=International Institute for Environment and Development, United Kingdom|year=2011|url=http://pubs.iied.org/pdfs/15515IIED.pdf|isbn=978-1-84369-822-7}}</ref> The value and distribution of the incomes from informal or underground economy is difficult to quantify, making true income Gini coefficients estimates difficult.<ref name=mfeld>{{cite web|title=Is income inequality really the problem? (Overview)|author=Martin Feldstein|date=August 1998|publisher=U.S. Federal Reserve|url=http://www.kc.frb.org/publicat/sympos/1998/S98feldstein.pdf}}</ref><ref name=tandw>{{cite book|title=Principles of Microeconomics: Global Financial Crisis Edition|author=Taylor and Weerapana|year=2009|isbn=978-1439078211|pages=416–418}}</ref> Different assumptions and quantifications of these incomes will yield different Gini coefficients.<ref>{{cite journal|title=INCOME INEQUALITY AND THE INFORMAL ECONOMY IN TRANSITION ECONOMIES|author=J. Barkley Rosser, Jr., Marina V. Rosser, and Ehsan Ahmed|journal=Journal of Comparative Economics|date=March 2000|volume= 28|pages=156–171|issue=1|doi=10.1006/jcec.2000.1645}}</ref><ref>{{cite web|title=Earnings inequality and the informal economy: evidence from Serbia|author=Gorana Krstić and Peter Sanfey|publisher=European Bank for Reconstruction and Development|date=February 2010|url=http://www.ebrd.com/downloads/research/economics/workingpapers/wp0114.pdf}}</ref><ref>{{cite web|title=The Size of the Shadow Economies of 145 Countries all over the World: First Results over the Period 1999 to 2003|author=Friedrich Schneider|date=December 2004|url= http://ssrn.com/abstract=636661}}</ref>
 
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]] and the [[Atkinson index]]). These measures attempt to compare the distribution of resources by intelligent agents in the market with a maximum [[information entropy|entropy]] [[random distribution]], which would occur if these agents acted like non-intelligent particles in a closed system following the laws of statistical physics.
 
==Relation to other statistical measures==
 
Gini coefficient closely related to the [[Receiver operating characteristic#Area under curve|AUC]] ([[Integral|Area Under]] [[receiver operating characteristic]] Curve) measure of performance.<ref name=hand>{{cite journal | last = Hand | first = David J. | coauthors = Robert J. Till | title = A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems | journal = Machine Learning | year = 2001 | volume = 45 | issue = 2 | pmid = | pages = 171–186 | doi = 10.1023/A:1010920819831 }}</ref> The relation follows the formula <math>AUC = (G+1)/2</math> Gini coefficient is also closely related to [[Mann–Whitney U]].
 
Gini index is also related to Pietra index&nbsp;— both of which are a measure of statistical heterogeneity and are derived from Lorenz curve and the diagonal line.<ref>{{cite journal|author=Iddo Eliazar and Igor Sokolov|year=2010 |title=Measuring statistical heterogeneity: The Pietra index|journal=Physica A-Statistical Mechanics and Its Applications|volume=389|pages= 117–125|doi= 10.1016/j.physa.2009.08.006|issue= 1}}</ref><ref>{{cite journal|title=Probabilistic Analysis of Global Performances of Diagnostic Tests: Interpreting the Lorenz Curve-Based Summary Measures|author=Wen-Chung Lee|journal=Statistics in Medicine|volume= 18|pages= 455–471|year= 1999|url= http://ntur.lib.ntu.edu.tw/bitstream/246246/160620/1/37.pdf|doi=10.1002/(SICI)1097-0258(19990228)18:4<455::AID-SIM44>3.0.CO;2-A|pmid=10070686|issue=4}}</ref>
 
In certain fields such as ecology, Simpson's index is used, which is related to Gini. [[Diversity index#Simpson index|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 Gini-Simpson Index.<ref>{{cite journal|title=The Measurement of Species Diversity|author=Robert K. Peet|journal=Annual Review of Ecology and Systematics|volume=5|year=1974 |pages= 285–307|jstor=2096890|doi=10.1146/annurev.es.05.110174.001441}}</ref>
 
