In Europe, the variation in average amounts of financial wealth per adult varied considerably as of 2022, from approximately ******* U.S. dollars in Switzerland to roughly ***** U.S. dollars in Azerbaijan. In Europe, the overall average financial wealth per adult as of 2022 was ****** U.S. dollars. In terms of private wealth, Europe held the second highest value in the world, after North America. What is financial wealth? Financial wealth, also known as financial assets or liquid assets can include wealth that an individual has in the forms of cash, stocks, bonds, mutual funds, and bank deposits. In addition to financial wealth, wealth can also be measured in other assets, called non-financial wealth. This includes physical assets, such as real estate, land, vehicles, jewelry, and art, just to name a few. Where do most wealthy individuals live? Individuals with a net worth over *********** U.S. dollars are called high-net worth individuals (HNWI). The United States was the home country to the highest number of HNWIs in 2021. China followed, although their number of HNWIs did not even reach ********* of the number in the United States. In Europe, Switzerland is the country with the highest average financial wealth per adult, but with its small population size, the number of HNWIs does not come near the numbers in the United Kingdom, Germany, France, and Italy – the European countries with the highest number of HNWIs. Considering Switzerland’s small population size, however, it is the country in the world with the highest proportion of millionaires.

The statistic displays the average value of wealth per adult in selected European countries as of 2014. The average value of wealth per adult in Luxembourg amounted to 432.2 thousand euros, while in the United Kingdom (UK) it reached approximately 188.6 thousand euros.

In 2023, the Middle East and North Africa, and Latin America were the regions with the lowest level of distribution of wealth worldwide, with the richest ten percent holding around ** percent of the total wealth. On the other hand, in Europe, the richest ten percent held around ** percent of the wealth. East and South Asia were the regions where the poorest half of the population held the highest share of the wealth, but still only around **** percent, underlining the high levels of wealth inequalities worldwide.

Purpose and brief description EU-SILC (European Union - Statistics on Income and Living Conditions) is a survey on income and living conditions and an important tool to map poverty and social exclusion at both Belgian and European level. The objective of this survey is to establish a global framework for the production of 'Community' statistical data on income and living conditions (EU-SILC), including both coherent cross-sectional and longitudinal data on income and poverty (level, composition,...) at national and European level. The survey is carried out in Belgium and in the other EU Member States and is coordinated by Eurostat, the statistical office of the European Union. In Belgium, the SILC is organised by Statbel. Population Private households in Belgium Data collection method and sample size CAPI (Computer Assisted Personal Interview) - CATI (Computer Assisted Telephone Interview). Response rate ± 60% (N= ± 6.000 households) Periodicity Annually. Release calendar First quarter after survey year Forms SILC: individual questionnaire SILC: questionnaire households Definitions Risk of poverty or social exclusion (AROPE) The risk of poverty or social exclusion, abbreviated AROPE, refers to the situation in which individuals are faced with at least one of the 3 following poverty risks: monetary poverty, severe material and social deprivation or living in a household with very low work intensity. The AROPE rate, the share of the total population at risk of poverty or social exclusion, is the main indicator for monitoring the ‘EU 2030’ target on poverty and social exclusion. Poverty risk = Monetary poverty risk (AROP) The at-risk-of-poverty rate (AROP) is the percentage of people with an equivalised disposable income (after social transfer) below the poverty threshold. The indicator does not measure wealth or poverty, but low income in comparison to other residents in that country. This does not necessarily imply a low standard of living. Poverty risk before social transfers: Percentage of people whose equivalised disposable income after deduction of all social transfers falls below the poverty threshold. Poverty risk before social transfers, excluding pensions: Percentage of people whose equivalised disposable income after deduction of social transfers, excluding pensions, falls below the poverty threshold. Material and social deprivation rate (MSD) and severe material and social deprivation (SMSD) The material and social deprivation rate refers to the inability to afford some goods/services considered by most people to be desirable or even necessary to lead an adequate life. The indicator distinguishes between individuals who cannot afford a certain good/service/activity, and those who do not have this good/service/activity for another reason, e.g. because they do not want or do not need it. The EU-SILC survey asks households about their financial (in)ability to: Pay the bills as scheduled Take every year one week’s holiday away from home Eat a meal with meat, chicken, fish or vegetarian equivalent every second day Face unexpected financial expenses Afford a car Keep the home warm Replace damaged or worn-out furniture In addition, people are asked about their individual financial (in)ability to: Replace worn out or old-fashioned clothes by new ones Have two pairs of shoes in good condition Afford an internet connection at home Get together with friends/family (relatives) for a drink/meal at least once a month Participate regularly in a leisure activity Spend a small amount of money each week on yourself The material and social deprivation rate (MSD) is defined as the enforced inability to pay for at least five of the above-mentioned items. The severe material and social deprivation rate (SMSD) is defined as the enforced inability to pay for at least seven of the above-mentioned items. Low work intensity (LWI) The indicator persons living in households with very low work intensity is defined as the number of persons living in a household where the members of working age worked a working time less than 20% of their total work-time potential during the previous 12 months. The work intensity of a household is the ratio of the total number of months that all working-age household members have worked during the income reference year and the total number of months the same household members theoretically could have worked in the same period. An employee of working age is a person aged 18-59, excluding students aged 18-24. Households composed only of children, of students aged less than 25 and/or people aged 60 or more are completely excluded from the indicator calculation. Level of education The level of education is measured using a detailed questionnaire, and the people are then divided into three groups. Low-skilled people are people who list lower secondary education as their highest level of education. Medium-skilled people are people who obtained a diploma of higher secondary education but not of higher

