Distribution of wealth facts for kids

Global share of wealth by wealth group, Credit Suisse, 2021

World distribution of wealth, GDP, and population by region in the year 2000. Created with openoffice.org Calc. Data obtained from the UNU-WIDER report on worldwide distribution of household wealth: Press release. The World Distribution of Household Wealth. December 5, 2006. By James B. Davies, Susanna Sandstrom, Anthony Shorrocks, and Edward N. Wolff. Tables to the 2006 report in Excel (including Gini coefficients for 229 countries). UNU-WIDER.

The distribution of wealth is a comparison of the wealth of various members or groups in a society. It shows one aspect of economic inequality or economic heterogeneity.

The distribution of wealth differs from the income distribution in that it looks at the economic distribution of ownership of the assets in a society, rather than the current income of members of that society. According to the International Association for Research in Income and Wealth, "the world distribution of wealth is much more unequal than that of income."

For rankings regarding wealth, see list of countries by wealth equality or list of countries by wealth per adult.

Definition of wealth

Wealth of an individual is defined as net worth, expressed as: wealth = assets − liabilities

A broader definition of wealth, which is rarely used in the measurement of wealth inequality, also includes human capital. For example, the United Nations definition of inclusive wealth is a monetary measure which includes the sum of natural, human and physical assets.

The relation between wealth, income, and expenses is: change of wealth = saving = income − consumption (expenses). If an individual has a large income but also large expenses, the net effect of that income on her or his wealth could be small or even negative.

Conceptual framework

There are many ways in which the distribution of wealth can be analyzed. One common-used example is to compare the amount of the wealth of individual at say 99 percentile relative to the wealth of the median (or 50th) percentile. This is P99/P50 is one of the potential Kuznets ratios which is the inverted U shape that indicates the relationship between the inequality and the income per capita. Another common measure is the ratio of total amount of wealth in the hand of top say 1% of the wealth distribution over the total wealth in the economy. In many societies, the richest ten percent control more than half of the total wealth.

The Pareto Distribution has often been used to mathematically quantify the distribution of wealth at the right tail (the wealth of the very rich); stating that the upper 20% owns 80%, the upper 4% owns 64%, the upper 0.8% owns 51.2%, etc. In fact, the tail of wealth distributions, similar to that of income distribution, behaves like a Pareto distribution but with a thicker tail.

Wealth over people (WOP) curves are a visually compelling way to show the distribution of wealth in a nation. WOP curves are modified distribution of wealth curves. The vertical and horizontal scales each show percentages from zero to one hundred. We imagine all the households in a nation being sorted from richest to poorest. They are then shrunk down and lined up (richest at the left) along the horizontal scale. For any particular household, its point on the curve represents how their wealth compares (as a proportion) to the average wealth of the richest percentile. For any nation, the average wealth of the richest 1/100 of households is the topmost point on the curve (people, 1%; wealth, 100%) or (p=1, w=100) or (1, 100). In the real world two points on the WOP curve are always known before any statistics are gathered. These are the topmost point (1, 100) by definition, and the rightmost point (poorest people, lowest wealth) or (p=100, w=0) or (100, 0). This unfortunate rightmost point is given because there are always at least one percent of households (incarcerated, long term illness, etc.) with no wealth at all. Given that the topmost and rightmost points are fixed ... our interest lies in the form of the WOP curve between them. There are two extreme possible forms of the curve. The first is the "perfect communist" WOP. It is a straight line from the leftmost (maximum wealth) point horizontally across the people scale to p=99. Then it drops vertically to wealth = 0 at (p=100, w=0).

The other extreme is the "perfect tyranny" form. It starts on the left at the Tyrant's maximum wealth of 100%. It then immediately drops to zero at p=2, and continues at zero horizontally across the rest of the people. That is, the tyrant and his friends (the top percentile) own all the nation's wealth. All other citizens are serfs or slaves. An obvious intermediate form is a straight line connecting the left/top point to the right/bottom point. In such a "Diagonal" society a household in the richest percentile would have just twice the wealth of a family in the median (50th) percentile. Such a society is compelling to many (especially the poor). In fact it is a comparison to a diagonal society that is the basis for the Gini values used as a measure of the disequity in a particular economy. These Gini values (40.8 in 2007) show the United States to be the third most dis-equitable economy of all the developed nations (behind Denmark and Switzerland).

More sophisticated models have also been proposed.

Theoretical approaches

To model aspects of the distribution and holdings of wealth, there have been many different types of theories used. Before the 1960s, the data regarding this was collected mostly from wealth tax and estate tax records, with further proof gathered from small unrepresentative examinations and a variety of other sources. The results from these sources tended to show that the distribution of wealth was very unequal, and that material inheritance had a big role in the matter of wealth differences and in the transmission of the status of wealth from generation to generation. There was also reason to believe that the inequality in wealth was shrinking over time, and also the distribution's shape demonstrated particular statistical regularities that could not have been caused by coincidence. Thus, early theoretical work on the distribution of wealth wanted to explain the statistical regularities, and also comprehend the relationship of basic forces which could be an explanation for the concentration of wealth to be high and the trend of declining over time.