==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.<ref name=natureArticle>{{cite journal | last = Wittebolle | first = Lieven | title = Initial community evenness favours functionality under selective stress | journal = [[Nature (journal)|Nature]] | year = 2009 | volume = 458 | issue = 7238 | pmid = 19270679 | pages = 623–626 | doi = 10.1038/nature07840| author3 = and others | displayauthors = 2 | last2 = Marzorati | first2 = Massimo | last4 = Balloi | first4 = Annalisa | last5 = Daffonchio | first5 = Daniele | last6 = Heylen | first6 = Kim | last7 = De Vos | first7 = Paul | last8 = Verstraete | first8 = Willy | last9 = Boon | first9 = Nico }}</ref> In health, it has been used as a measure of the inequality of health related [[quality of life]] in a population.<ref name=popHealthArticle>{{cite journal | last=Asada | first=Yukiko | title = Assessment of the health of Americans: the average health-related quality of life and its inequality across individuals and groups | journal = Population Health Metrics | year = 2005 | volume = 3 | pmid=16014174 | page = 7 | pmc=1192818 | doi = 10.1186/1478-7954-3-7}}</ref> In education, it has been used as a measure of the inequality of universities.<ref name=MinervaArticle>{{cite journal | last= Halffman | first= Willem | last2= Leydesdorff | first2= L | title = Is Inequality Among Universities Increasing? Gini Coefficients and the Elusive Rise of Elite Universities | journal = Minerva | year = 2010 | volume = 48 | pmid= 20401157 | issue= 1 | pages = 55–72 | pmc= 2850525 | doi = 10.1007/s11024-010-9141-3}}</ref> In chemistry it has been used to express the selectivity of [[protein kinase inhibitors]] against a panel of kinases.<ref name=JMedChemArticle>{{cite journal | last = Graczyk | first = Piotr | title = Gini Coefficient: A New Way To Express Selectivity of Kinase Inhibitors against a Family of Kinases | journal = Journal of Medicinal Chemistry | year = 2007 | volume = 50 | issue = 23 | pmid = 17948979 | pages = 5773–5779 | doi = 10.1021/jm070562u}}</ref> In engineering, it has been used to evaluate the fairness achieved by Internet routers in scheduling packet transmissions from different flows of traffic.<ref name=GreedyFairQueueing>{{Cite book |first1=Hongyuan |last1=Shi |first2=Harish |last2=Sethu |contribution=Greedy Fair Queueing: A Goal-Oriented Strategy for Fair Real-Time Packet Scheduling |pages=345–356 |title=Proceedings of the 24th IEEE Real-Time Systems Symposium |publisher=[[IEEE Computer Society]] |isbn=0-7695-2044-8 |year=2003}}</ref> 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.<ref>{{cite journal |last=Gonzalez |first=Luis |title=The Similarity between the Square of the Coeficient of Variation and the Gini Index of a General Random Variable |year=2010 |journal=Journal of Quantitative Methods for Economics and Business Administration |volume=10 |pages=5–18 |issn=1886-516X |url=http://www.upo.es/RevMetCuant/art.php?id=40|author3=and others |displayauthors=2 }}</ref>
 
The Gini coefficient is sometimes used for the measurement of the discriminatory power of [[credit rating|rating]] systems in [[credit risk]] management.<ref>{{cite book|title=The Analytics of Risk Model Validation (Quantitative Finance)|author=George A. Christodoulakis and Stephen Satchell (Editors)|isbn=978-0750681582|date=November 2007|publisher=Academic Press}}</ref>
 
The discriminatory power refers to a credit risk model's ability to differentiate between defaulting and non-defaulting clients. The formula <math>G_1</math>, 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==
{{col-begin}}{{col-break}}
* [[Diversity index]]
* [[Economic inequality]]
* [[Human Poverty Index]]
* [[Pareto distribution]]
* [[Robin Hood index]]
{{col-break|gap=2em}}
* [[ROC analysis]]
* [[Social welfare provision]]
* [[Suits index]]
* [[Utopia]]
* [[Welfare economics]]
{{col-break|gap=2em}}
* [[List of countries by distribution of wealth]]
* [[List of countries by income equality]]
* [[List of U.S. states by income equality]]
* [[Herfindahl index]]
{{col-end}}
 
