Inequality: Global trends
This factsheet presents trends and statistics that draw on a number of different datasets and use a variety of measures to understand the levels and trends of inequality.
DownloadsIntroduction
Inequality is all about the distribution of power and resources, of the rights people can exercise, and opportunities they can access. Some amount of inequality is inevitable. But inequality is problematic when it is of a degree that prevents people from living decent lives and fulfilling their rights.
Inequality is closely linked to poverty. We cannot hope to reduce poverty without addressing inequality. The relationship between the two is complex and includes a number of dimensions:
- Poverty is relative. The context in which a person lives and the outcomes of others in their community has an important impact on their experience and what is necessary for them to participate fully in their society.
- Environmental resources are finite, so where wealth exists alongside deprivation, it is necessary to think about how available resources are distributed.
- Recognising and understanding horizontal inequalities, where people face exclusion and discrimination based on their identity, is critical to tackling the root cause of poverty and to Leave No One Behind.
- Economic inequality is closely linked to political inequalities, which create a self-perpetuating cycle, reinforcing division in society as the poorest people have less influence over political decision-making than the wealthiest people.
There is no single measure that can capture all aspects of inequality, nor a single dataset that provides comprehensive and timely data to underpin all inequality measures. As such, the facts and statistics included in this factsheet draw on a number of different datasets and use a variety of measures to understand the levels and trends of inequality. Each inequality measure and underlying dataset has its strengths and limitations and should be understood and interpreted based on this.
Our accompanying briefing paper explains in more detail how understanding and tackling inequality is critical to reducing poverty. It also provides a summary of inequality indicators and associated data issues.
This factsheet is the second in a series of papers produced as part of our work to reduce poverty and inequality. For more information, read our first factsheet in the series, Poverty trends: global, regional and national.
Key facts
- Global inequality: Globally, the world is vastly unequal, with extreme wealth coexisting with extreme poverty. The poorest 50% of the global population share just 8.5% of total income. At the same time, the richest 10% of the global population earn over 50% of total income. Go to Fact 1.
- Between-country income inequality: Between countries, income inequality is high, but the gap is narrowing. Between-country inequality accounts for two-thirds of global income inequality. However, between country inequality has decreased as previously low-income countries such as China and India have experienced faster growth than higher income countries. Go to Fact 2.
- Wealth inequality: The concentration of wealth inequality has intensified during the Covid-19 pandemic. When measuring economic inequality by wealth, the gap is even bigger than that measured by income. The wealthiest 10% of people in the world own 76% of total wealth. During the Covid-19 pandemic, billionaires around the world added US$1.9 and US$1.6 trillion to their net wealth in 2020 and 2021, respectively. Go to Fact 3.
- Covid-19: The Covid-19 pandemic threatens to exacerbate and intensify the disadvantage of lower income countries. Despite initially impacting richer countries hardest, lower income countries face a much harder recovery as they have lower financial capacities to fund the economic recovery and much lower access to vaccines. Go to Fact 4.
- Climate change: Lower income countries, which did the least to cause climate change, will face the biggest costs. Between-country inequalities are likely to grow as the devastating and costly impacts of climate change are felt more acutely in lower income countries – those that did the least to cause it. Go to Fact 5.
- Finance: Development finance could be better used as a tool to tackle inequality. Finance that redistributes resources from higher to lower income countries to reach the poorest people can help reduce inequalities between and within countries. Official development assistance (ODA), in particular, is conceptually well placed to tackle the most complex needs in least developed countries (LDC), but its current scale and allocations fall short of this potential. Go to Fact 6.
- Horizontal inequalities: Economic inequalities intersect horizontal inequalities. Personal characteristics, such as gender, age, disability status, ethnicity, religion, migrant status and/or geography, can also intersect to exacerbate inequalities experienced by particular individuals and groups. Go to Fact 7.
- Within-country inequality: Inequalities within countries are influenced by a number of factors. The level of inequality within any given country or community depends upon numerous structural and contextual factors. Policy responses can also have a significant impact on inequality. Go to Fact 8.
Global inequality
Globally, the world is vastly unequal, with extreme wealth coexisting with extreme poverty
Global income is estimated at PPP$122 trillion, and the global adult population is 5.1 billion.[1] If this income was shared equally between all adults around the world, this would equate to PPP$23,380 yearly.[2]
The reality is very different from this.
- People in the poorest 50% of the global population share just 8.5% of global income between them. They earn on average PPP$3,920 per person, per year ($10 per day).
- Meanwhile, the richest 10% of people in the world are estimated to have more than half of global income (52%), earning on average PPP$122,100 per person, per year ($334 per day).[3]
Between-country income inequality
Between countries, income inequality is high, but the gap is narrowing
Between-country inequality is the difference in average incomes in different countries. The country in which you are born is a critical factor in where you are likely to be on the global income distribution. Between-country inequality accounts for two-thirds of global income inequality.
Between-country income inequality has narrowed in recent decades as previously low-income and high population countries such as China and India have experienced faster growth than higher income countries.
Figure 1: Faster growth in lower income countries in recent decades has reduced between-country and global inequality

Max of gini |
Column Labels | ||
---|---|---|---|
Row Labels | World | World (between-countries) | World (within-countries) |
1990 | 70.019 | 61.397 | 42.45 |
1991 | 70.244 | 61.291 | 43.354 |
1992 | 70.13 | 60.896 | 43.714 |
1993 | 69.94 | 60.369 | 44.013 |
1994 | 69.867 | 60.168 | 44.295 |
1995 | 69.571 | 59.695 | 44.543 |
1996 | 69.221 | 59.271 | 44.656 |
1997 | 68.978 | 59.143 | 44.547 |
1998 | 69.034 | 59.114 | 44.731 |
1999 | 68.872 | 58.885 | 44.873 |
2000 | 68.815 | 58.854 | 44.994 |
2001 | 68.487 | 58.332 | 45.225 |
2002 | 68.164 | 57.81 | 45.502 |
2003 | 67.737 | 57.215 | 45.63 |
2004 | 67.308 | 56.642 | 45.644 |
2005 | 66.848 | 55.974 | 45.754 |
2006 | 66.313 | 55.246 | 45.77 |
2007 | 65.699 | 54.341 | 45.736 |
2008 | 65.039 | 53.537 | 45.581 |
2009 | 63.882 | 51.86 | 45.538 |
2010 | 63.372 | 51.202 | 45.497 |
2011 | 63.006 | 50.697 | 45.442 |
2012 | 62.625 | 50.059 | 45.477 |
2013 | 62.193 | 49.422 | 45.412 |
2014 | 61.757 | 48.858 | 45.25 |
2015 | 61.539 | 48.425 | 45.279 |
2016 | 61.199 | 47.842 | 45.313 |
2017 | 61.006 | 47.466 | 45.341 |
2018 | 60.941 | 47.253 | 45.484 |
2019 | 60.736 | 47.069 | 45.403 |
Source: https://www.wider.unu.edu/database/world-income-inequality-database-wiid
Note: Inequality as measured by the Gini coefficient.