More lately, the research about wealth distribution has moved away from the worry with overall distributional characteristics, and in its place focuses more on the grounds of individual differences in the holdings of wealth. This change was caused partly because the importance of saving for retirement increased, and it is reflected in the vital role now assigned to the model of lifecycle savings developed by Modigliani and Brumberg (1954), and Ando and Modigliani (1963). Another important progress has been the increase in availability and finesse in sets of micro-data, which offer not just estimations of individuals' asset holdings and savings but also a variety of other household and personal characteristics that can assist in explain the differences in wealth.

Wealth inequality

A homeless individual sleeping on the street, next to a limousine

Wealth inequality refers to uneven distribution of wealth among individuals and entities. Although most research depends on written sources, archaeologists and anthropologists often view large houses as occupied by wealthy households. The distribution of contemporaneous house sizes in a society (perhaps analyzed using the Gini coefficient) then can regarded as a measure of wealth inequality. This approach has been used at least since 2014 and has shown, for example, that ancient wealth disparities in Eurasia were greater than those in North America and in Mesoamerica following the earliest Neolithic period.

Global inequality statistics

Share of wealth globally by year, as seen by Oxfam, based on the net worth

A study by the World Institute for Development Economics Research at United Nations University reports that the richest 1% of adults alone owned 40% of global assets in the year 2000, and that the richest 10% of adults accounted for 85% of the world total. The bottom half of the world adult population owned 1% of global wealth. A 2006 study found that the richest 2% own more than half of global household assets. The Pareto distribution gives 52.8% owned by the upper 1%.

According to the OECD in 2012 the top 0.6% of world population (consisting of adults with more than US$1 million in assets) or the 42 million richest people in the world held 39.3% of world wealth. The next 4.4% (311 million people) held 32.3% of world wealth. The bottom 95% held 28.4% of world wealth. The large gaps of the report get by the Gini index to 0.893, and are larger than gaps in global income inequality, measured in 2009 at 0.38. For example, in 2012 the bottom 60% of the world population held the same wealth in 2012 as the people on Forbes' Richest list consisting of 1,226 richest billionaires of the world.

A 2021 Oxfam report found that collectively, the 10 richest men in the world owned more than the combined wealth of the bottom 3.1 billion people, almost half of the entire world population. Their combined wealth doubled during the pandemic.

‘Global wealth Report 2021’, published by Credit Suisse, shows a substantial worldwide increase in wealth inequality during 2020. According to Credit Suisse, wealth distribution pyramid in 2020 shows that the richest group of adult population (1.1%) owns 45.8% of the total wealth. When compared to the 2013 wealth distribution pyramid, an overall increase of 4.8% can be seen. The bottom half of the world’s total adult population, the bottom quartile in the pyramid, owns only 1.3% of the total wealth. Again, when compared to the 2013 wealth distribution pyramid, a decrease of 1.7% can be observed. In conclusion, this comparison shows a substantial worldwide increase in wealth inequality over these years.

One of the main explanations for the ongoing increase of wealth inequality are the repercussions of the COVID-19 pandemic. Credit Suisse claims that the economic impact of the pandemic on employment and incomes in 2020 are likely to have a negative effect for the lowest groups of wealth holders, forcing them to spend more from their savings or incur higher debt. On the other hand, top wealth groups appeared to be relatively unaffected in this negative way. Moreover, they seemed to benefit from the impact of lower interest rates on share and house prices.

According to the ‘Global Wealth Report 2021’ published by Credit Suisse, there are 56 million millionaires in the world in 2020, increasing by 5.2 million from a year earlier. The biggest number of dollar millionaires is reported in the USA, with 22 million millionaires (approximately 39% of the world total). This is far ahead of China, holding second place, with 9.4% of all global millionaires. The third place is currently being held by Japan, with 6.6% of all global millionaires.

Real estate

While sizeable numbers of households own no land, few have no income. For example, the top 10% of land owners (all corporations) in Baltimore, Maryland own 58% of the taxable land value. The bottom 10% of those who own any land own less than 1% of the total land value. This form of analysis as well as Gini coefficient analysis has been used to support land value taxation.

Wealth distribution pyramid

Pyramid of global wealth distribution in 2013

In 2013, Credit Suisse prepared a wealth pyramid infographic (shown right). Personal assets were calculated in net worth, meaning wealth would be negated by having any mortgages. It has a large base of low wealth holders, alongside upper tiers occupied by progressively fewer people. In 2013 Credit-suisse estimate that 3.2 billion individuals – more than two thirds of adults in the world – have wealth below US$10,000. A further one billion (adult population) fall within the 10,000 – US$100,000 range. While the average wealth holding is modest in the base and middle segments of the pyramid, their total wealth amounts to US$40 trillion, underlining the potential for novel consumer products and innovative financial services targeted at this often neglected segment.