== References ==
'''Notes'''
{{Reflist|2}}
 
'''Further reading'''
{{Refbegin|30em}}
* {{Cite book | author=Amiel, Y.; Cowell, F.A. | year=1999 | title=Thinking about Inequality | publisher=Cambridge | isbn=0-521-46696-2}}
* {{Cite book | first=Sudhir | last=Anand | year=1983 | title=Inequality and Poverty in Malaysia | publisher=Oxford University Press | location=New York | isbn=0-19-520153-1}}
* {{Cite journal | author=Brown, Malcolm | title=Using Gini-Style Indices to Evaluate the Spatial Patterns of Health Practitioners: Theoretical Considerations and an Application Based on Alberta Data | journal=Social Science Medicine | year=1994 | volume=38 | pages=1243–1256 | doi=10.1016/0277-9536(94)90189-9 | pmid=8016689 | issue=9}}
* {{Cite book | first=S. R. | last=Chakravarty | year=1990 | title=Ethical Social Index Numbers | publisher=Springer-Verlag | location=New York | isbn=0-387-52274-3 }}
* {{Cite book | first=Angus | last=Deaton | year=1997 | title=Analysis of Household Surveys | publisher=Johns Hopkins University Press | location=Baltimore MD | isbn=0-585-23787-5 }}
* {{Cite journal | author=Dixon, PM, Weiner J., Mitchell-Olds T, Woodley R. | title=Bootstrapping the Gini coefficient of inequality | journal=Ecology | year=1987 | volume=68 | pages=1548–1551 | doi=10.2307/1939238 | jstor=1939238 | issue=5 | publisher=Ecological Society of America}}
* {{Cite journal | author=Dorfman, Robert | title=A Formula for the Gini Coefficient | journal=The Review of Economics and Statistics | year=1979 | volume=61 | pages=146–149 | doi=10.2307/1924845 | jstor=1924845 | issue=1 | publisher=The MIT Press}}
* {{Cite book | first=Glenn | last=Firebaugh | year=2003 | title=The New Geography of Global Income Inequality | publisher=Harvard University Press | location=Cambridge MA | isbn=0-674-01067-1 }}
* {{Cite journal | author=Gastwirth, Joseph L. | title=The Estimation of the Lorenz Curve and Gini Index | journal=The Review of Economics and Statistics | year=1972 | volume=54 | pages=306–316 | doi=10.2307/1937992 | jstor=1937992 | issue=3 | publisher=The MIT Press}}
* {{Cite journal | author=Giles, David | title=Calculating a Standard Error for the Gini Coefficient: Some Further Results | journal=Oxford Bulletin of Economics and Statistics | year=2004 | volume=66 | issue=3 | pages=425–433 | doi=10.1111/j.1468-0084.2004.00086.x }}
* Gini, Corrado (1912). "Variabilità e mutabilità" Reprinted in Memorie di metodologica statistica (Ed. Pizetti E, Salvemini, T). Rome: Libreria Eredi Virgilio Veschi (1955).
* {{Cite journal | author=Gini, Corrado | title=Measurement of Inequality of Incomes | journal=The Economic Journal | year=1921 | volume=31 | pages=124–126 |
doi=10.2307/2223319 | jstor=2223319 | issue=121 | publisher=Blackwell Publishing}}
* Giorgi, G. M. (1990). A bibliographic portrait of the Gini ratio, ''Metron'', 48, 183–231.
* {{Cite journal | author=Karagiannis, E. and Kovacevic, M. | title=A Method to Calculate the Jackknife Variance Estimator for the Gini Coefficient | journal=Oxford Bulletin of Economics and Statistics | year=2000 | volume=62 | pages=119–122 | doi=10.1111/1468-0084.00163}}
* {{Cite journal | author=Mills, Jeffrey A.; Zandvakili, Sourushe | title=Statistical Inference via Bootstrapping for Measures of Inequality | journal=Journal of Applied Econometrics | year=1997 | volume=12 | issue=2 | pages=133–150 | doi=10.1002/(SICI)1099-1255(199703)12:2<133::AID-JAE433>3.0.CO;2-H}}
* {{Cite journal | author=Modarres, Reza and Gastwirth, Joseph L. | title=A Cautionary Note on Estimating the Standard Error of the Gini Index of Inequality | journal=Oxford Bulletin of Economics and Statistics | year=2006 | volume=68 | issue=3 | pages=385–390 | doi=10.1111/j.1468-0084.2006.00167.x}}
* {{Cite journal | author=Morgan, James | title=The Anatomy of Income Distribution | journal=The Review of Economics and Statistics | year=1962 | volume=44 | pages=270–283 | doi=10.2307/1926398 | jstor=1926398 | issue=3 | publisher=The MIT Press }}
* {{Cite journal | author=Ogwang, Tomson | title=A Convenient Method of Computing the Gini Index and its Standard Error | journal=Oxford Bulletin of Economics and Statistics | year=2000 | volume=62 | pages=123–129 | doi=10.1111/1468-0084.00164 }}
* {{Cite journal | author=Ogwang, Tomson | title=Calculating a Standard Error for the Gini Coefficient: Some Further Results: Reply | journal=Oxford Bulletin of Economics and Statistics | year=2004 | volume=66 | issue=3 | pages=435–437 | doi=10.1111/j.1468-0084.2004.00087.x}}
* {{Cite journal | author=Xu, Kuan | title=How Has the Literature on Gini's Index Evolved in the Past 80 Years? | publisher=Department of Economics, Dalhousie University |date = January 2004| version= | url=http://economics.dal.ca/RePEc/dal/wparch/howgini.pdf | accessdate=2006-06-01 }} The Chinese version of this paper appears in {{cite journal | author=Xu, Kuan | title=How Has the Literature on Gini's Index Evolved in the Past 80 Years? | journal = China Economic Quarterly | year=2003 | volume=2 | pages=757–778 }}
* {{Cite journal | author=Yitzhaki, S. | title=Calculating Jackknife Variance Estimators for Parameters of the Gini Method| journal=Journal of Business and Economic Statistics | year=1991 | volume=9 | pages=235–239 | doi=10.2307/1391792 | jstor=1391792 | issue=2 | publisher=American Statistical Association}}
{{Refend}}
 