Wealth inequality
The concentration of wealth inequality has intensified during the Covid-19 pandemic
When measuring global economic inequality by wealth, the gap is even bigger than that measured by income. Wealth includes the value of financial and non-financial assets, net of debt. Wealth can provide the means for people to be able to respond to any financial shock, such as an unexpected medical bill or poor harvest, as well as to invest in their future.
- The poorest 50% of the population own just 2% of total net wealth, an average of PPP$4,100 per adult in 2021.[4]
- The middle 40% of people own 22% of total net wealth, an average of PPP$46,600 per adult in 2021.
- The richest 10% of people own 76% of total net wealth, an average of PPP $771,300 per adult in 2021.
Figure 2: The poorest 50% of people own just 2% of wealth

Share of wealth |
Wealth in US$ | |
Richest 10% of people |
76% | $771,300 |
Poorest 50% of people |
2% | $4,100 |
Middle 40% of people |
22% | $46,600 |
Source: World Inequality Lab, 2021. World Inequality Report 2022. Page 22. Available at: https://wir2022.wid.world/www-site/uploads/2021/12/Summary_WorldInequalityReport2022_English.pdf
Between countries, the difference is also stark. Countries in North America and Europe together account for 57% of total household wealth but contain only 17% of the world adult population. Conversely, countries in Africa account for 1% of wealth and 13% of the adult population.
Despite the contraction in economic output in most economies in 2020 due to the Covid-19 pandemic, total global wealth grew by 7.4% in 2020, totalling US$418.3 trillion. This wealth accumulation was concentrated at the very top of the global distribution, where people are already wealthy. Billionaires added US$1.9 and US$1.6 trillion to their net wealth in 2020 and 2021, respectively.
The people whose assets increased during the Covid-19 pandemic live disproportionately in richer countries. Total wealth grew in countries in North America (10%), Europe (9.8%), and East Asia and Pacific (6.7%), while decreasing in Latin America (−10.1%). Total wealth grew in China (6%) and decreased in India (-4.4%). There was small growth in Africa (0.7%), driven by a small decrease of household debt. Debt also decreased in Latin America and India, while increasing in the other regions, but changes in debt represented a much smaller proportion of the change in overall net wealth compared with the change in the value of financial and non-financial assets.[5]
Covid-19
The Covid-19 pandemic threatens to exacerbate and intensify the disadvantage of lower income countries
Income shocks during the Covid-19 pandemic varied depending on where you lived. Higher income countries were generally worst affected by the health and economic impacts of the virus during the first waves of the pandemic in 2020. This reduced between-country inequalities in 2020.
However, as the virus continues to sweep across the world, the longer-term between-country impacts are becoming clearer. Higher-income countries have navigated the impacts of the pandemic through stimulus packages, estimated at over US$12 trillion globally. Meanwhile, in LDCs the stimulus packages per person are 580 times less than richer countries.[6] Within countries, the longer-term economic impacts of stimulus packages would still need to consider distributive impacts, taking into account how increases in public debt are managed and affect different groups in the population.
The absence of income support for countries in Africa and Asia has particularly affected vulnerable groups such as women, minorities and young people. An estimated 50 million more people were pushed into income poverty between 2019 and 2020 in the lowest income countries,[7] where there is an absence of both established social protection mechanisms as well as those put in place as part of the Covid-19 response.
Compounding the inequality of recovery packages is the inequality in access to vaccines needed to protect the health of the population to restore social and economic behaviour. In low-income countries, 10% of people have been vaccinated, in comparison to almost 78% of people in high-income countries[8]. Economic recovery is predicted to be faster for countries with higher vaccination rates, with a US$7.93 billion increase in global gross domestic product (GDP) for every million people vaccinated. Poorer countries are not estimated to achieve pre-Covid-19 levels of growth until 2024.[9]