The pyramid shows that:

• half of the world's net wealth belongs to the top 1%,
• top 10% of adults hold 85%, while the bottom 90% hold the remaining 15% of the world's total wealth,
• top 30% of adults hold 97% of the total wealth.

Wealth distribution pyramid in 2020

In 2020, Credit Suisse created an updated wealth pyramid infographic. The infographic was constructed similarly to the pyramid in 2013, thus personal assets were calculated in net worth. In 2020, Credit Suisse estimated that approximately 2.88 billion people (55% of adult population) have wealth below US$10,000. Further, 1.7 billion individuals (38.2% of adult population) have wealth within the range of 10,000 – US$100,000. To continue, 583 million people have wealth within the range of 100,000 – US$1,000,000 and approximately 56 million people (1.1% of adult population) have wealth over US$1,000,000.

Comparison of 2013 and 2020 pyramids

Vast differences between 2013 and 2020 infographic can be observed. For the first time, more than 1% of all global adults have wealth over US$1,000,000. Credit Suisse explains in the ‘Global Wealth Report 2021’, that this increase reflects the economic disruption caused by the pandemic and disconnect between the improvement in the financial and real assets of households. However, the biggest difference can be seen in the 10,000 – US$100,000 segment. Since 2013, there had been an increase of almost 10% of total adult population. According to Credit Suisse, the number of adults in this segment tripled since 2000. Credit Suisse explains this fact by stating that this increase was a result of growing prosperity of emerging economies, especially China, and the expansion of the middle class in the developing world. The upper-middle segment, with wealth in a range of 100,000 – US$1,000,000 has increased by 3.4%. Credit Suisse in the report states that the middle class in developed countries typically belong to this group.

Wealth outlook for 2020-2025

According to the ‘Global wealth Report 2021’, published by Credit Suisse, global wealth is projected to rise by 39% over the next five years reaching USD 583 trillion by 2025. Wealth per adult is also projected to increase by 31% and so is the number of global millionaires. The wealth pyramid, an infographic used to determine wealth distribution, will also change. The bottom segment covering adults with a net worth below USD 10,000 will likely decrease by approximately 108 million over the next five years. The lower-middle segment of the pyramid containing adults with a net worth in the range of USD 10,000 and USD 100,000 is projected to rise by 237 million adults. Most of these new members are most likely to be from lower-income countries. The upper-middle segment, consisting of adults with wealth between USD 100,000 and USD 1 million is projected to rise by 178 million adults. Most of these new members (approximately 114 million) are likely to come from upper-middle-income countries. Number of global millionaires is also projected to increase. According to the estimates made by Credit Suisse, the number of global millionaires could exceed 84 million by 2025, a rise of almost 28 million from 2020. The increase of millionaires will not only occur in developed countries such as the USA or other developed countries in Europe, but it is also expected to rapidly increase in lower-income countries. The biggest increase is expected in China, with a change of 92.7%, which is about 4.8 million new dollar millionaires. As a consequence, the number of Ultra High Net Worth Individuals (UHNWI) with net worth exceeding USD 50 million, will also increase.

Gini Coefficient

Gini coefficient (or Gini index) is an indicator that is often used to determine wealth inequality. A Gini coefficient of 0 reflects perfect equality, where all income or wealth values are the same, while a Gini coefficient of 1 (or 100%) reflects maximal inequality among values, a situation where a single individual has all the income while all others have none.According to the Credit Suisse ‘Global wealth Report 2021’, Brunei had the highest Gini coefficient in 2021 (91.6%), therefore the wealth distribution in Brunei is vastly unequal. Slovakia had the lowest Gini coefficient in 2021 (50.3%) out of all countries, which makes Slovakia the most equal country in terms of wealth distribution. When compared to the report made by Credit Suisse in 2019, an increasing trend of wealth inequality can be observed. This may be the result of repercussions of the Covid-19 pandemic. The biggest increase was recorded in Brazil. The Gini coefficient in 2019 was 88.2% and 89% in 2021, with an increase of 0.8% over this period.

The following table was created from information provided by the Credit Suisse Research Institute's "Global Wealth Databook", Table 3-1, published 2021.

