Global Wealth Databook 2023 - UBS

[Pages:158]Global Wealth Databook 2023

Leading perspectives to navigate the future

Preface

The 2023 edition of the Global Wealth Report reflects on a year that has delivered a significant setback in what had been a consistent uptrend in the accumulation of wealth in the household sector. 2022 recorded the first fall in net global household wealth since the global financial crisis of 2008.

Measured in current nominal USD, total net private wealth fell by USD 11.3 trillion (?2.4%) to USD 454.4 trillion at the end of the year. Wealth per adult also declined by USD 3,198 (?3.6%) to reach USD 84,718 per adult at end-2022. Much of this decline comes from the appreciation of the US dollar against many other currencies. If exchange rates are held constant at 2021 rates, then total wealth increased by 3.4% and wealth per adult by 2.2% during 2022. This is still the slowest increase of wealth at constant exchange rates since 2008. Keeping exchange rates constant but counting the effects of inflation results in a real wealth loss of ?2.6% in 2022.

A more detailed examination shows that financial assets contributed most to wealth declines in 2022 while nonfinancial assets (mostly real estate) stayed resilient, despite rapidly rising interest rates. But the relative contributions of financial and non-financial assets may reverse in 2023 if house prices decline in response to higher interest rates.

Regionally, the loss of global wealth was heavily concentrated in wealthier regions such as North America and Europe, which together shed USD 10.9 trillion. Asia Pacific recorded losses of USD 2.1 trillion, while Latin America is the outlier with a total wealth increase of USD 2.4 trillion, helped by an average 6% currency appreciation against the US dollar. Heading the list of losses in country terms in 2022 is the United States, followed by Japan, China, Canada and Australia. The largest wealth increases at the other end were recorded for Russia, Mexico, India and Brazil.

In terms of wealth per adult, Switzerland continues to top the list followed by the USA, Hong Kong SAR, Australia and Denmark despite sizeable reductions in mean wealth versus 2021. Ranking markets by median wealth results in a different list, with Belgium in the lead followed by Australia, Hong Kong SAR, New Zealand and Denmark.

When looked at in demographic terms, Generation X and Millennials continued to do relatively well in 2022 in the USA and Canada but were not immune to the overall wealth reduction. Broken down by race, non-Hispanic Caucasians in the USA saw their wealth decrease in 2022, while AfricanAmericans were left almost unscathed by the downturn. In contrast, Hispanics achieved 9.5% growth in 2022, owing to their greater holdings of housing assets compared to financial assets.

Along with the decline in aggregate wealth, overall wealth inequality also fell in 2022, with the wealth share of the global top 1% falling to 44.5%. The number of USD millionaires worldwide fell by 3.5 million during 2022 to 59.4 million people before taking into account 4.4 million "inflation millionaires" who would no longer qualify if the millionaire threshold were adjusted for inflation in 2022. Global median wealth, arguably a more meaningful indicator of how the typical person is faring, did in fact rise by 3% in 2022 in contrast to the 3.6% fall in wealth per adult. For the world as a whole, median wealth has increased five-fold this century at roughly double the pace of wealth per adult, largely due to the rapid wealth growth in China.

According to our projections, global wealth will rise by 38% over the next five years, reaching USD 629 trillion by 2027. Growth by middle-income markets will be the primary driver of global trends. We estimate wealth per adult to reach USD 110,270 in 2027 and the number of millionaires to reach 86 million while the number of ultra-high-net-worth individuals (UHNWIs) is likely to rise to 372,000 individuals.