Figure 3: Due to vaccine hoarding by high-income countries, people living in low-income countries are much less likely to access a Covid-19 vaccination

Entity | Code | Day |
Total vaccinations per hundred people |
GDP per capita, PPP (constant 2011 international $) |
---|---|---|---|---|
Argentina | ARG | 13/01/2022 | 178.86 |
$ 18,933.91 |
Aruba | ABW | 13/01/2022 | 153.5 |
$ 35,973.78 |
Australia | AUS | 13/01/2022 | 176.02 |
$ 44,648.71 |
Austria | AUT | 13/01/2022 | 187.71 |
$ 45,436.69 |
Azerbaijan | AZE | 13/01/2022 | 113.39 |
$ 15,847.42 |
Bahrain | BHR | 13/01/2022 | 188.57 |
$ 43,290.70 |
Brazil | BRA | 13/01/2022 | 158.54 |
$ 14,103.45 |
Bulgaria | BGR | 13/01/2022 | 57.59 |
$ 18,563.31 |
Canada | CAN | 13/01/2022 | 191.65 |
$ 44,017.59 |
China | CHN | 13/01/2022 | 202.44 |
$ 15,308.71 |
Curacao | CUW | 13/01/2022 | 141.87 | |
Czechia | CZE | 13/01/2022 | 152.48 |
$ 32,605.91 |
Estonia | EST | 13/01/2022 | 115.32 |
$ 29,481.25 |
Faeroe Islands |
FRO | 13/01/2022 | 205.12 | |
Georgia | GEO | 13/01/2022 | 65.24 |
$ 9,745.08 |
Germany | DEU | 13/01/2022 | 187.28 |
$ 45,229.25 |
Greece | GRC | 13/01/2022 | 175.3 |
$ 24,574.38 |
Hong Kong | HKG | 13/01/2022 | 137.78 |
$ 56,054.92 |
Iceland | ISL | 13/01/2022 | 201.35 |
$ 46,482.96 |
India | IND | 13/01/2022 | 111.52 |
$ 6,426.67 |
Indonesia | IDN | 13/01/2022 | 106.11 |
$ 11,188.74 |
Isle of Man | IMN | 13/01/2022 | 214.33 | |
Israel | ISR | 13/01/2022 | 188.98 |
$ 33,132.32 |
Italy | ITA | 13/01/2022 | 196.11 |
$ 35,220.08 |
Japan | JPN | 13/01/2022 | 160.08 |
$ 39,002.22 |
Kazakhstan | KAZ | 13/01/2022 | 92.78 |
$ 24,055.59 |
Kyrgyzstan | KGZ | 13/01/2022 | 35.2 |
$ 3,393.47 |
Latvia | LVA | 13/01/2022 | 142.96 |
$ 25,063.85 |
Lebanon | LBN | 13/01/2022 | 68.96 |
$ 13,367.57 |
Lithuania | LTU | 13/01/2022 | 159.99 |
$ 29,524.26 |
Macao | MAC | 13/01/2022 | 151.81 |
$ 104,861.85 |
Malaysia | MYS | 13/01/2022 | 184.01 |
$ 26,808.16 |
Mongolia | MNG | 13/01/2022 | 161.82 |
$ 11,840.85 |
Montenegro | MNE | 13/01/2022 | 102.46 |
$ 16,409.29 |
New Zealand | NZL | 13/01/2022 | 167.7 |
$ 36,085.84 |
Pakistan | PAK | 13/01/2022 | 73.75 |
$ 5,034.71 |
Panama | PAN | 13/01/2022 | 140.98 |
$ 22,267.04 |
Philippines | PHL | 13/01/2022 | 105.66 |
$ 7,599.19 |
Poland | POL | 13/01/2022 | 129.94 |
$ 27,216.44 |
Portugal | PRT | 13/01/2022 | 198.31 |
$ 27,936.90 |
Russia | RUS | 13/01/2022 | 102.54 |
$ 24,765.95 |
Saint Lucia | LCA | 13/01/2022 | 57.76 |
$ 12,951.84 |
Saudi Arabia | SAU | 13/01/2022 | 151.48 |
$ 49,045.41 |
Slovenia | SVN | 13/01/2022 | 136.91 |
$ 31,400.84 |
South Korea | KOR | 13/01/2022 | 211.99 |
$ 35,938.37 |
Sri Lanka | LKA | 13/01/2022 | 160.46 |
$ 11,669.08 |
Suriname | SUR | 13/01/2022 | 83.48 |
$ 13,767.12 |
Sweden | SWE | 13/01/2022 | 178.18 |
$ 46,949.28 |
Trinidad and Tobago |
TTO | 13/01/2022 | 103.14 |
$ 28,763.07 |
Turkey | TUR | 13/01/2022 | 162.66 |
$ 25,129.34 |
Ukraine | UKR | 13/01/2022 | 67.03 |
$ 7,894.39 |
United Arab Emirates |
ARE | 13/01/2022 | 229.93 |
$ 67,293.48 |
United States | USA | 13/01/2022 | 157.89 |
$ 54,225.45 |
Uruguay | URY | 13/01/2022 | 203.93 |
$ 20,551.41 |
Uzbekistan | UZB | 13/01/2022 | 118.58 |
$ 6,253.10 |
Zambia | ZMB | 13/01/2022 | 10.78 |
$ 3,689.25 |
Zimbabwe | ZWE | 13/01/2022 | 49.09 |
$ 1,899.77 |
Source: Our World in Data. COVID-19 vaccine doses administered vs GDP per capita. Available at: https://ourworldindata.org/grapher/covid-vaccinations-vs-gdp-per-capita. Data from 13 January 2022.
Climate change
Lower income countries, which did the least to cause climate change, will face the biggest costs
Carbon emission data finds that the highest emitters, and those most responsible for climate change, are people with the highest incomes. The 3.8 billion people that make up the poorest 50% of people contribute to just 12% of total carbon emissions. Meanwhile, the richest 10% of people on the planet, 771 million people, are responsible for 47.6% of global carbon emissions.[10]
Historically, global carbon inequality was mostly due to differences between countries,[11] whereby the average citizen in a richer country emitted more carbon than the average citizen in a poorer country. But, within-country inequalities account for nearly two-thirds of global emissions inequality, as the highest emitters within any given country pull away from the rest. Since 1990, the emissions of the richest 1% of individuals around the world grew by 26% and the emissions of the top 0.01% grew by more than 110%.