Click at right to show/hide table with detailed statistics

Country Adults (In 1,000) Wealth per adult (USD) Distribution of adults (%) by wealth range (USD) Gini (%) Mean Median Under 10k 10k – 100k 100k – 1M Over 1M Afghanistan 18,356 1,744 734 97.6 2.4 0.1 0.0 72.8 Albania 2,187 30,524 15,363 41.0 54.2 4.7 0.1 68.2 Algeria 27,620 8,871 2,302 87.0 11.7 1.2 0.1 84.8 Angola 14,339 3,529 1,131 93.5 6.2 0.2 0.0 80.6 Argentina 30,799 7,224 2,157 88.2 11.2 0.6 0.0 81.2 Armenia 2,176 22,573 9,411 52.3 44.0 3.5 0.1 73.0 Australia 19,159 483,755 238,072 9.8 20.7 60.0 9.4 65.6 Austria 7,271 290,348 91,833 14.2 36.9 44.1 4.8 73.5 Azerbaijan 7,155 11,926 5,022 73.5 25.2 1.3 0.0 72.7 Bahamas 278 56,737 7,507 54.0 39.7 5.7 0.6 91.4 Bahrain 1,318 87,559 14,520 45.0 48.0 6.1 0.9 88.9 Bangladesh 106,060 7,837 3,062 84.6 14.6 0.7 0.0 75.2 Barbados 221 63,261 21,071 41.0 46.0 12.4 0.6 80.4 Belarus 7,367 23,278 12,168 45.9 51.3 2.8 0.1 66.7 Belgium 8,993 351,327 230,548 11.9 20.1 62.3 5.7 60.3 Belize 245 10,364 3,015 82.0 16.6 1.4 0.0 83.4 Benin 5,839 2,558 890 95.6 4.3 0.1 0.0 78.2 Bolivia 7,088 12,286 3,804 78.1 20.5 1.3 0.1 81.0 Bosnia and Herzegovina 2,637 30,597 15,283 41.0 54.1 4.8 0.1 68.6 Botswana 1,358 15,598 3,680 80.0 16.8 3.1 0.1 87.3 Brazil 153,307 18,272 3,469 79.5 17.5 2.8 0.1 89.0 British Caribbean 567 45,109 14,684 44.0 47.7 7.9 0.4 80.8 Brunei 309 39,098 5,122 64.0 32.1 3.5 0.4 91.6 Bulgaria 5,586 36,443 17,403 38.7 54.9 6.2 0.2 70.1 Burkina Faso 9,480 1,681 622 98.0 1.9 0.1 0.0 76.8 Burundi 5,381 728 281 99.5 0.5 0.0 0.0 75.1 Cambodia 10,180 5,895 2,031 90.7 8.7 0.6 0.0 78.7 Cameroon 12,716 3,042 941 94.3 5.5 0.2 0.0 81.6 Canada 29,934 332,323 125,688 20.7 25.1 48.6 5.6 71.9 Central African Republic 2,161 840 212 98.8 1.2 0.0 0.0 85.9 Chad 7,059 1,117 355 98.7 1.3 0.1 0.0 80.6 Chile 14,259 53,591 17,747 39.1 51.6 8.8 0.5 79.7 China 1,104,956 67,771 24,067 20.9 66.1 12.5 0.5 70.4 Colombia 35,612 16,928 4,854 72.0 25.4 2.5 0.1 82.7 Comoros 447 5,397 1,466 91.5 7.9 0.6 0.0 84.8 Congo, Dem. Rep. 39,740 1,240 356 98.3 1.6 0.1 0.0 83.2 Congo, Rep. 2,707 2,180 582 95.6 4.2 0.1 0.0 84.7 Costa Rica 3,696 44,337 14,662 44.0 47.4 8.4 0.3 79.9 Croatia 3,303 69,140 34,945 27.0 57.0 15.5 0.5 68.5 Cyprus 679 142,304 35,300 23.0 57.0 18.3 1.7 80.7 Czechia 8,528 78,103 23,794 29.6 55.7 14.0 0.7 77.7 Denmark 4,557 376,069 165,622 15.4 25.4 52.5 6.7 73.6 Djibouti 618 3,112 1,077 94.0 6.0 0.0 0.0 78.8 Dutch Caribbean 258 40,909 16,810 40.0 52.7 7.1 0.2 69.1 Ecuador 11,361 17,151 5,444 69.9 27.9 2.1 0.1 80.8 Egypt 59,547 19,468 6,329 66.5 30.7 2.6 0.1 79.2 El Salvador 4,201 34,003 11,372 47.6 46.0 6.2 0.2 79.1 Equatorial Guinea 776 18,246 4,561 77.0 18.8 4.1 0.1 86.3 Eritrea 1,728 2,846 1,086 95.2 4.7 0.1 0.0 75.7 Estonia 1,044 77,817 38,901 30.5 53.5 15.3 0.7 73.8 Ethiopia 57,104 3,540 1,527 94.4 5.4 0.2 0.0 71.1 Fiji 564 15,708 5,764 69.0 28.3 2.6 0.1 77.4 Finland 4,373 167,711 73,775 27.8 35.2 35.1 1.9 74.0 