CS Global Wealth Databook 2015.pdf

CS Global Wealth Databook 2015.pdf - October 2015 Research...

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Unformatted text preview: October 2015 Research Institute Thought leadership from Credit Suisse Research and the world’s foremost experts Global Wealth Databook 2015 October 2015 Preface Since 2010, the Credit Suisse Wealth Report has been the leading reference on global wealth. It contains the most comprehensive and up-to-date findings on global wealth across the entire wealth spectrum – from the very base of the “wealth pyramid,” capturing 3.4 billion adults with wealth below USD 10,000, to the millionaires, who account for 0.7% of adult population, but yet own 45.2% of global wealth. Research for the Credit Suisse Global Wealth Databook has been undertaken on behalf of the Credit Suisse Research Institute by Professors Anthony Shorrocks and Jim Davies, recognized authorities on this topic and the architects and principal authors of "Personal Wealth from a Global Perspective," Oxford University Press, 2008. Rodrigo Lluberas has also been a very significant contributor to the project. The aim of the Credit Suisse Global Wealth project is to provide the best available estimates of the wealth holdings of households around the world for the period since the year 2000. While the Credit Suisse Global Wealth Report highlights the main findings of our study, this 158-page Databook underlines the extent of our analysis. More importantly, it sets out in detail the data employed in our Global Wealth project, the methodology used to calculate estimates of wealth and how this may differ from other reports in this field. The Credit Suisse Global Wealth Databook provides detailed information on the evolution of household wealth levels through the period 2000 to mid-2015 at both the regional and country level. It presents our estimates on the distribution of wealth for over 200 countries. Based on this rich data, the Databook presents findings on the global middle class, its size and how it fared over time. Markus Stierli Head of Fundamental Micro Research, Credit Suisse Private Banking & Wealth Management Credit Suisse Global Wealth Databook 2015 3 October 2015 Contents 3 5 Preface Section 1 Estimating the pattern of global household wealth 10 Table 1-1 Coverage of wealth levels data 11 Table 1-2 Household balance sheet and financial balance sheet sources 13 Table 1-3 Survey sources 14 Table 1-4 Changes in asset prices and exchange rates 2013–2015, selected countries 15 Table 1-5 Wealth shares for countries with wealth distribution data 17 Section 2 Household wealth levels, 2000–2015 19 Table 2-1 Country details 23 Table 2-2 Population by country (thousands) 27 Table 2-3 Number of adults by country (thousands) 31 Table 2-4 (by year) Wealth estimates by country 2000–2015 95 Table 2-5 Components of wealth per adult in USD, by region and year 96 Table 2-6 Components of wealth as percentage of gross wealth, by region and year 97 Table 2-7 Changes in household wealth 2014–2015, selected countries 98 Section 3 Estimating the distribution of global wealth 101 Table 3-1 Wealth pattern within countries, 2015 105 Table 3-2 Wealth pattern by region, 2015 106 Table 3-3 Membership of top wealth groups for selected countries, 2015 107 Table 3-4 Percentage membership of global wealth deciles and top percentiles by country of residence, 2015 111 Table 3-5 Main gains and losses in global wealth distribution, 2014–2015 112 Table 3-6 High net worth individuals by country and region, 2015 114 Section 4 The global middle class 121 Table 4-1 Minimum wealth of middle class, 2015, selected countries 122 Table 4-2 Share of middle-class adults and wealth, 2015 by region 123 Table 4-3 Number of middle-class adults (million), 2015 by region and country 124 Table 4-4 Middle-class share of all adults (%), 2015, by country and region 125 Table 4-5 Wealth holdings of middle class, 2015, by country and region 126 Table 4-6 Ratio of share of middle-class wealth to share of middle-class adults, 2015 127 Table 4-7 Change in number of middle-class adults, 2000–2015, for regions and selected countries 128 Table 4-8 Change in total wealth of middle class, 2000–2015, for regions and selected countries 129 Table 4-9 Percentage of wealth owned by middle-class adults, 2000–2015, by region 129 Table 4-10 Number of middle-class adults (million) in China and USA, 2000–2015 130 Section 5 Composition of wealth portfolios 133 Table 5-1 Assets and debts as percentage of gross household wealth for selected countries by year 134 Table 5-2 Percentage composition of gross household financial wealth, by country and year 137 Section 6 Region and country focus 143 Table 6-1 Summary details for regions and selected countries, 2015 144 Table 6-2 Wealth per adult (in USD) at current and constant exchange rates, for regions and selected countries, 2000–2015 146 Table 6-3 Total wealth (in USD bn) at current and constant exchange rates, for regions and selected countries, 2000–2015 148 Table 6-4 Composition of wealth per adult for regions and selected countries, 2015 149 Table 6-5 Wealth shares and minimum wealth of deciles and top percentiles for regions and selected countries, 2015 150 Table 6-6 Distribution of wealth for regions and selected countries, 2015 153 156 157 158 Bibliography and data references About the authors Imprint General disclaimer / Important information Credit Suisse Global Wealth Databook 2015 4 October 2015 1. Estimating the pattern of global household wealth 1.1 Introduction We aim to provide the best available 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 nations of individual net worth, defined as the marketable value of financial