All countries will need to adapt to climate change in one way or another, but climate change will disproportionately affect low-income countries, despite them having emitted the least carbon emissions historically. This is largely due to the geography and climate in low-income countries, as well as socioeconomic conditions, dependence on natural resources and limited adaptation capacities. Research estimates that even if warming is limited to a 1.5°C target,[12] climate change could cause a 33.1% GDP hit to the world's most vulnerable countries by 2100, the worst projected being Sudan.[13] Within countries, the impacts of climate change will also differ, with the poorest people likely to be most impacted due to the impacts on food systems and climate-sensitive livelihood activities such as farming, fishing, livestock production and small-scale trade.[14]
Figure 4: People in countries with the lowest incomes per capita, who have contributed the least carbon emissions, are the most vulnerable to the impacts of climate change

Country code |
Country name | GDP 2020 |
Vulnerablity 2019 |
Annual CO2 emissions per capita |
---|---|---|---|---|
AFG | Afghanistan | 1978.96158 | 0.579588112 | 0.3124 |
AGO | Angola | 6198.08384 | 0.505861227 | 0.6754 |
ALB | Albania | 13295.4109 | 0.411491957 | 1.5757 |
ARG | Argentina | 19686.5237 | 0.391301865 | 3.4733 |
ARM | Armenia | 12592.6354 | 0.380703203 | 1.9878 |
ATG | Antigua and Barbuda | 17956.3157 | 0.467726489 | 4.3952 |
AUS | Australia | 48697.837 | 0.30571092 | 15.3684 |
AUT | Austria | 52119.8483 | 0.266655264 | 6.7324 |
AZE | Azerbaijan | 13699.6656 | 0.397940296 | 3.7203 |
BDI | Burundi | 731.06323 | 0.558512241 | 0.0506 |
BEL | Belgium | 48210.0331 | 0.320963267 | 7.2262 |
BEN | Benin | 3323.14445 | 0.577186594 | 0.5529 |
BFA | Burkina Faso | 2160.51153 | 0.543886886 | 0.1899 |
BGD | Bangladesh | 4818.09474 | 0.543013359 | 0.5637 |
BGR | Bulgaria | 22383.8055 | 0.33844493 | 5.3888 |
BHR | Bahrain | 40933.3527 | 0.444411972 | 20.5456 |
BHS | Bahamas | 30764.116 | 0.443524542 | 5.9446 |
BIH |
Bosnia and Herzegovina |
14339.8312 | 0.36422786 | 6.5282 |
BLR | Belarus | 19148.1751 | 0.330593575 | 6.0793 |
BLZ | Belize | 6119.88769 | 0.451228806 | 1.4657 |
BOL |
Bolivia, Plurinational State of |
7931.75431 | 0.467941809 | 1.7733 |
BRA | Brazil | 14063.9825 | 0.38098252 | 2.1988 |
BRB | Barbados | 12870.0425 | 0.378420768 | 3.7817 |
BRN | Brunei Darussalam | 62243.5832 | 0.358042116 | 23.2203 |
BTN | Bhutan | 10909.1002 | 0.527748252 | 2.4953 |
BWA | Botswana | 16040.0085 | 0.450168399 | 2.7721 |
CAF |
Central African Republic |
928.589508 | 0.560927149 | 0.0389 |
CAN | Canada | 45900.1829 | 0.291767926 | 14.1969 |
CHE | Switzerland | 68752.7702 | 0.249922869 | 3.7319 |
CHL | Chile | 23324.5248 | 0.316738315 | 4.2462 |
CHN | China | 16410.7978 | 0.387506874 | 7.4117 |
CIV | Côte d’Ivoire | 5174.10055 | 0.509330622 | 0.3818 |
CMR | Cameroon | 3576.3495 | 0.472457409 | 0.2595 |
COD |
Congo, the Democratic Republic of the |
1072.21011 | 0.591716885 | 0.0277 |
COG | Congo | 3449.1457 | 0.512200941 | 0.5648 |
COL | Colombia | 13441.493 | 0.408777988 | 1.7512 |
COM | Comoros | 3140.69877 | 0.525009526 | 0.2972 |
CPV | Cape Verde | 6045.06089 | 0.423424829 | 0.9891 |
CRI | Costa Rica | 19679.2886 | 0.359802061 | 1.5523 |
CYP | Cyprus | 37655.1804 | 0.347065636 | 5.3805 |
CZE | Czech Republic | 38509.2669 | 0.294589775 | 8.215 |
DEU | Germany | 51374.0274 | 0.284089903 | 7.6901 |
DJI | Djibouti | 5481.11482 | 0.477630022 | 0.3557 |
DMA | Dominica | 9891.29194 | 0.417229888 | 1.9343 |
DNK | Denmark | 55819.9095 | 0.34025778 | 4.5224 |
DOM | Dominican Republic | 17003.013 | 0.42324059 | 2.5599 |
DZA | Algeria | 10681.6793 | 0.387209304 | 3.5346 |
ECU | Ecuador | 10329.1988 | 0.43853988 | 1.7532 |
EGY | Egypt | 11951.4475 | 0.439372528 | 2.0859 |
ESP | Spain | 36219.9384 | 0.286944196 | 4.4683 |
EST | Estonia | 35215.3638 | 0.341306188 | 7.8795 |
ETH | Ethiopia | 2296.82735 | 0.558170494 | 0.1276 |
FIN | Finland | 47167.4272 | 0.282248841 | 7.0907 |
FJI | Fiji | 10997.4735 | 0.421694334 | 1.5544 |
FRA | France | 42313.1931 | 0.289919399 | 4.2381 |
GAB | Gabon | 14399.8688 | 0.417783896 | 1.9311 |
GBR | United Kingdom | 41627.1293 | 0.287300608 | 4.8549 |
GEO | Georgia | 14089.3023 | 0.387791157 | 2.4988 |
GHA | Ghana | 5304.98353 | 0.454960616 | 0.515 |
GIN | Guinea | 2670.82336 | 0.529609978 | 0.2584 |
GMB | Gambia | 2159.44191 | 0.529798143 | 0.2069 |
GNB | Guinea-Bissau | 1847.46582 | 0.628736705 | 0.1457 |
GNQ | Equatorial Guinea | 17007.6248 | 0.443817708 | 7.3167 |
GRC | Greece | 27287.0834 | 0.318850311 | 5.0115 |
GRD | Grenada | 15065.872 | 0.37441438 | 2.6203 |
GTM | Guatemala | 8393.28464 | 0.449136785 | 1.0571 |
GUY | Guyana | 18679.9802 | 0.452389582 | 2.8131 |
HND | Honduras | 5138.3854 | 0.461282088 | 0.9753 |
HRV | Croatia | 26465.1273 | 0.365502557 | 4.1366 |
HTI | Haiti | 2773.08136 | 0.529586966 | 0.256 |
HUN | Hungary | 31007.7684 | 0.351340563 | 4.9973 |
IDN | Indonesia | 11444.9607 | 0.44583404 | 2.1552 |
IND | India | 6118.35733 | 0.50279681 | 1.7694 |
IRL | Ireland | 90624.719 | 0.315245603 | 6.7538 |
IRN |
Iran, Islamic Republic of |
12433.297 | 0.388783716 | 8.8702 |
IRQ | Iraq | 9255.2569 | 0.435669171 | 5.2416 |