Nannette Hechler-Fayd'herbe Chief Investment Officer for the EMEA region and Global Head of Economics and Research, Credit Suisse

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Global Wealth Databook 2023

Contents

4 Section 1

Estimating the pattern of global household wealth

8 Table 1-1

Coverage of wealth levels data

9 Table 1-2

Household balance sheet and financial balance sheet sources

11 Table 1-3

Survey sources

13 Table 1-4

Changes in asset prices and exchange rates 2022, selected markets

14 Table 1-5

Wealth shares for markets with wealth distribution data

18 Section 2

Household wealth levels, 2000?22

20 Table 2-1

Market details

25 Table 2-2 (by year) Wealth estimates by market 2000?22

117 Table 2-3

Components of wealth per adult in USD, by region and year

118 Table 2-4

Components of wealth as percentage of gross wealth, by region and year

119 Table 2-5

Changes in household wealth 2022, selected markets

120 Section 3

Estimating the distribution of global wealth

123 Table 3-1

Wealth pattern within markets, 2022

127 Table 3-2

Wealth pattern by region, 2022

128 Table 3-3

Membership of top wealth groups for selected markets, 2022

129 Table 3-4

Percentage membership of global wealth deciles and top percentiles by market of residence, 2022

133 Table 3-5

Main gains and losses in global wealth distribution, 2022

134 Table 3-6

High-net-worth individuals by market and region, 2022

136 Section 4

Region and market focus

138 Table 4-1 139 Table 4-2 141 Table 4-3

143 Table 4-4

Summary details for regions and selected markets, 2022

Wealth per adult (USD) at current and smoothed exchange rates, for regions and selected markets, 2000?22 Total wealth (USD bn) at current and smoothed exchange rates, for regions and selected markets, 2000?22 Composition of wealth per adult for regions and selected markets, 2022

144 Table 4-5

Wealth shares and minimum wealth of deciles and top percentiles for regions and selected markets, 2022

145 Table 4-6

Distribution of wealth for regions and selected markets, 2022

148 Bibliography and data references

151 About the authors

153 General disclaimer/important information

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1. Estimating the pattern of global household wealth

1.1 Introduction We provide estimates of the wealth holdings of households around the world for each year since 2000. More specifically, we are interested in the distribution within and across markets of individual net worth, defined as the marketable value of financial assets plus non-financial assets (principally housing and land) less debts. No market in the world has a single comprehensive source of information on personal wealth, and many low- and middleincome markets have little direct evidence of any kind. However, a growing number of markets ? including China and India, as well as many high-income markets ? have relevant data from a variety of different sources, which we are able to exploit in order to achieve our objective.

We focus on 217 markets or economically self-governing territories (such as Hong Kong SAR) for which population sizes are recorded by the United Nations and which also have GDP and exchange rate data. These "markets" are listed in Table 2-1 along with some summary details. Note that China and India are treated as separate regions due to the size of their populations.

The second step in our estimation procedure involves constructing the pattern of wealth holdings within markets. We use direct data on the distribution of wealth for 40 markets. Inspection of data for these markets suggests a relationship between wealth distribution and income distribution, within world regions, that can be exploited in order to provide an initial estimate of wealth distribution for another 140 markets, which have data on income distribution but not on wealth ownership. The remaining 37 markets are a subset of the 46 markets to which we assigned wealth levels above. We follow a similar procedure here and assign to each of these markets the average wealth pattern of its (United Markets) subregion.

It is well known that the traditional sources of wealth distribution data are unlikely to provide an accurate picture of wealth ownership in the top tail of the distribution for most markets. To overcome this deficiency, the third step makes use of the information in the Forbes world list of billionaires to adjust the wealth distribution pattern in the highest wealth ranges.

Our estimation procedure involves three main steps, the first two of which follow the structure set out in Davies et al. (2008, 2011). (See also Davies et al., 2017.) The first step establishes the average level of wealth for each market. The best source of data for this purpose is household balance sheet (HBS) data, which are now provided by 51 markets, although 24 of these markets cover only financial assets and debts. For an additional two markets, wealth levels can be calculated from household survey data. Together these markets cover 64% of the global population and 93% of total global wealth.