This article advances the literature on the spatial patterns of EU support by arguing that the relationship between regional inequality and EU trust is not linear. We posit that, to fully understand this relationship, we should systematically investigate three dimensions of regional inequality, i.e., regional wealth status, regional wealth growth, and regional wealth growth at different levels of wealth status. Using individual-level survey data for EU27 countries and the UK from 11 Eurobarometer waves (2015-2019), we show that a non-linear association exists whereby poor and rich European regions tend to trust the EU more compared to middle-income regions, and that within-region over-time growth is associated with higher levels of EU trust. We demonstrate that the association between growth and EU trust is more pronounced among poor and middle-income regions compared to rich regions. Our findings have implications about the nature of public Euroscepticism and the ways in which to address it.

This table contains statistics regarding income and capital of self-employed persons in the Netherlands. A distinction is made between, on the one hand, persons for whom self-employment provides for the main source of income, and on the other hand all persons with income from self-employed work. The figures in this table are broken down by type of self-employed person, sector, gender, age, migration background, position in the household, and by income and wealth decile groups.

All statistics in this table are at the individual level, this includes capital; (corporate) assets are summed per household and then assigned to all household members, thus serving as a measure of personal prosperity. The sample date for both population and capital is the first of January of the reporting year. For the older years 2007 up to and including 2010, capital is sampled on the first of January of the year following the reporting year.

The General Business Register (ABR) is used to determine the sector (SBI) of self-employed persons. The ABR has been subject to various trend breaks in the period 2007-2011. This leads to a sharp decrease in the number of self-employed persons in the financial services (sector K) in 2010. Therefore caution is advised when consulting sector trends or comparing numbers across sectors.

Data available from: 2007.

Status of the figures: The figures for 2006 to 2022 are final. The figures for 2023 are preliminary.

Changes as of November 1 2024: Figures for 2022 have been finalized. Figures for 2023 have been added.

Changes as of March 2022: Figures on the wealth of the self-employed in 2010 were incorrect, and have been removed. For this year the wealth of 2011 applies, as 2011 marks a shift in sample date from December 31 to January 1. Missing wealth figures for 2013 have been supplemented.

Changes as of July 2021: Revised data for 2006 to 2019 have been added. Due to the availability of new sources and improvements in the methodology, wealth figures have changed. Additionally everyone with personnel is now classified as self-employed with employee (formerly this distinction was based solely on the enterprise constituting the main source of income).

When will new figures be published? New figures for 2024 will be published in December 2025.

In 2023, Switzerland led the ranking of countries with the highest average wealth per adult, with approximately ******* U.S. dollars per person. Luxembourg was ranked second with an average wealth of around ******* U.S. dollars per adult, followed by Hong Kong SAR. However, the figures do not show the actual distribution of wealth. The Gini index shows wealth disparities in countries worldwide. Does wealth guarantee a longer life? As the old adage goes, “money can’t buy you happiness”, yet wealth and income are continuously correlated to the quality of life of individuals in different countries around the world. While greater levels of wealth may not guarantee a higher quality of life, it certainly increases an individual’s chances of having a longer one. Although they do not show the whole picture, life expectancy at birth is higher in the wealthier world regions. Does money bring happiness? A number of the world’s happiest nations also feature in the list of those countries for which average income was highest. Finland, however, which was the happiest country worldwide in 2022, is missing from the list of the top twenty countries with the highest wealth per adult. As such, the explanation for this may be the fact that the larger proportion of the population has access to a high income relative to global levels. Measures of quality of life Criticism of the use of income or wealth as a proxy for quality of life led to the creation of the United Nations’ Human Development Index. Although income is included within the index, it also has other factors taken into account, such as health and education. As such, the countries with the highest human development index can be correlated to those with the highest income levels. That said, none of the above measures seek to assess the physical and mental environmental impact of a high quality of life sourced through high incomes. The happy planet index demonstrates that the inclusion of experienced well-being and ecological footprint in place of income and other proxies for quality of life results in many of the world’s materially poorer nations being included in the happiest.