ISL | Iceland | 52381.1127 | 0.314021092 | 8.6036 |
ISR | Israel | 38341.2978 | 0.315452833 | 6.5104 |
ITA | Italy | 38992.1484 | 0.313800521 | 5.0249 |
JAM | Jamaica | 8741.55044 | 0.42430077 | 2.509 |
JOR | Jordan | 9816.55453 | 0.374988567 | 2.498 |
JPN | Japan | 39715.9339 | 0.360559387 | 8.1499 |
KAZ | Kazakhstan | 25337.1524 | 0.342450339 | 15.5158 |
KEN | Kenya | 4220.44025 | 0.51779596 | 0.3003 |
KGZ | Kyrgyzstan | 4706.57024 | 0.34167505 | 1.7639 |
KHM | Cambodia | 4191.85 | 0.521813145 | 0.9167 |
KNA | Saint Kitts and Nevis | 23259.3623 | 0.424462073 | 3.9863 |
KOR | Korea, Republic of | 42251.4451 | 0.365724746 | 11.6562 |
LAO |
Lao People’s Democratic Republic |
7805.79856 | 0.514142379 | 4.6521 |
LBN | Lebanon | 11649.0501 | 0.411667447 | 3.8048 |
LBR | Liberia | 1353.84292 | 0.605374838 | 0.1995 |
LBY | Libya | 10282.2911 | 0.418919659 | 7.3815 |
LCA | Saint Lucia | 12270.0133 | 0.35020865 | 2.3956 |
LKA | Sri Lanka | 12536.9418 | 0.469694686 | 0.9857 |
LSO | Lesotho | 2279.89587 | 0.458510889 | 1.0192 |
LTU | Lithuania | 36732.0347 | 0.361775733 | 5.0691 |
LUX | Luxembourg | 110261.157 | 0.288584143 | 13.059 |
LVA | Latvia | 29932.4939 | 0.379479006 | 3.5907 |
MAR | Morocco | 6916.34641 | 0.377276396 | 1.7484 |
MDA | Moldova, Republic of | 12324.7363 | 0.413718635 | 1.2759 |
MDG | Madagascar | 1510.14173 | 0.545524785 | 0.1329 |
MDV | Maldives | 13049.0467 | 0.493145839 | 3.3235 |
MEX | Mexico | 17887.7507 | 0.403547365 | 2.7686 |
MKD |
Macedonia, the former Yugoslav Republic of |
15848.4193 | 0.358684661 | 3.4302 |
MLI | Mali | 2216.77326 | 0.597628098 | 0.1674 |
MLT | Malta | 39222.1434 | 0.319384401 | 3.6121 |
MMR | Myanmar | 4544.02157 | 0.537252614 | 0.6676 |
MNE | Montenegro | 18278.7308 | 0.352997428 | 3.6778 |
MNG | Mongolia | 11470.6738 | 0.390005159 | 26.978 |
MOZ | Mozambique | 1229.08002 | 0.513030465 | 0.2102 |
MRT | Mauritania | 4983.22063 | 0.558400739 | 0.7263 |
MUS | Mauritius | 19469.5246 | 0.420998508 | 3.129 |
MWI | Malawi | 1486.77825 | 0.547513577 | 0.0729 |
MYS | Malaysia | 26435.1716 | 0.368239194 | 8.4226 |
NAM | Namibia | 8893.81316 | 0.469830681 | 1.5259 |
NER | Niger | 1196.87756 | 0.676519382 | 0.0698 |
NGA | Nigeria | 4916.72138 | 0.492881126 | 0.6086 |
NIC | Nicaragua | 5280.14058 | 0.445134906 | 0.7659 |
NLD | Netherlands | 54325.5088 | 0.33797245 | 8.0596 |
NOR | Norway | 63583.736 | 0.249118552 | 7.615 |
NPL | Nepal | 3800.0657 | 0.509857 | 0.582 |
NZL | New Zealand | 42404.3669 | 0.279654549 | 6.9418 |
PAK | Pakistan | 4622.77077 | 0.518371679 | 1.0628 |
PAN | Panama | 25381.8485 | 0.387167414 | 2.4983 |
PER | Peru | 11260.8458 | 0.437971735 | 1.3559 |
PHL | Philippines | 7953.58164 | 0.462078295 | 1.2413 |
PNG | Papua New Guinea | 4101.21888 | 0.524371844 | 0.7435 |
POL | Poland | 32238.1573 | 0.316538303 | 7.916 |
PRT | Portugal | 32177.9651 | 0.318927296 | 3.9609 |
PRY | Paraguay | 12335.4724 | 0.397479445 | 1.0613 |
QAT | Qatar | 85266.2106 | 0.36044902 | 37.0193 |
ROU | Romania | 28832.6232 | 0.392210115 | 3.7154 |
RUS | Russian Federation | 26456.3879 | 0.331008044 | 10.8072 |
RWA | Rwanda | 2098.71036 | 0.565902738 | 0.0797 |
SAU | Saudi Arabia | 44328.1839 | 0.38924083 | 17.9672 |
SDN | Sudan | 4022.86597 | 0.614840261 | 0.4301 |
SEN | Senegal | 3300.08549 | 0.526792155 | 0.6242 |
SGP | Singapore | 93397.0488 | 0.38063028 | 7.778 |
SLB | Solomon Islands | 2482.87192 | 0.56394288 | 0.435 |
SLE | Sierra Leone | 1648.05336 | 0.563136538 | 0.11 |
SLV | El Salvador | 8056.54309 | 0.441713928 | 0.9441 |
SOM | Somalia | 829.611429 | 0.676129752 | 0.0354 |
SRB | Serbia | 18210.0046 | 0.418430759 | 4.9369 |
STP | Sao Tome and Principe | 4051.60484 | 0.513810882 | 0.5144 |
SUR | Suriname | 16130.1708 | 0.379882861 | 3.7914 |
SVK | Slovakia | 30330.0429 | 0.351769821 | 5.6286 |
SVN | Slovenia | 37091.0009 | 0.295303169 | 6.043 |
SWE | Sweden | 51003.2808 | 0.285423504 | 3.8255 |
SWZ | Swaziland | 8392.71756 | 0.511687423 | 0.8238 |
SYC | Seychelles | 24361.8939 | 0.430076163 | 4.9936 |
TCD | Chad | 1519.91236 | 0.621749328 | 0.0555 |
TGO | Togo | 2107.87726 | 0.504940779 | 0.2647 |
THA | Thailand | 17286.8666 | 0.419051601 | 3.6929 |
TJK | Tajikistan | 3657.57351 | 0.390458882 | 0.9906 |
TLS | Timor-Leste | 3181.13719 | 0.499023988 | 0.3987 |
TTO | Trinidad and Tobago | 23728.1587 | 0.357037267 | 25.3731 |
TUN | Tunisia | 9727.50426 | 0.382299988 | 2.3799 |
TUR | Turkey | 28384.9878 | 0.348106911 | 4.6573 |
TZA |
Tanzania, United Republic of |
2635.33589 | 0.519737848 | 0.1831 |
UGA | Uganda | 2177.59585 | 0.581164791 | 0.107 |
UKR | Ukraine | 12377.0173 | 0.368346143 | 4.8912 |
URY | Uruguay | 21608.4303 | 0.39220065 | 1.6812 |
USA | United States | 60235.728 | 0.321025616 | 14.2379 |
UZB | Uzbekistan | 6994.16941 | 0.380041268 | 3.3698 |
VNM | Viet Nam | 8200.33187 | 0.479539845 | 2.6126 |
VUT | Vanuatu | 2762.79139 | 0.540900355 | 0.591 |
WSM | Samoa | 6295.73184 | 0.479365067 | 1.239 |
ZAF | South Africa | 11466.1897 | 0.406496434 | 7.6204 |
ZMB | Zambia | 3270.03511 | 0.517449743 | 0.3575 |
ZWE | Zimbabwe | 2744.69076 | 0.518014089 | 0.7086 |
Source: Vulnerability scores from Notre Dame Global Adaptation Initiative. Available at: https://gain.nd.edu/our-work/country-index/download-data/ and, CO2 emissions from Our World in Data and GDP from World Bank.