France 49,967 299,355 133,559 14.8 27.0 53.3 4.9 70.0 French Caribbean 631 68,443 23,740 36.0 44.0 19.5 0.5 73.8 Gabon 1,216 13,696 4,685 74.0 24.5 1.4 0.1 79.3 Gambia 1,115 2,500 658 94.9 4.9 0.2 0.0 84.9 Georgia 2,959 14,162 4,223 77.7 20.7 1.5 0.1 81.3 Germany 68,015 268,681 65,374 10.6 45.2 39.8 4.3 77.9 Ghana 16,617 6,132 2,198 88.5 11.1 0.4 0.0 77.5 Greece 8,462 104,603 57,595 22.1 49.3 27.7 0.9 65.7 Guinea 6,078 2,942 938 94.5 5.4 0.2 0.0 80.8 Guinea-Bissau 949 1,828 670 97.0 3.0 0.0 0.0 77.6 Guyana 497 12,280 4,637 74.0 24.6 1.4 0.0 76.5 Haiti 6,621 767 193 99.2 0.7 0.0 0.0 85.2 Hong Kong 6,292 503,335 173,768 13.7 23.7 54.3 8.3 74.6 Hungary 7,769 53,664 24,126 21.4 67.6 10.7 0.3 66.5 Iceland 255 337,787 231,462 6.0 18.0 70.7 5.3 50.9 India 900,443 14,252 3,194 77.2 21.1 1.7 0.1 82.3 Indonesia 180,782 17,693 4,693 67.2 30.8 1.9 0.1 77.7 Iran 57,987 22,249 7,621 59.1 37.1 3.7 0.1 78.6 Iraq 21,247 14,506 6,378 68.3 30.1 1.6 0.1 71.0 Ireland 3,619 266,153 99,028 30.8 19.7 44.5 5.0 80.0 Israel 5,626 228,268 80,315 15.8 41.2 40.1 2.9 73.4 Italy 49,746 239,244 118,885 15.5 30.1 51.4 3.0 66.5 Jamaica 2,041 19,893 5,976 66.7 30.3 2.9 0.1 82.0 Japan 104,953 256,596 122,980 11.0 32.6 52.9 3.5 64.4 Jordan 5,866 28,316 10,842 48.3 47.1 4.5 0.2 75.9 Kazakhstan 12,226 33,463 12,029 46.3 49.3 4.2 0.2 76.4 Kenya 27,473 12,313 3,683 79.6 18.8 1.5 0.1 82.2 Korea, South 42,490 211,369 89,671 14.8 38.3 44.4 2.5 67.6 Kuwait 3,146 129,890 28,698 42.8 44.0 10.7 2.5 86.5 Kyrgyzstan 3,927 5,816 2,238 89.7 9.8 0.5 0.0 75.7 Laos 4,288 7,379 1,610 91.6 7.0 1.3 0.0 87.9 Latvia 1,477 70,454 33,884 36.0 50.5 12.7 0.8 80.9 Lebanon 4,548 55,007 18,159 40.6 50.5 8.4 0.5 79.7 Lesotho 1,243 1,226 264 97.8 2.2 0.1 0.0 88.6 Liberia 2,502 4,453 1,464 91.9 7.8 0.3 0.0 80.1 Libya 4,440 17,198 6,512 67.0 31.0 1.9 0.1 76.0 Lithuania 2,166 63,500 29,679 29.3 58.0 12.2 0.5 71.0 Luxembourg 498 477,306 259,899 13.0 19.0 59.2 8.8 67.0 Madagascar 13,812 1,962 666 96.9 3.0 0.1 0.0 79.3 Malawi 8,887 2,045 606 96.2 3.7 0.1 0.0 82.4 Malaysia 22,315 29,287 8,583 55.0 41.1 3.7 0.2 82.9 Maldives 409 25,511 8,519 56.0 39.3 4.5 0.2 79.8 Mali 8,625 2,424 869 96.0 3.9 0.1 0.0 77.6 Malta 358 148,934 84,390 13.0 45.0 40.6 1.4 61.7 Mauritania 2,370 2,788 1,037 95.2 4.7 0.1 0.0 76.3 Mauritius 968 63,372 27,456 31.0 56.0 12.5 0.5 72.1 Melanesia 711 31,106 12,183 46.0 48.6 5.2 0.2 75.8 Mexico 85,136 42,689 13,752 44.7 46.9 8.1 0.3 80.5 Micronesia 341 13,193 4,876 74.0 23.9 2.1 0.0 77.9 Moldova 3,188 15,491 7,577 61.8 36.5 1.7 0.0 69.4 Mongolia 2,053 6,324 2,546 88.0 11.5 0.5 0.0 74.4 Montenegro 476 60,310 30,739 29.0 57.0 13.6 0.4 68.4 Morocco 24,654 13,459 3,874 78.4 19.7 1.9 0.1 81.9 Mozambique 14,186 1,003 345 98.9 1.0 0.1 0.0 79.1 Myanmar 35,734 5,025 2,458 91.7 8.0 0.3 0.0 67.0 Namibia 1,375 15,294 3,677 80.5 16.4 3.0 0.1 86.6 Nepal 17,887 4,056 1,437 93.3 6.3 0.3 0.0 78.1 Netherlands 13,462 377,092 136,105 13.6 29.4 49.3 7.7 75.3 New Zealand 3,600 