Coordinated by Facundo Alvaredo, Anthony B. Atkinson, Thomas Piketty, Emmanuel Saez and Gabriel Zucman, the World Wealth and Income Database aims to provide open access to data series on income and wealth worldwide. The goal is to be able to produce Distributional National Accounts: estimates of the distribution of wealth and income using concepts that are consistent with the macroeconomic national accounts. The focus lies not only on the national level, but also on the global and regional level.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The CSB Minimum Income Protection Indicators database contains data on minimum income protection provisions for workers, people at working age not in work, and the elderly. Information on net disposable incomes is available since 1992 for 15 EU member states. From 2001 on, CSB-MIPI covers 27 countries, mostly EU member states. In addition, yearly time series on the evolution of gross benefit levels for the 1990s and 2000s are provided.

This dataset provides values for GDP PER CAPITA PPP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Sampling Procedure Comment: Probability Sample: Multistage Stratified Random Sample

As of 2023, the countries in Europe with the greatest share of national wealth taken by the top one percent of wealthy people were Russia, Turkey, and Hungary, with over two-thirds of wealth in Russia being owned by the wealthiest decile. On the other hand, the Netherlands, Belgium, and Slovakia were the countries with the smallest share of national wealth going to the top one percent, with more than half of wealth in the Netherlands going to the bottom 90 percent.

The wealth-health relationship is not unambiguous and constant. Greater wealth affects individual and population health in opposite ways. Increased risk factors especially raise the probability of noncommunicable diseases (NCDs) impacting a population. Conversely, better healthcare and awareness reduce the chances of developing these diseases or increase the likelihood of treatment and cure. Therefore, this paper aims to assess and quantify the hard-to-grasp overall impact of prosperity on mortality from selected NCDs, allowing us to capture the relevant differences between European regions. In particular, we attempt to estimate the net effect of affluence and the health economic threshold of the GDP-mortality relationship, by developing a dedicated analytical tool based on joinpoint regression and forecasting methods. Our results show that in the case of most investigated diseases in more impoverished regions, a clear pattern reflects mortality rising with prosperity. After crossing the health economic threshold of around 20 thousand euros per capita, the trend changes by stabilising or reversing. The research we present shows that health policy should be more diversified locally to enable health convergence at the national and European regional levels. Moreover, health policy should evolve to prioritise mental and neurological disorders, by improving the resource allocation and increasing public awareness.

The JRC - Bioeconomics dataset has been elaborated jointly by JRC and the nova-Institute (nova-Institut für politische und ökologische Innovation GmbH). This database quantifies employment, value added and turnover in the Bioeconomy and in bioeconomy sectors, namely agriculture, forestry, fishing, the manufacture of food, beverage and tobacco, the manufacture of bio-based textile, the manufacture of wood and wood products, the manufacture of paper, the manufacture of bio-based chemicals, the manufacture of bio-based pharmaceuticals, the manufacture of bioplastics, the manufacture of liquid biofuels and the production of bioelectricity. The geographical scope of this database is the EU, processeed as aggregate and at individual EU Member state level. Since the data refers to the period 2008 to 2017, two aggregates are considered: "EU-27 (2020)" (current EU Members as from 1st February 2020) and "EU-28" (referring to the EU Members between 1st July 2013 and 31st January 2020). Please note that EU-28 includes Croatia also for data before 2013