==External links==
* Deutsche Bundesbank: [http://www.bundesbank.de/download/bankenaufsicht/dkp/200503dkp_b.pdf Do banks diversify loan portfolios?], 2005 (on using e.g. the Gini coefficient for risk evaluation of loan portfolios)
* [http://www.forbes.com/forbes/2003/0317/098.html Forbes Article, In praise of inequality]
* [http://www.theresearchkitchen.com/archives/219 Measuring Software Project Risk With The Gini Coefficient], an application of the Gini coefficient to software
* [http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,,contentMDK:20238991~menuPK:492138~pagePK:148956~piPK:216618~theSitePK:430367,00.html The World Bank: Measuring Inequality]
* [http://utip.gov.utexas.edu/tutorials/theo_basic_ineq_measures.doc Travis Hale, University of Texas Inequality Project:The Theoretical Basics of Popular Inequality Measures], online computation of examples: [http://www.poorcity.richcity.org/calculator/?quantiles=7,*18000|10,*22000|280,*25000|15,*35000|15,*40000|50,*60000|10,*75000|6,*80000|4,*120000|2,*200000|1,1000000 1A], [http://www.poorcity.richcity.org/calculator/?quantiles=12,*15000|25,*20000|1000,*30000|35,*35000|100,*45000|80,*50000|10,*60000|25,*80000|8,*175000|4,*250000|1,5000000 1B]
* [http://image.guardian.co.uk/sys-files/Guardian/documents/2009/03/13/inequality.pdf Article from The Guardian analysing inequality in the UK 1974 – 2006]
* [http://www.wider.unu.edu/research/Database/en_GB/database/ World Income Inequality Database]
* [http://www.oecd.org/els/social/inequality/GU Income Distribution and Poverty in OECD Countries]
* [http://www.had2know.com/academics/gini-coefficient-calculator.html Gini Coefficient Calculator]
* [http://inequality.org/unequal-americas-income-distribution/ U.S. Income Distribution: Just How Unequal?]
 
{{Globalization}}
 
{{DEFAULTSORT:Gini Coefficient}}
[[Category:Demographic economics]]
[[Category:Economic indicators]]
[[Category:Economic inequality]]
[[Category:Income distribution]]
[[Category:Socioeconomics]]
[[Category:Summary statistics]]
[[Category:Welfare economics]]
[[Category:Distribution of wealth]]
[[Category:Globalization-related indices]]

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