348,198 171,624 21.2 20.0 52.5 6.3 69.9 Nicaragua 4,107 12,239 3,694 78.2 20.5 1.3 0.1 81.0 Niger 9,739 1,287 492 98.7 1.3 0.1 0.0 75.6 Nigeria 95,931 6,451 1,474 91.7 7.6 0.7 0.0 85.8 Norway 4,184 275,880 117,798 28.0 19.0 48.8 4.2 78.5 Oman 3,765 39,434 9,886 50.5 43.1 6.0 0.4 86.7 Pakistan 123,522 5,258 2,187 90.5 9.2 0.4 0.0 73.2 Panama 2,843 43,979 13,147 45.3 46.6 7.8 0.3 82.5 Papua New Guinea 4,941 6,710 1,790 91.3 7.7 1.0 0.0 84.3 Paraguay 4,454 11,962 3,644 78.8 19.9 1.2 0.1 81.6 Peru 22,530 17,017 5,445 70.4 27.4 2.1 0.1 80.1 Philippines 66,960 15,290 3,155 83.1 14.8 2.0 0.1 86.9 Poland 30,315 67,477 23,550 19.8 64.8 14.9 0.5 70.7 Polynesia 423 37,998 14,076 44.0 49.3 6.4 0.3 77.9 Portugal 8,339 142,537 61,306 23.2 45.1 30.0 1.6 70.5 Qatar 2,396 146,730 83,680 12.0 45.3 41.7 1.0 58.1 Romania 15,208 50,009 23,675 32.1 58.5 9.1 0.3 70.1 Russia 111,845 27,162 5,431 72.8 23.8 3.1 0.2 87.8 Rwanda 6,581 4,188 1,266 92.8 6.9 0.3 0.0 81.9 Sao Tome and Principe 104 4,029 1,702 92.4 7.3 0.2 0.0 73.1 Saudi Arabia 24,186 68,697 15,495 46.4 44.4 8.2 1.0 86.7 Senegal 7,975 4,702 1,570 91.4 8.3 0.3 0.0 79.7 Serbia 5,480 31,705 14,954 41.7 52.9 5.3 0.1 70.6 Seychelles 69 63,427 24,651 36.0 51.0 12.5 0.5 75.9 Sierra Leone 3,937 995 370 99.0 0.9 0.0 0.0 76.7 Singapore 4,887 332,995 86,717 16.2 38.6 39.7 5.5 78.3 Slovakia 4,346 68,059 45,853 11.6 69.8 18.4 0.2 50.3 Slovenia 1,672 120,173 67,961 18.0 53.0 28.2 0.8 67.1 South Africa 37,590 20,308 4,523 75.8 20.2 3.9 0.2 88.0 Spain 37,798 227,122 105,831 16.7 31.6 48.6 3.0 69.2 Sri Lanka 14,732 23,832 8,802 54.3 42.0 3.7 0.1 76.8 Sudan 21,941 1,014 383 99.0 0.9 0.1 0.0 75.9 Suriname 382 5,644 1,349 91.2 8.1 0.7 0.0 87.1 Sweden 7,794 336,166 89,846 34.0 18.4 40.3 7.3 87.2 Switzerland 6,958 673,962 146,733 11.9 33.7 43.2 11.2 78.1 Syria 10,811 2,197 807 96.3 3.6 0.1 0.0 77.2 Taiwan 19,633 238,862 93,044 13.9 38.6 44.4 3.1 70.8 Tajikistan 5,227 4,390 1,844 92.4 7.3 0.3 0.0 73.1 Tanzania 27,744 3,647 1,433 93.7 6.1 0.2 0.0 74.5 Thailand 54,054 25,292 8,036 55.5 41.9 2.5 0.2 77.1 Timor-Leste 689 5,185 2,838 91.4 8.3 0.3 0.0 62.6 Togo 4,084 1,484 468 98.0 2.0 0.1 0.0 81.2 Trinidad and Tobago 1,032 44,182 15,649 42.5 49.0 8.2 0.3 78.0 Tunisia 8,207 17,550 6,177 67.4 30.2 2.3 0.1 77.8 Turkey 57,768 27,466 8,001 57.6 38.8 3.4 0.2 81.8 Turkmenistan 3,722 20,328 9,030 54.0 43.2 2.7 0.1 70.6 Uganda 19,830 1,994 646 96.6 3.3 0.1 0.0 80.4 Ukraine 34,639 13,104 2,529 79.1 19.5 1.3 0.1 84.4 United Arab Emirates 8,053 115,476 21,613 45.1 46.0 6.8 2.1 88.8 United Kingdom 52,568 290,754 131,522 18.0 27.8 49.5 4.7 71.7 United States 249,969 505,421 79,274 26.3 28.5 36.4 8.8 85.0 Uruguay 2,530 60,914 22,088 37.0 51.3 11.2 0.4 77.2 Venezuela 18,359 21,040 7,341 60.5 36.8 2.5 0.1 78.1 Vietnam 68,565 14,075 4,559 76.3 21.9 1.8 0.1 80.2 Yemen 15,281 5,581 1,223 93.0 6.2 0.8 0.0 88.0 Zambia 8,331 3,068 692 94.3 5.5 0.2 0.0 87.7 Zimbabwe 7,086 7,131 2,356 86.9 12.5 0.6 0.0 79.8