This data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all Luxembourg Wealth Study (LWS) datasets in all waves (as of March 2022). It includes Gini coefficients calculated on: • Disposable Net Worth • Value of Principal residence • Financial AssetsThis project sought to renew the ESRC's invaluable financial support to LIS (formerly the Luxembourg Income Study) for a period of five more years. LIS is an independent, non-profit cross-national data archive and research institute located in Luxembourg. LIS relies on financial contributions from national science foundations, other research institutions and consortia, data-providing agencies, and supranational organisations to support data harmonisation and enable free and unlimited data access to researchers in the participating countries and to students world-wide. LIS' primary activity is to make harmonised household microdata available to researchers, thus enabling cross-national, interdisciplinary primary research into socio-economic outcomes and their determinants. Users of the Luxembourg Income Study Database and Luxembourg Wealth Study Database come from countries around the globe, including the UK. LIS has four goals: 1) to harmonise microdatasets from high- and middle-income countries that include data on income, wealth, employment, and demography; 2) to provide a secure method for researchers to query data that would otherwise be unavailable due to country-specific privacy restrictions; 3) to create and maintain a remote-execution system that sends research query results quickly back to users at off-site locations; and 4) to enable, facilitate, promote and conduct crossnational comparative research on the social and economic wellbeing of populations across countries. LIS contains the Luxembourg Income Study (LIS) Database, which includes income data, and the Luxembourg Wealth Study (LWS) Database, which focuses on wealth data. LIS currently includes microdata from 46 countries in Europe, the Americas, Africa, Asia and Australasia. LIS contains over 250 datasets, organised into eight time "waves," spanning the years 1968 to 2011. Since 2007, seventeen more countries have been added to LIS, including the BRICS countries (Brazil, Russia, India, China, South Africa), Japan, South Korea and a number of other Latin American countries. LWS contains 20 wealth datasets from 12 countries, including the UK, and covers the period 1994 to 2007. All told, LIS and LWS datasets together cover 86% of world GDP and 64% of world population. Users submit statistical queries to the microdatabases using a Java-based job submission interface or standard email. The databases are especially valuable for primary research in that they offer access to cross-national data at the micro-level - at the level of households and persons. Users are economists, sociologists, political scientists, and policy analysts, among others, and they employ a range of statistical approaches and methods. LIS also provides extensive documentation - metadata - for both LIS and LWS, concerning technical aspects of the survey data, the harmonisation process, and the social institutions of income and wealth provision in participating countries. In the next five years, for which support is sought, LIS will: - expand LIS, adding Waves IX (2013) and X (2016), and add new middle-income countries; - develop LWS, adding another wave of datasets to existing countries; acquire new wealth datasets for 14 more countries in cooperation with the European Central Bank (based on the Household Finance and Consumption Survey); - create a state-of-the-art metadata search and storage system; - maintain international standards in data security and data infrastructure systems; - provide high-quality harmonised household microdata to researchers around the world; - enable interdisciplinary cross-national social science research covering 45+ countries, including the UK; - aim to broaden its reach and impact in academic and non-academic circles through focused communications strategies and collaborations. All surveyed households and their members are included in our estimates of Gini and Atkinson coefficients, percentile ratios, and poverty lines. Poverty lines are calculated based on the total population. Those lines are then used to calculate poverty rates among subgroups (children and the elderly). Thus, when calculating poverty rates, the subgroups vary, but the poverty lines remain constant within any given dataset. The data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all LWS datasets in all waves (as of March 2022).

Asset and Wealth Management Market Outlook

The global market size for Asset and Wealth Management was valued at approximately USD 3.2 trillion in 2023 and is projected to reach USD 6.5 trillion by 2032, growing at a CAGR of 8.1% during the forecast period from 2024 to 2032. The growth of this market is primarily driven by the increasing need for sophisticated financial advisory services, rapid technological advancements, and a heightened focus on personalized investment strategies.

A significant growth factor in the Asset and Wealth Management market is the rising global wealth, particularly among high net worth individuals (HNWIs) and institutional investors. As economies worldwide continue to expand, the accumulation of wealth has necessitated advanced asset management solutions. This growth is further fueled by the increasing complexities in financial markets, which require sophisticated portfolio management and advisory services to optimize returns and mitigate risks. Additionally, the trend towards globalization has opened new investment opportunities and diversified portfolios, further driving the market's growth.

Technological advancements have also played a crucial role in the expansion of the Asset and Wealth Management market. The integration of Artificial Intelligence (AI), blockchain, and big data analytics into financial services has revolutionized the way wealth management firms operate. These technologies enhance decision-making processes, provide deep insights through predictive analytics, and ensure higher levels of security and transparency in transactions. As a result, firms are better equipped to offer personalized advice and innovative financial products, catering to the evolving demands of their clientele.