For markets without HBS or survey data we use standard econometric techniques to estimate net wealth levels and its components for a further 118 markets. This leaves 46 markets lacking sufficient suitable data for wealth estimation. Most of these are small island states in the Pacific or Caribbean. The remainder are either small (e.g., Andorra, Monaco) or semidetached from the global economy (e.g., Cuba, Somalia, North Korea). Together these markets account for only 4.9% of global adults and 0.9% of global wealth. But for completeness, we assign net wealth values to these markets based on the assumption that their wealth/GDP ratio is the same as the subregion to which they belong (which is generally a good rule of thumb). However, because these estimates are less reliable, we do not report the detailed results for these markets in the summary Table 2-2 or elsewhere.

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The following sections describe the estimation procedures in more detail. Two other general points should be mentioned at the outset. First, we use official exchange rates throughout to convert currencies to our standard measure of value, which is US dollars at the time in question (usually end-year). In international comparisons of consumption or income, it is common to convert currencies using purchasing power parity (PPP) exchange rates, which take account of local prices, especially for non-traded services. However, in all markets, a large share of personal wealth is owned by households in the top few percentiles of the distribution who tend to be internationally mobile and to move their assets across borders with significant frequency. For such people, the prevailing foreign currency rate is most relevant for international comparisons. So there is a stronger case for using official exchange rates in studies of global wealth.

The second issue concerns the appropriate unit of analysis. A case can be made for basing the analysis on households or families. However, personal assets and debts are typically owned (or owed) by named individuals and may be retained by those individuals if they leave the family. Furthermore, even though some household assets, such as housing, provide communal benefits in households that include members other than a single individual or married couple, it is unusual for members to have an equal say in the management of assets, or to share equally in the proceeds if the asset is sold. Membership of households can be

Global Wealth Databook 2023

quite fluid (e.g., with respect to older children living away from home) and the pattern of household structure varies markedly across markets. For all these reasons ? plus the practical consideration that the number of households is unknown in most markets ? we prefer to base our analysis on individuals rather than household or family units. More specifically, since children have little formal or actual wealth ownership, we focus on wealth ownership by adults, defined to be individuals aged 20 or above.

1.2 Household balance sheet data The most reliable source of information on household wealth is HBS data. As shown in Table 1-1, "complete" financial and non-financial balance sheet data are available for 29 markets for at least one year. These are predominantly high-income markets, the exceptions being China, Mexico and South Africa, which fall within the upper middle-income category according to the World Bank, and India which is classed as lower middleincome. The data are described as complete if financial assets, liabilities and non-financial assets are all adequately covered. Another 24 markets have financial balance sheets, but no details of real assets. This group contains seven upper middleincome markets, including Russia. It also includes ten other transition markets. Hence it is less biased toward the rich Western world than the group with complete household balance sheets. The sources of these data are recorded in Table 1-2.

Europe and North America, and OECD markets in particular, are well represented among markets with HBS data. China joined this group a few years ago. There had been considerable recent discussion of the household balance sheet in China. Li (2017) surveyed the series that had been developed by different researchers. Piketty et al. (2017, 2019) provide the most comprehensive data and also the longest times series, so we use their estimates here. Li (2017) shows that his own independent estimates, which are for 2004?14 only, are similar to those of Piketty et al. (2017) if farmland is omitted from the latter. This provides support for the accuracy of the Piketty et al. estimates, but also a reason to prefer them, in addition to the greater length of their time series, since farmland is a key household asset in rural China. Piketty et al. estimate the value of this land carefully, taking into account its increasingly private character over time.

Relatively few markets have HBS coverage in Africa, Asia-Pacific and Latin America. However, it is available for some of the larger economies in these regions (see Table 1-1). Complete HBS data is found in Chile and Mexico (although not in all years), while financial HBS is available in Brazil and Colombia. In the Asia-Pacific region (excluding China and India) seven highincome markets, including for example Australia, Japan and Korea, have complete HBS data while three sizeable upper middle-income markets have financial HBS. Africa has the least coverage ? HBS data is found only in South Africa.

