Image by © 2018 European Union/Dominique Catton
  • Factsheet
  • 21 February 2022

Inequality: Global trends

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Authors

Deborah Hardoon , Elena Suckling

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Introduction

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:

  1. 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.
  2. Environmental resources are finite, so where wealth exists alongside deprivation, it is necessary to think about how available resources are distributed.
  3. 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.
  4. 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.

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Key facts

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
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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]
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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.

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Figure 1: Faster growth in lower income countries in recent decades has reduced between-country and global inequality

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.

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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.
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Figure 2: The poorest 50% of people own just 2% of wealth

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]

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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]

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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

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.

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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]

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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

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]

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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]

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Figure 5: LDCs receive a lower share of ODA than the proportion of extreme poverty they represent

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

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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]
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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]

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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

Figure 6: Inequality of income within countries as measured by the ratio of the income of the richest 10% to the poorest 50%, 2021
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]

Notes

  • 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
  • 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