Progress in the global economy has been built on a model of increasing consumption and resource extraction, which has led to catastrophic effects on our climate. In the context of almost 1 billion people still living below the extreme poverty line, it is essential that we look at the redistribution of resource, consumption and emissions to tackle poverty and, through this effort, also reduce the impact of climate change on vulnerable populations.[15]
Development finance
Development finance could be better used as a tool to tackle inequality
International development finance can redistribute money from high-income countries to low-income countries. When effectively targeted to reach the poorest people within a country it can also help to reduce inequalities within countries.
International development finance includes official flows, such as ODA grants and loans from international financial institutions; commercial flows, such as foreign direct investment (FDI); and private flows, such as remittances and philanthropy, which tend to originate in countries with higher incomes than where they are received.[16] The extent to which this finance can reduce inequality depends on the terms associated with the contracts and how the money is spent in country. Loans are an example of where an initial transfer of funds from a higher income country to a lower income country is followed by loan repayments and interest, which flow back in the opposite direction. Loans are making up an increasing share of development finance in countries such as Uganda and Kenya.[17] Progressive development finance can also be undermined by illicit financial flows, which flow from low-income countries to tax havens and other entities in higher income countries. In Africa, the billions of dollars lost to illicit financial flows are almost equal to ODA and FDI, undermining Africa’s ability to leave no one behind.[18]
ODA, in particular, is conceptually well placed to tackle the most complex needs in LDCs; it has a clear remit to deliberately transfer resources to the poorest people. The scale of ODA flows is small, estimated at $157 billion in 2020.[19] However, it remains the most important source of development finance in the most fragile places. Fewer domestic resources and less income from key international financial flows (such as FDI, remittances and tourism) put more reliance on ODA as a key resource for the funding of basic human capital investments that tackle poverty.
But the eligibility for ODA funding has been expanding in recent years to include higher income countries, and the competition for ODA across multiple demands has watered down the potential for these funds to have a laser-like focus on getting to the poorest people in the poorest places first. Instead, the very poorest countries received the least ODA per person living in poverty.[20] ODA has not adapted to reflect changing distributions of poverty and the impacts of the pandemic. In particular, while it is estimated that in 2019 over half of the population living in extreme poverty were in LDCs, these countries received only 29% of ODA.[21]
Figure 5: LDCs receive a lower share of ODA than the proportion of extreme poverty they represent

Bubble size | Year | Location | |
ODA to LDCs percent of total (country allocable only) |
32% | 2010 | 3 |
ODA to LDCs percent of total (country allocable only) |
29% | 2019 | 3 |
ODA to LDCs percent of total (country allocable only) as things stand |
29% | 2025 | 3 |
Share of people living in extreme poverty in LDCs |
31% | 2010 | 6 |
Share of people living in extreme poverty in LDCs |
52% | 2019 | 6 |
Share of people living in extreme poverty in LDCs |
57% | 2025 | 6 |
Source: Development Initiatives based on OECD DAC and World Bank PovcalNet.
Note: LDC = least developed country
Horizontal inequalities
Economic inequalities intersect horizontal inequalities
People are more likely to experience economic hardship when they are discriminated against because of identity, including gender, age, disability status, ethnicity, religion, migrant status and/or because of where they live. These horizontal inequalities also intersect to compound and exacerbate the inequalities felt by individuals and groups.[22] Evidence shows that inequalities are commonly experienced by particular groups:
- Children are more likely to live in poverty compared with adults. They make up one-third of the global population, but represent half of the population that lives on less than $1.90 a day. The poorest children are twice as likely to die during childhood compared with wealthier children. Even in richer countries, one in seven children still live in poverty.[23] Cuts to essential services during the pandemic will affect educational achievements and health and children from the poorest families that are unable to access IT equipment or broadband will be particularly affected.
- Women are disproportionately more likely to find themselves in the bottom of the economic distribution.[24] Gender inequality goes beyond the economic sphere to include legal and cultural discrimination and deprivations more likely to be faced by women including with respect to freedom of movement, land rights, access to education, maternal health, access to safe and legal abortions, gender-based violence, and political and decision-making power. A number of indices compare the extent to which women fall behind in different countries across a range of measures. Afghanistan is ranked lowest overall in terms of its gender gap index by the World Economic Forum, ranking lowest for economic participation and opportunity, with only 22.6% of women being active in the labour market and earning 16% less than men.[25]
- Persons with disabilities are more likely to experience negative socioeconomic outcomes, including poorer health, lower levels of employment and higher poverty rates. Children with disabilities face multiple forms of discrimination[26] within the education environment, resulting in lower school attendance rates and therefore lower education achievements, which ultimately impacts employment in adulthood. Prejudice creates cross-cutting challenges and can lead to social isolation. However, there is notorious difficulty in calculating precise statistics on people with disabilities[27] due to the complexity of defining disabilities and the poor quality of the data available.[28]
Within-country inequality
Inequalities within countries are influenced by a number of factors
The level of inequality within any given country or community, and its trend, depends on numerous structural and contextual factors. Policy interventions, that either directly or inadvertently affect people differently across the income distribution, can exacerbate or reduce the gaps in outcomes and opportunities.