assets plus non-financial assets (principally housing and land) less debts. No country in the world has a single comprehensive source of information on personal wealth, and many low and middle income countries have little direct evidence of any kind. However a growing number of countries – including China and India as well many high income countries – have relevant data from a variety of different sources which we are able to exploit in order to achieve our objective. The procedure involves three main steps, the first two of which mimic the structure followed by Davies et al (2008, 2011). The first step establishes the average level of wealth for each country. The best source of data for this purpose is household balance sheet (HBS) data, which are now provided by 48 countries, although 31 of these countries cover only financial assets and debts. An additional four countries have household survey data from which wealth levels can be calculated. Together these countries cover 66% of the global population and 96% of total global wealth. The results are supplemented by econometric techniques, which generate estimates of the level of wealth in 160 countries that lack direct information for one or more years. The second step involves constructing the pattern of wealth holdings within nations. Direct data on the distribution of wealth are available for 31 countries. Inspection of data for these countries suggests a relationship between wealth distribution and income distribution, which can be exploited in order to provide a rough estimate of wealth distribution for 135 other countries, which have data on income distribution but not on wealth ownership. 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 countries. To overcome this deficiency, the third step makes use of the information in the rich lists published by Forbes Magazine and others to adjust the wealth distribution pattern in the highest wealth ranges. Implementing these procedures leaves 50 countries for which it is difficult to estimate either the level of household wealth or the distribution of wealth, or both. Usually the countries concerned are small (e.g. Andorra, Bermuda, Guatemala, Monaco) or semi-detached from the global economy (e.g. Afghanistan, Cuba, North Korea). For our estimates of the pattern of global wealth, we assign these countries the average level and distribution of the region and income class to which they belong. This is done in preference to omitting the countries altogether, which would implicitly assume that their pattern of wealth holdings matches the world average. However, checks indicate that excluding these nations from the global picture makes little difference to the results. Table 2-1 lists the 215 countries in the world along with some summary details. Note that China and India are treated as separate regions due to the size of their populations. 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. 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 countries a large share of personal wealth is owned by households in the top few percentiles of the distribution, who tend to be internationally mobile Credit Suisse Global Wealth Databook 2015 5 October 2015 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, it is unusual for household 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 quite fluid (for example, with respect to older children living away from home) and the pattern of household structure varies markedly across countries. For all these reasons – plus the practical consideration that the number of households is unknown in most countries – 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 household balance sheet (HBS) data. As shown in Table 1-1, “complete” financial and non-financial “real” balance sheet data are available for 17 countries for at least one year. These are predominantly high income countries, the exceptions being the Czech Republic and South Africa, which fall within the upper middle income category according to the World Bank. The data are described as complete if financial assets, liabilities and non-financial assets are all adequately covered. Another 31 countries have financial balance sheets, but no details of real assets. This group contains eleven upper middle income countries and six lower middle income countries, and hence is less biased towards the rich world. The sources of these data are recorded in Table 1-2. Europe and North America, and OECD countries in particular, are well represented among countries with HBS data, but coverage is sparse in Africa, Asia and Latin America. Fortunately survey evidence on wealth is available for the largest developing countries – China, India and Indonesia – which compensates to some extent for this deficiency. Although only financial HBS data are available for Russia, complete HBS data are available for the Czech Republic and financial data are recorded for nine other former socialist countries in Europe. 