1.3 Household survey data Information on assets and debts is collected in nationally representative surveys undertaken in an increasing number of markets (see Table 1-3 for our current list and sources.) For two markets this is the only data we have, and we use it to help estimate wealth levels, as explained in the next section, as well as distributions. Data on wealth obtained from household

surveys vary in quality, due to the sampling and non-sampling problems faced by all sample surveys. The high skewness of wealth distributions makes sampling error important. Nonsampling error is also a problem due to differential response rates ? above some level wealthier households are less likely to participate ? and under-reporting, especially of financial assets. Both of these problems make it difficult to obtain an accurate picture of the upper tail of the wealth distribution using survey evidence alone. To compensate, wealthier households are oversampled in an increasing number of surveys. This is best done using individual information, as in the US Survey of Consumer Finances, the Household Finance and Consumption (HFCS) surveys in Finland, France and Spain, and the Wealth and Assets Survey (WAS) in the United Kingdom (Vermeulen, 2018). Oversampling at the upper end is not routinely adopted by the developing markets that include asset information in their household surveys, but the reported response rates tend to be higher than in developed markets and the sample sizes are very large in some cases, e.g., in India.

The US Survey of Consumer Finance is sufficiently well designed to capture most household wealth, but this is atypical. In particular, surveys usually yield lower totals for financial assets compared with HBS data. However, surveys generally do remarkably well for owner-occupied housing, which is the main component of non-financial assets (Davies and Shorrocks, 2000). Our methodology recognizes the general underreporting of financial assets in surveys and attempts to correct this deficiency.

For markets which have both HBS and survey data, when estimating wealth levels we give priority to the HBS figures. The HBS estimates typically use a market's wealth survey results as one input, but also take account of other sources of information and should therefore dominate wealth survey estimates in quality. However, this does not ensure that HBS data are error-free.

1.4 Estimating the level and composition of wealth for other markets

We use standard econometric techniques to establish the determinants of per capita wealth levels in the 53 markets with HBS or survey data in at least one year. The regression equations are then used to estimate wealth levels in the markets that have no direct data on wealth. Availability of data on the explanatory variables needed for the latter procedure limits the number of markets that can be included. However, we are able to estimate wealth values for 171 markets, which collectively cover 95% of the world's population in 2022 either from direct data on wealth or by using this regression-based procedure. There is a trade-off here between coverage and reliability. Alternative sets of explanatory variables could achieve greater market coverage, but not without compromising the quality of the regressionbased estimates.

Separate regressions are run for financial assets, non-financial assets and liabilities. As errors in the three equations are likely to be correlated, the seemingly unrelated regressions (SUR) technique due to Zellner (1962) is applied, but only to financial assets and liabilities, since there are fewer observations for nonfinancial assets. In particular, we include a dummy for cases

Global Wealth Databook 2023

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where the data source is a survey rather than HBS data. This turns out to be negative and highly significant in the financial assets regression, indicating that the average level of financial assets tends to be much lower when the data derive from sample surveys. We use this result to adjust upwards the value of financial assets in the wealth level estimates for Indonesia and Uruguay. We also include region-income dummies to capture any common fixed effects at the region-income level, and year dummies to control for shocks ? like the global financial crisis ? or time trends that affect the world as a whole.

The resulting estimates of net worth per adult and the three components are reported in Table 2-2 for the years 2000 to 2022. HBS data are used where available (see Table 1-1); adjusted survey means are used for Indonesia and Uruguay in specific years.

A growing number of markets are reporting wealth data with relatively little delay ? around three months in the case of the United States, for example. For markets lacking HBS data we use information on changes in house price indexes, share prices, and GDP per adult to update the estimates of each of the wealth components.