For example, South Africa has one of the highest levels of inequality in the world, regardless of how you measure it. Structural inequalities from a legacy of discrimination under apartheid continue to determine the high levels of economic inequality. As a result, economic inequality is intrinsically connected to the horizontal inequality of race, with black Africans being most disadvantaged in employment and earning on average less than half of their white counterparts.[29] Even though apartheid was dismantled more than 25 years ago, the legacy of discrimination continues to affect future generations.
Where you live within a country can also change the inequality you may experience. The geographic inequalities within Kenya and Uganda are just as stark as the inequalities that are comparable between countries at a global level. In Kenya, 79% of people in Turkana live below the national poverty line compared with 17% in Nairobi.[30] Geographic inequalities are also evident in Uganda, where 97% of the population in Buvuma lives in poverty, compared with 4% in Kampala.[31]
Figure 6: Inequality of income within countries as measured by the ratio of the income of the richest 10% to the poorest 50%, 2021
Dark red countries have a higher (more unequal) ratio, while lighter yellow countries have a lower (less unequal) ratio

Year | Country | T10B50 |
---|---|---|
2021 | United Arab Emirates | 19.20401 |
2021 | Afghanistan | 11.66961 |
2021 | Albania | 8.989956 |
2021 | Armenia | 10.95756 |
2021 | Angola | 32.0927 |
2021 | Argentina | 13.18025 |
2021 | Austria | 7.679657 |
2021 | Australia | 10.3931 |
2021 | Azerbaijan | 9.630005 |
2021 |
Bosnia and Herzegovina |
9.32185 |
2021 | Bangladesh | 12.56127 |
2021 | Belgium | 8.060792 |
2021 | Burkina Faso | 15.71123 |
2021 | Bulgaria | 13.20016 |
2021 | Bahrain | 28.28058 |
2021 | Burundi | 17.26154 |
2021 | Benin | 23.97782 |
2021 | Brunei Darussalam | 9.82353 |
2021 | Bolivia | 19.25327 |
2021 | Brazil | 29.0747 |
2021 | Bahamas | 19.25328 |
2021 | Bhutan | 14.184 |
2021 | Botswana | 36.49285 |
2021 | Belarus | 7.417274 |
2021 | Belize | 19.25327 |
2021 | Canada | 13.05789 |
2021 | DR Congo | 19.31731 |
2021 |
Central African Republic |
42.52485 |
2021 | Congo | 28.20051 |
2021 | Switzerland | 7.17662 |
2021 | Cote d'Ivoire | 23.59909 |
2021 | Chile | 28.94271 |
2021 | Cameroon | 24.47231 |
2021 | China | 14.50512 |
2021 | Colombia | 24.20766 |
2021 | Costa Rica | 23.36557 |
2020 | Cuba | 11.86326 |
2021 | Cabo Verde | 19.82651 |
2021 | Cyprus | 9.494887 |
2021 | Czech Republic | 5.607577 |
2021 | Germany | 9.760151 |
2021 | Djibouti | 18.93213 |
2021 | Denmark | 7.918193 |
2021 | Dominican Republic | 19.25327 |
2021 | Algeria | 10.01204 |
2021 | Ecuador | 11.56246 |
2021 | Estonia | 9.522069 |
2021 | Egypt | 16.81582 |
2021 | Eritrea | 14.35813 |
2021 | Spain | 8.161876 |
2021 | Ethiopia | 14.35813 |
2021 | Finland | 7.900719 |
2021 | France | 7.09109 |
2021 | Gabon | 15.0223 |
2021 | United Kingdom | 8.766573 |
2021 | Georgia | 17.63293 |
2021 | Ghana | 20.02922 |
2021 | Gambia | 15.27091 |
2021 | Guinea | 13.18233 |
2021 | Equatorial Guinea | 22.54529 |
2021 | Greece | 7.759043 |
2021 | Guatemala | 19.25328 |
2021 | Guinea-Bissau | 31.34362 |
2021 | Guyana | 19.25328 |
2021 | Hong Kong | 17.72405 |
2021 | Honduras | 19.25327 |
2021 | Croatia | 9.613139 |
2021 | Haiti | 19.25328 |
2021 | Hungary | 7.691266 |
2021 | Indonesia | 19.36618 |
2021 | Ireland | 8.603584 |
2021 | Israel | 18.66862 |
2021 | India | 21.75767 |
2021 | Iraq | 20.35585 |
2021 | Iran | 19.82819 |
2021 | Iceland | 5.820321 |
2021 | Italy | 7.778762 |
2021 | Jamaica | 19.25328 |
2021 | Jordan | 17.33108 |
2021 | Japan | 13.37805 |
2021 | Kenya | 18.71654 |
2021 | Kyrgyzstan | 13.12427 |
2021 | Cambodia | 16.77386 |
2021 | Comoros | 22.05612 |
2020 | North Korea | 14.11285 |
2021 | Korea | 14.48125 |
2020 | Kosovo | 8.973072 |
2021 | Kuwait | 22.9941 |
2021 | Kazakhstan | 12.99736 |
2021 | Lao PDR | 19.25665 |
2020 | Lebanon | 26.64623 |
2021 | Sri Lanka | 17.52091 |
2021 | Liberia | 14.01174 |
2021 | Lesotho | 21.94127 |
2021 | Lithuania | 10.12634 |
2021 | Luxembourg | 8.303045 |
2021 | Latvia | 9.637069 |
2021 | Libya | 13.52975 |
2021 | Morocco | 18.22916 |
2021 | Moldova | 9.45135 |
2021 | Montenegro | 10.89054 |
2021 | Madagascar | 20.33344 |
2021 | North Macedonia | 6.978232 |
2021 | Mali | 12.62937 |
2021 | Myanmar | 13.83316 |
2021 | Mongolia | 14.83673 |
2021 | Macao | 14.50512 |
2021 | Mauritania | 12.05908 |
2021 | Malta | 7.931879 |
2021 | Mauritius | 16.00726 |
2021 | Maldives | 11.06386 |
2021 | Malawi | 23.93923 |
2021 | Mexico | 31.26212 |
2021 | Malaysia | 11.64152 |
2021 | Mozambique | 38.94406 |
2021 | Namibia | 49.03003 |
2021 | Niger | 13.77456 |
2021 | Nigeria | 13.78481 |
2021 | Nicaragua | 19.25327 |
2021 | Netherlands | 6.539901 |
2021 | Norway | 5.956634 |
2021 | Nepal | 12.57065 |
2021 | New Zealand | 8.832035 |
2021 | Oman | 139.5909 |
2021 | Panama | 19.25328 |
2021 | Peru | 22.24838 |
2021 | Papua New Guinea | 18.28115 |
2021 | Philippines | 16.09484 |
2021 | Pakistan | 12.52541 |
2021 | Poland | 9.694258 |
2021 | Palestine | 21.06058 |
2021 | Portugal | 8.785279 |
2021 | Paraguay | 19.25328 |