1.3 Household survey data Information on assets and debts is collected in nationally representative surveys undertaken in an increasing number of countries (see Table 1-3 for the current list and sources.) For three countries this is the only data we have, and we use it to estimate wealth levels (with a correction for financial assets 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. Non-sampling error is also a problem due to differential response rates – above some level wealthier households are less likely to participate – and underreporting, especially of financial assets. Both of these problems make it difficult to obtain an accurate picture of the upper tail of the wealth distribution. To compensate, wealthier households are over-sampled in an increasing number of surveys, such as the US Survey of Consumer Finances and similar surveys in Canada, Germany, Spain, and several other EU countries. Over-sampling at the upper end is not routinely adopted by the developing countries which include asset information in their household surveys, but the response rates are much higher than in developed countries, and the sample sizes are large in China and India: 16,035 for the 2002 survey in China, and 105,800 for the 2012−2013 survey 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 usually do remarkably well for owner-occupied Credit Suisse Global Wealth Databook 2015 6 October 2015 housing, which is the main component of non-financial assets (see Davies and Shorrocks, 2000, p. 630). Our methodology recognizes the general under-reporting of financial assets in surveys and attempts to correct this deficiency. Other features of the survey evidence from developing countries capture important real differences. Very high shares of non-financial wealth are found for the two low-income countries in our sample, India and Indonesia, reflecting both the importance of land and agricultural assets and the lack of financial development. On the other hand, the share of nonfinancial assets in China is relatively modest, in part because urban land is not privately owned. In addition, there has been rapid accumulation of financial assets by Chinese households in recent years. Debts are very low in India and Indonesia, again reflecting poorly developed financial markets. For countries which have both HBS and survey data, we give priority to the HBS figures. The HBS estimates typically use a country’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 countries For countries lacking direct data on wealth, we use standard econometric techniques to estimate per capita wealth levels from the 51 countries with HBS or survey data in at least one year. Data availability limits the number of countries that can be included in this procedure. However, we are able to employ a theoretically attractive model that yields observed or estimated wealth values for 174 countries, which collectively cover 97% of the world’s population in 2015. There is a trade-off here between coverage and reliability. Alternative sets of explanatory variables could achieve greater country coverage, but not without compromising the quality of the regression 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 non-financial assets. The independent variables selected are broadly those used in Davies et al (2011). In particular, we include a dummy for cases 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 China, India and Indonesia. 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 recent 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 2-4 for the years 2000 to 2015. HBS data are used where available (see Table 1-1); corrected survey data are used for China, India and Indonesia in specific years. Financial assets and liabilities are estimated for 147 countries, and non-financial assets for 164 countries in at least one year using the regressions described in the previous section. There remain 38 countries containing 3% of the global adult population without an estimate of wealth per adult. In order to generate wealth figures for regions and for the world as a whole, we assigned to each of these countries the mean wealth per adult of the corresponding region (six categories) and income class (four categories). This imputation is admittedly crude, but better than simply disregarding the excluded countries, which would implicitly assume (incorrectly) that the countries concerned are representative of their region or the world. For a few countries, including the United States, wealth levels are available for the most recent years, including the first quarter of 2015. However, the majority of countries are missing wealth levels for at least part of the period 2012–2015. In order to obtain estimates of net worth per adult and its components we update the most recent available figures using, where available, Credit Suisse Global Wealth Databook 2015 7 October 2015 house price growth for non-financial assets, market capitalization for financial assets and GDP per capita growth for debts (see Table 1-4). Our projections are based on estimated relationships between these variables and the corresponding asset/debt totals in preceding years, rather than on proportionality. For countries without information on house prices and market capitalization, recent growth of GDP per capita is used to project net worth per adult forwards to mid-2015. 1.5 Wealth distribution within countries An analysis of the global pattern of wealth holdings by individuals requires information on the distribution of wealth within countries. Direct observations on wealth distribution across households or individuals are available for 31 countries. One set of figures was selected for each of these nations, with a preference for the most recent year, and for the most reliable source of information. Summary details are reported in Table 1-5 using a common template, which gives the shares of the top 10%, 5%, 1%, together with other distributional information in the form of cumulated shares of wealth (i.e. Lorenz curve ordinates.) The distributional data now available 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 countries. The exceptions are the Nordic countries (Denmark, Finland, Norway and Sweden) and Switzerland...
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  • Fall '18
  • Distribution of wealth, Household income in the United States, Wealth in the United States, Credit Suisse Global Wealth Databook

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