Geographical distribution

Wealth is unevenly distributed across different world regions. At the end of the 20th century, wealth was concentrated among the G8 and Western industrialized nations, along with several Asian and OPEC nations. In the 21st century, wealth is still concentrated among the G8 with United States of America leading with 30.2%, along with other developed countries, several Asia-pacific countries and OPEC countries.

Countries by total wealth (trillions USD), Credit Suisse

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  World distribution of wealth by country (PPP)

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  World distribution of wealth by region (PPP)

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  World distribution of wealth by country (exchange rates)

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  World distribution of wealth by region (exchange rates)

Worlds regions by total wealth (in trillions USD), 2018

By region

World distribution of financial wealth. In 2007, 147 companies controlled nearly 40 percent of the monetary value of all transnational corporations.

In the United States

Net personal wealth in the U.S. since 1962, and across age groups

The average personal wealth of people in the top 1% is more than a thousand times that of people in bottom 50%.

The logarithmic scale shows how wealth has increased for all percentile groups, though moreso for wealthier people.

Average net worth—which heavily weights extremely high-wealth families—substantially exceeds median net worth (families in the fiftieth percentile). Further, average net worth outgrew median net worth from 2019 through 2022.

Though the 10th percentile of American households have zero net worth, the 90th percentile has $1.6 million of household wealth.

Higher educational attainment in the US corresponds with higher household wealth.

Median wealth of married couples is almost three times that of single individuals, regardless of gender and across all age categories.

According to PolitiFact, in 2011 the 400 wealthiest Americans "have more wealth than half of all Americans combined." Inherited wealth may help explain why many Americans who have become rich may have had a "substantial head start". In September 2012, according to the Institute for Policy Studies, "over 60 percent" of the Forbes richest 400 Americans "grew up in substantial privilege".

In 2007, the richest 1% of the American population owned 34.6% of the country's total wealth (excluding human capital), and the next 19% owned 50.5%. The top 20% of Americans owned 85% of the country's wealth and the bottom 80% of the population owned 15%. From 1922 to 2010, the share of the top 1% varied from 19.7% to 44.2%, the big drop being associated with the drop in the stock market in the late 1970s. Ignoring the period where the stock market was depressed (1976–1980) and the period when the stock market was overvalued (1929), the share of wealth of the richest 1% remained extremely stable, at about a third of the total wealth. Financial inequality was greater than inequality in total wealth, with the top 1% of the population owning 42.7%, the next 19% of Americans owning 50.3%, and the bottom 80% owning 7%. However, after the Great Recession which started in 2007, the share of total wealth owned by the top 1% of the population grew from 34.6% to 37.1%, and that owned by the top 20% of Americans grew from 85% to 87.7%. The Great Recession also caused a drop of 36.1% in median household wealth but a drop of only 11.1% for the top 1%, further widening the gap between the 1% and the 99%.

Dan Ariely and Michael Norton show in a study (2011) that US citizens across the political spectrum significantly underestimate the current US wealth inequality and would prefer a more egalitarian distribution of wealth, raising questions about ideological disputes over issues like taxation and welfare.

Wealth inequality in the United States increased from 1989 to 2013.

Wealth concentration

Wealth concentration is a process by which created wealth, under some conditions, can become concentrated by individuals or entities. Those who hold wealth have the means to invest in newly created sources and structures of wealth, or to otherwise leverage the accumulation of wealth, and are thus the beneficiaries of even greater wealth.