1.5 Wealth distribution within markets An analysis of the global pattern of wealth holdings by individuals requires information on the distribution of wealth within markets. Direct observations on wealth distribution across households or individuals are available for 39 markets. The number of survey years we have varies across markets. Summary details are reported in Table 1-5 using a common template, which gives the shares of the top 10%, 5%, and 1%, together with other distributional information in the form of cumulated shares of wealth (i.e., Lorenz curve ordinates).

The distributional data have certain fairly standard features. The unit of analysis is usually a household or family, but is in a few cases the (adult) individual. Household sample surveys are employed in almost all markets. The exceptions are the Nordic markets (Denmark, Finland, Norway and Sweden), which use data from tax and other registers covering the entire population. For all other markets, except the United States, the wealth shares of the top groups are expected to be understated because wealthy households are less likely to respond, and because the financial assets that are of greater importance to the wealthy ? e.g., equities and bonds ? are especially likely to be under-reported. And in those markets using register data there can be difficulties due to valuation problems, e.g., in connection with pension assets and life insurance. The United States has Distributional Financial Accounts (DFA) published quarterly that combine the triennial Survey of Consumer Finance (SCF) and Flow of Funds balance sheet data, as well as taking into account the wealth of the "Forbes 400". The DFA provides shares of the top 1%, top 10% and bottom 50%. We interpolate other shares using the SCF survey nearest in date.

The summary details reported in Table 1-5 show a great deal of distributional information, but there are some empty cells. Estimates for the empty cells were generated by a revised version of the Shorrocks-Wan ungrouping program, which constructs a synthetic sample conforming exactly to any set of

Lorenz values (Shorrocks and Wan, 2009). Where markets have some wealth distribution data, Lorenz curves for missing years are estimated by interpolation or by projection forwards or backwards.

For most markets lacking direct wealth distribution data, the pattern of wealth distribution was constructed from information on income distribution, based on the view that wealth inequality is likely to be fairly highly correlated with income inequality across the markets with missing wealth data. Income distribution data was derived from the World Income Inequality Database, and the ungrouping program was used to generate all the Lorenz curve values required for the same template applied to wealth distribution.

For the 40 markets that have data on both wealth and income distribution, the Lorenz curves for wealth are everywhere lower than for income, indicating that wealth is more unequally distributed than income. We calculate the Gini coefficient values for both income and wealth and then estimate the missing Lorenz curves for wealth by scaling down the Lorenz curves for income by the median ratio of income to wealth Ginis.

To generate regional and global wealth patterns, each market lacking income distribution data was assigned the average (adult population weighted) wealth distribution pattern for the corresponding subregion. This again was done in preference to simply disregarding the markets concerned.

1.6 Assembling the global distribution of wealth To construct the global distribution of wealth, the level of wealth for each market was combined with details of its wealth pattern. Specifically, the ungrouping program was applied to each market to generate a set of synthetic sample values and sample weights consistent with the (estimated or imputed) wealth distribution, with the sample weights representing approximately 10,000 adults in the bottom 90% of the distribution, 1,000 adults in the top decile, and 100 adults in the top percentile. The wealth sample values were then scaled up to match the mean wealth of the respective market and merged into a single world dataset comprising between 1.1 million and 1.5 million observations for each year. The complete global sample may be processed in a variety of ways, e.g., to obtain the minimum wealth and the wealth share of each percentile in the global distribution of wealth. The distribution within regions may also be calculated, along with the number of representatives of each market in any given global wealth percentile.

1.7 Adjusting the upper wealth tail The survey data which yield most of our wealth distribution estimates tend to under-represent the wealthiest groups and to omit ultra-high net worth individuals. This deficiency does not affect our estimates of average wealth levels around the world, since these are determined by other methods. But it does imply that the shares of the top percentile and top decile are likely to err on the low side unless adjustments are made to the upper tail.

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Global Wealth Databook 2023

We would also not expect to generate accurate predictions of the number and value of holdings of high-net-worth individuals.

