GINI index (World Bank estimate) - Country Ranking

Definition: Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

Source: World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldban

See also: Thematic map, Time series comparison

Find indicator:
Rank Country Value Year
1 South Africa 63.00 2014
2 Namibia 59.10 2015
3 Suriname 57.90 1999
4 Zambia 57.10 2015
5 Central African Republic 56.20 2008
6 Eswatini 54.60 2016
7 Colombia 54.20 2020
8 Mozambique 54.00 2014
9 Botswana 53.30 2015
9 Belize 53.30 1999
11 Angola 51.30 2018
12 St. Lucia 51.20 2016
13 Zimbabwe 50.30 2019
14 Panama 49.80 2019
15 Costa Rica 49.30 2020
16 Brazil 48.90 2020
16 Congo 48.90 2011
18 Guatemala 48.30 2014
19 Honduras 48.20 2019
20 Ecuador 47.30 2020
20 Burkina Faso 47.30 2018
22 Cameroon 46.60 2014
23 Nicaragua 46.20 2014
24 Jamaica 45.50 2004
25 Mexico 45.40 2020
26 Comoros 45.30 2014
27 Guyana 45.10 1998
28 Chile 44.90 2020
28 Lesotho 44.90 2017
30 Venezuela 44.80 2006
31 Peru 43.80 2020
32 Rwanda 43.70 2016
33 Bolivia 43.60 2020
34 Paraguay 43.50 2020
34 Ghana 43.50 2016
36 Uganda 42.70 2019
37 Madagascar 42.60 2012
38 Cabo Verde 42.40 2015
38 Togo 42.40 2018
40 Philippines 42.30 2018
40 Argentina 42.30 2020
42 Dem. Rep. Congo 42.10 2012
43 Papua New Guinea 41.90 2009
43 Turkey 41.90 2019
45 Djibouti 41.60 2017
46 United States 41.50 2019
47 Malaysia 41.10 2015
47 Haiti 41.10 2012
49 Iran 40.90 2019
50 Turkmenistan 40.80 1998
50 Kenya 40.80 2015
52 São Tomé and Principe 40.70 2017
53 Tanzania 40.50 2018
54 Bulgaria 40.30 2019
54 Trinidad and Tobago 40.30 1992
56 Uruguay 40.20 2020
57 Dominican Republic 39.60 2020
58 Morocco 39.50 2013
59 Sri Lanka 39.30 2016
60 Tuvalu 39.10 2010
61 Lao PDR 38.80 2018
61 El Salvador 38.80 2019
63 Samoa 38.70 2013
64 Israel 38.60 2018
64 Burundi 38.60 2013
66 Malawi 38.50 2019
67 China 38.20 2019
68 Senegal 38.10 2018
69 Gabon 38.00 2017
70 Benin 37.80 2018
71 Tonga 37.60 2015
72 Syrian Arab Republic 37.50 2003
72 Chad 37.50 2018
74 Bhutan 37.40 2017
75 Niger 37.30 2018
75 Indonesia 37.30 2021
77 Côte d'Ivoire 37.20 2018
78 Solomon Islands 37.10 2012
79 Somalia 36.80 2017
79 Montenegro 36.80 2018
79 Mauritius 36.80 2017
82 Yemen 36.70 2014
83 Mali 36.10 2018
84 Russia 36.00 2020
85 The Gambia 35.90 2015
86 India 35.70 2011
86 Sierra Leone 35.70 2018
86 Vietnam 35.70 2018
89 Uzbekistan 35.30 2003
89 Liberia 35.30 2016
89 Lithuania 35.30 2019
92 Italy 35.20 2018
93 Nigeria 35.10 2018
93 United Kingdom 35.10 2017
95 Ethiopia 35.00 2015
95 Thailand 35.00 2020
97 Romania 34.80 2019
97 Guinea-Bissau 34.80 2018
97 Nauru 34.80 2012
100 Serbia 34.50 2019
100 Latvia 34.50 2019
100 Georgia 34.50 2020
103 Spain 34.30 2019
103 Australia 34.30 2018
105 Luxembourg 34.20 2019
105 Sudan 34.20 2014
107 Tajikistan 34.00 2015
108 Jordan 33.70 2010
109 Canada 33.30 2017
110 Switzerland 33.10 2018
110 Greece 33.10 2019
112 Bosnia and Herzegovina 33.00 2011
112 North Macedonia 33.00 2018
114 Japan 32.90 2013
115 Portugal 32.80 2019
115 Nepal 32.80 2010
115 Tunisia 32.80 2015
118 Mongolia 32.70 2018
119 Mauritania 32.60 2014
120 France 32.40 2018
120 Bangladesh 32.40 2016
122 Vanuatu 32.30 2019
123 Seychelles 32.10 2018
124 Lebanon 31.80 2011
125 Germany 31.70 2018
126 Egypt 31.50 2017
127 Korea 31.40 2016
128 Cyprus 31.20 2019
129 Malta 31.00 2019
130 Albania 30.80 2019
130 Estonia 30.80 2019
132 Myanmar 30.70 2017
132 Fiji 30.70 2019
134 Ireland 30.60 2018
135 Poland 30.20 2018
135 Austria 30.20 2019
137 Hungary 30.00 2019
138 Pakistan 29.60 2018
138 Guinea 29.60 2018
140 Iraq 29.50 2012
141 Sweden 29.30 2019
142 Netherlands 29.20 2019
143 Kyrgyz Republic 29.00 2020
144 Croatia 28.90 2019
145 Timor-Leste 28.70 2014
146 Kiribati 27.80 2019
146 Kazakhstan 27.80 2018
148 Norway 27.70 2019
148 Finland 27.70 2019
148 Denmark 27.70 2019
151 Algeria 27.60 2011
152 Belgium 27.20 2019
153 Azerbaijan 26.60 2005
154 Iceland 26.10 2017
155 Moldova 26.00 2019
155 United Arab Emirates 26.00 2018
157 Ukraine 25.60 2020
158 Czech Republic 25.30 2019
159 Armenia 25.20 2020
160 Belarus 24.40 2020
160 Slovenia 24.40 2019
162 Slovak Republic 23.20 2019

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Limitations and Exceptions: Gini coefficients are not unique. It is possible for two different Lorenz curves to give rise to the same Gini coefficient. Furthermore 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. Another limitation of the Gini coefficient is that it is not additive across groups, i.e. the total Gini of a society is not equal to the sum of the Gini's for its sub-groups. Thus, country-level Gini coefficients cannot be aggregated into regional or global Gini's, although a Gini coefficient can be computed for the aggregate. Because the underlying household surveys differ in methods and types of welfare measures collected, data are not strictly comparable across countries or even across years within a country. Two sources of non-comparability should be noted for distributions of income in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (number of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution. World Bank staff have made an effort to ensure that the data are as comparable as possible. Wherever possible, consumption has been used rather than income. Income distribution and Gini indexes for high-income economies are calculated directly from the Luxembourg Income Study database, using an estimation method consistent with that applied for developing countries.

Statistical Concept and Methodology: The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality. The Gini index provides a convenient summary measure of the degree of inequality. Data on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data. The distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. No adjustment has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and middle-income economies, see Ravallion and Chen (1996). Survey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected.

Unit of Measure: %

Periodicity: Annual

General Comments: The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (indu