Economic conditions

Global share of wealth by wealth group

The first necessary condition for the phenomenon of wealth concentration to occur is an unequal initial distribution of wealth. The distribution of wealth throughout the population is often closely approximated by a Pareto distribution, with tails which decay as a power-law in wealth. (See also: Distribution of wealth and Economic inequality). According to PolitiFact and others, the 400 wealthiest Americans had "more wealth than half of all Americans combined." Inherited wealth may help explain why many Americans who have become rich may have had a "substantial head start". In September 2012, according to the Institute for Policy Studies, "over 60 percent" of the Forbes 400 Richest Americans "grew up in substantial privilege".

The second condition is that a small initial inequality must, over time, widen into a larger inequality. This is an example of positive feedback in an economic system. A team from Jagiellonian University produced statistical model economies showing that wealth condensation can occur whether or not total wealth is growing (if it is not, this implies that the poor could become poorer).

Joseph E. Fargione, Clarence Lehman and Stephen Polasky demonstrated in 2011 that chance alone, combined with the deterministic effects of compounding returns, can lead to unlimited concentration of wealth, such that the percentage of all wealth owned by a few entrepreneurs eventually approaches 100%.

Correlation between being rich and earning more

Given an initial condition in which wealth is unevenly distributed (i.e., a "wealth gap"), several non-exclusive economic mechanisms for wealth condensation have been proposed:

• A correlation between being rich and being given high-paid employment (oligarchy).
• A marginal propensity to consume low enough that high incomes are correlated with people who have already made themselves rich (meritocracy).
• The ability of the rich to influence government disproportionately to their favor thereby increasing their wealth (plutocracy).

In the first case, being wealthy gives one the opportunity to earn more through high paid employment (e.g., by going to elite schools). In the second case, having high paid employment gives one the opportunity to become rich (by saving your money). In the case of plutocracy, the wealthy exert power over the legislative process, which enables them to increase the wealth disparity. An example of this is the high cost of political campaigning in some countries, in particular in the US (more generally, see also plutocratic finance).

Because these mechanisms are non-exclusive, it is possible for all three explanations to work together for a compounding effect, increasing wealth concentration even further. Obstacles to restoring wage growth might have more to do with the broader dysfunction of a dollar dominated political system particular to the US than with the role of the extremely wealthy.

Counterbalances to wealth concentration include certain forms of taxation, in particular wealth tax, inheritance tax and progressive taxation of income. However, concentrated wealth does not necessarily inhibit wage growth for ordinary workers with low wages.

The investor, billionaire, and philanthropist Warren Buffett, one of the wealthiest people in the world, voiced in 2005 and once more in 2006 his view that his class, the "rich class", is waging class warfare on the rest of society. In 2005 Buffet said to CNN: "It's class warfare, my class is winning, but they shouldn't be." In a November 2006 interview in The New York Times, Buffett stated that "[t]here’s class warfare all right, but it’s my class, the rich class, that’s making war, and we’re winning."

Redistribution of wealth and public policy

In many societies, attempts have been made, through property redistribution, taxation, or regulation, to redistribute wealth, sometimes in support of the upper class, and sometimes to diminish economic inequality.

Examples of this practice go back at least to the Roman republic in the third century B.C., when laws were passed limiting the amount of wealth or land that could be owned by any one family. Motivations for such limitations on wealth include the desire for equality of opportunity, a fear that great wealth leads to political corruption, to the belief that limiting wealth will gain the political favor of a voting bloc, or fear that extreme concentration of wealth results in rebellion. Various forms of socialism attempt to diminish the unequal distribution of wealth and thus the conflicts and social problems arising from it.

During the Age of Reason, Francis Bacon wrote "Above all things good policy is to be used so that the treasures and monies in a state be not gathered into a few hands… Money is like fertilizer, not good except it be spread."

The rise of Communism as a political movement has partially been attributed to the distribution of wealth under capitalism in which a few lived in luxury while the masses lived in extreme poverty or deprivation. However, in the Critique of the Gotha Program, Marx and Engels criticized German Social Democrats for placing emphasis on issues of distribution instead of on production and ownership of productive property. While the ideas of Marx have nominally influenced various states in the 20th century, the Marxist notions of socialism and communism remains elusive.

On the other hand, the combination of labor movements, technology, and social liberalism has diminished extreme poverty in the developed world today, though extremes of wealth and poverty continue in the Third World.

In the Outlook on the Global Agenda 2014 from the World Economic Forum the widening income disparities come second as a worldwide risk. According to a 2009 meta-analysis by Paul and Moser, countries with high income inequality and poor unemployment protections experience worse mental health outcomes among the unemployed.

See also

• Wealth inequality in the United States
• Wealth distribution in Europe
• Distributive justice
• Desert (philosophy)
• Generational accounting
• Cycle of poverty
• Gini coefficient
• Social mobility
• Social inequality
• Economic mobility
• Economic inequality
• Kinetic exchange models of markets
• List of countries by financial assets per capita
• List of countries by total wealth
• List of countries by wealth per adult
• List of countries by wealth inequality