2021 | Qatar | 30.23827 |
2021 | Romania | 13.67213 |
2021 | Serbia | 9.652961 |
2021 | Russian Federation | 13.6709 |
2021 | Rwanda | 22.77526 |
2021 | Saudi Arabia | 24.65472 |
2021 | Seychelles | 21.47181 |
2021 | Sudan | 14.28164 |
2021 | Sweden | 6.471685 |
2021 | Singapore | 13.90012 |
2021 | Slovenia | 6.411677 |
2021 | Slovakia | 5.393878 |
2021 | Sierra Leone | 15.67551 |
2021 | Senegal | 17.8305 |
2021 | Somalia | 14.74409 |
2021 | Suriname | 19.25328 |
2021 | South Sudan | 20.99106 |
2021 |
Sao Tome and Principe |
11.25307 |
2021 | El Salvador | 17.58623 |
2020 | Syrian Arab Republic | 26.54566 |
2021 | Swaziland | 38.11222 |
2021 | Chad | 20.04688 |
2021 | Togo | 19.63543 |
2021 | Thailand | 17.56547 |
2021 | Tajikistan | 14.00482 |
2021 | Timor-Leste | 12.63401 |
2021 | Turkmenistan | 20.77022 |
2021 | Tunisia | 12.45055 |
2021 | Turkey | 22.77977 |
2021 | Trinidad and Tobago | 19.25327 |
2021 | Taiwan | 8.426919 |
2021 | Tanzania | 19.83795 |
2021 | Ukraine | 7.419922 |
2021 | Uganda | 21.46992 |
2021 | USA | 17.07761 |
2021 | Uruguay | 10.96074 |
2021 | Uzbekistan | 15.86599 |
2020 | Venezuela | 19.25327 |
2021 | Viet Nam | 15.39477 |
2021 | Yemen | 31.96306 |
2021 | South Africa | 63.09864 |
2021 | Zambia | 44.42086 |
2021 | Zimbabwe | 31.92477 |
2021 | Zanzibar | 19.83795 |
Source: World Inequality Lab, 2021. World Inequality Report 2022. Methodology. https://wir2022.wid.world/methodology/
Economic inequality within countries can increase over time for many reasons. Liberal economic policies in some high-income countries during the 1970s and 1980s were associated with increases in income inequality. For example, the top rate of tax in the United States fell from 70% to 28% during these years,[32] and the Gini coefficient also increased over this period from 0.35 to 0.37.[33] Structural adjustment programmes from international finance institutions, such as the World Bank and International Monetary Fund, also began in the 1980s. These led to a reduction in fiscal deficits but exacerbated inequality due to conditions set through lending programmes. For example, expenditure reduction targets were passed on as a reduction in incomes for low-income households that rely on government transfers.[34]
By understanding how different policies impact different groups, national governments can effectively reduce inequality, alongside the other objectives of any given policy. For example, investments in Universal Health Coverage can improve health outcomes, while disproportionately benefiting the most vulnerable people and thereby reducing inequalities. Similarly, social protection mechanisms can be deliberately targeted to reach the most vulnerable populations, including groups facing discrimination. Deliberately targeting spending towards people with the lowest incomes, or key human capital sectors, can be done through national,[35] or subnational spending,[36] while focussing revenue raising efforts on those that can afford it the most.
Governments in Brazil and other countries in Latin America, which had among the highest levels of inequality in the world in the 1990s, successfully reduced their countries’ inequality between 2000 and 2014.[37] They did this through progressive policies on social protection and education that targeted people with the lowest incomes. However, this progress has reversed in recent years, with the top 10% of people capturing 55% of national income.[38]
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- Datasets Inequality: Global trends
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1
PPP stands for Purchasing Power Parity. Purchasing Power Parity helps to measure how incomes in a particular country are measured against goods and services, compared to other countries. This can be measured through how much a basket of goods might cost in one country, compared to another. Calculating figures of PPP is done by the International Comparison Programme, with the last available PPP figures being from 2017.Return to source text
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2
World Inequality Lab, 2021. World Inequality Report 2022. Chapter 1, page 26. Available at: https://wir2022.wid.world/www-site/uploads/2021/12/Summary_WorldInequalityReport2022_English.pdfReturn to source text
-
3
PPP stands for Purchasing Power Parity. Purchasing Power Parity helps to measure how incomes in a particular country are measured against goods and services, compared to other countries. This can be measured through how much a basket of goods might cost in one country, compared to another. Calculating figures of PPP is done by the International Comparison Programme, with the last available PPP figures being from 2017.Return to source text
-
4
World Inequality Report 2022. Available at https://wir2022.wid.world/www-site/uploads/2021/12/WorldInequalityReport2022_Full_Report.pdfReturn to source text
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5
Credit Suisse, 2021. Why wealth matters. The global wealth report. Page 22. Available at: www.credit-suisse.com/about-us/en/reports-research/global-wealth-report.htmlReturn to source text
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