We tackle this problem by exploiting well-known statistical regularities in the top wealth tail and by making use of information on the wealth holdings of named individuals revealed in the rich list data reported by Forbes magazine. As described in more detail in Section 3, we used the number of billionaires reported by Forbes to fit a Pareto distribution to the upper tail of 56 markets and five regions. The revised top tail values in the synthetic sample were then replaced by the new estimates, and the resulting sample for each market was rescaled to match the mean wealth value. This sequence was repeated until the process converged, typically after a few rounds. The overall global weighted sample still contains between 1.1 and 1.5 million observations, typically representing about 100, 1,000 or 10,000 adults. The adjusted sample can be used to produce improved estimates of the true wealth pattern within markets, regions, and the world. The minimum sample size of 100 allows reliable estimates of the number and value of wealth holdings up to USD 100 million at the regional and global level. Estimates above USD 100 million are obtained by projecting the Pareto distribution forward.

1.8 Concluding remarks Great progress has been achieved in recent years in the study of global household wealth. Data on the level of wealth have improved in quality and are available for more markets. New household wealth surveys have begun in many markets. However, much remains to be done to improve the quality and frequency of wealth data, and to make the data available for a greater number of markets. In the meantime, we will continue to try to fill the gaps in the estimates of wealth level by market and to improve the estimates of wealth distribution within markets. In future, some revisions to our estimates are inevitable. Nevertheless, we are confident that the broad trends revealed in the Global Wealth Report for 2023 will remain substantially intact.

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Table 1-1: Coverage of wealth levels data

Complete financial and non-financial data for at least one year

region

income group

market

financial non-financial data years data years

North America

High

Canada

2000-22

2000-22

United States

2000-22

2000-22

Latin America High

Chile

2002-22

2007-07

Uruguay

2013

2013

Latin America Upper middle

Mexico

2008-22

2003-20

Europe

High

Czechia

2000-22

2000-19

Denmark

2000-22

2000-16

Finland

2000-22

2000-20

France

2000-22

2000-19

Germany

2000-22

2000-16

Greece

2000-22

2000-10

Hungary

2000-22

2000-12

Italy

2000-22

2000-20

Netherlands

2000-22

2000-14

Spain

2000-22

2000-19

Sweden

2000-22

2000-20

Asia-Pacific High

Switzerland United Kingdom Australia

2000-22 2000-22 2000-22

2000-22 2000-19 2000-22

Israel

2001-17

2001-13

Japan

2000-22

2000-18

Korea

2000-22

2000-19

New Zealand

Singapore

Taiwan

Asia-Pacific Upper middle

Mainland China

Asia-Pacific Lower middle

India

Indonesia

Africa

Upper middle

South Africa

Note: survey data only for Uruguay and Indonesia

2000-22 2000-22 2000-20 2000-15 2000-19

2000 2000-22

2000-22 2000-22 2006-12 2000-15 2002-12

2000 2000-22

Markets with complete financial and non-financial data for at least one year Markets with financial data only Markets with wealth level fully estimated by regression method Markets with wealth level assigned via wealth/GDP ratio

Financial data (only)

region

income group

Latin America

Upper middle income

Europe

High income

Europe

Upper middle income

Asia-Pacific

Upper middle income

market Brazil

financial data years

2009-18

Colombia Austria Belgium Croatia Cyprus Estonia Iceland Ireland Latvia Lithuania Luxembourg Malta Norway Poland Portugal Romania

2000-22 2000-22 2000-22 2001-22 2004-22 2004-22 2003-20 2002-22 2004-22 2004-22 2000-22 2000-22 2000-22 2003-22 2000-22 2000-22

Slovakia

2000-22

Slovenia

2004-22

Bulgaria

2006-22

Russia

2011-22

Kazakhstan 2009-09

Thailand Turkey

2005-05 2009-22

Number

29 24 118 46

Cumulated Cumulated percentage of percentage of

population total wealth

54.9

87.8

63.8

93.4

95.1

99.1

100

100

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Global Wealth Databook 2023

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