The growing demand for personalized investment strategies is another major growth driver for the Asset and Wealth Management market. Clients are increasingly seeking tailored financial plans that align with their specific goals, risk appetites, and investment horizons. Wealth management firms are responding by offering bespoke financial solutions, including customized portfolio management, estate planning, and tax optimization services. This trend is particularly prevalent among HNWIs and institutional investors who require a more hands-on approach to managing their assets effectively.

Regionally, North America holds a significant share of the Asset and Wealth Management market, primarily due to its mature financial industry, high concentration of wealth, and advanced technological infrastructure. Europe also represents a substantial market, driven by the presence of numerous financial institutions and favorable regulatory frameworks. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rising wealth in emerging economies like China and India, increasing financial literacy, and a burgeoning middle class. Latin America and the Middle East & Africa are also projected to experience steady growth, albeit at a slower pace, due to economic uncertainties and regulatory challenges.

Investment Management Analysis

Investment management forms a core component of the Asset and Wealth Management market, encompassing activities that help individuals and institutions manage their investment portfolios. This segment includes a wide array of services such as asset allocation, portfolio management, and performance measurement. The demand for investment management services is driven by the need for professional guidance in navigating the complexities of financial markets and achieving optimal returns. With increasing market volatility and the proliferation of financial instruments, clients are seeking expertise to manage their investments strategically.

Technological advancements have significantly impacted the investment management segment. The adoption of AI and machine learning algorithms allows wealth managers to analyze vast amounts of data and generate insights for making informed investment decisions. Robo-advisors, a product of these technologies, have made investment management services more accessible to a broader audience by offering cost-effective and automated portfolio management solutions. These digital platforms cater particularly to younger investors who prefer technology-driven investment options over traditional advisory services.

Another critical trend within investment management is the growing emphasis on Environmental, Social, and Governance (ESG) criteria. Investors are increasingly considering ESG factors as part of their decisio

Financial literacy of EU citizens. Topics: self-rated knowledge about financial matters compared to other adults in the own country; knowledge test: development of savings with a special interest rate over one year, development of the purchasing power of a special amount of money given a special inflation over one year, development of bond prices in case of rising interest rates, riskiness of investments with higher returns, riskiness of investments with a wide range of company shares; financial knowledge score; attitude towards the following statements: respondent carefully considers whether something is affordable before buying it, respondent keeps track and monitors own expenses, respondent sets long-term financial goals and strives to achieve them; financial behaviour score; overall financial literacy score; number of months being able to continue to cover own living expenses without borrowing any money or moving house in case of loss of main source of income; kind of financial products currently having or having had in the last two years: private pension or retirement product, life insurance, non-life insurance, mortgage or home loan, other consumer loan, investment product, crypto-securities, none of these; confidence with regard to having enough money to live comfortably throughout retirement years; comfort with using digital financial services; confidence in investment advice from bank / insurer / financial advisor. Demography: age; sex; nationality; responsible person for making day-to-day decisions about money in the household; highest completed level of full time education; ISCED level; household’s total income: awareness of weekly, monthly, yearly income; household´s total income per: week, month, year; age at end of education; occupation; professional position; type of community; household composition and household size. Additionally coded was: respondent ID; country; device used for interview; region; nation group; weighting factor. Finanzielle Bildung der EU-Bevölkerung. Themen: Selbsteinschätzung des Wissens über finanzielle Angelegenheiten im Vergleich zu anderen Erwachsenen im eigenen Land; Wissenstest: Wertentwicklung von Ersparnissen bei einem bestimmten Zinssatz über ein Jahr, Entwicklung der Kaufkraft eines bestimmten Betrags bei einer bestimmten Inflationsrate über ein Jahr, Entwicklung von Anleihepreisen bei steigenden Zinsen, Risiko von Investitionen mit höherer Rendite, Risiko von Investitionen mit einer breiten Palette von Unternehmensanteilen; Score Finanzielle Bildung; Einstellung zu den folgenden Aussagen: sorgfältiges Abwägen der Bezahlbarkeit vor der Anschaffung von Dingen, Überwachung der eigenen Ausgaben, Setzen langfristiger Finanzziele; Score Finanzverhalten; Gesamtscore Finanzielle Bildung; Anzahl der Monate, in denen man bei Verlust der Haupteinnahmequelle weiterhin den eigenen Lebensunterhalt bestreiten kann, ohne sich Geld leihen oder umziehen zu müssen; aktuell oder in den letzten zwei Jahren gehaltene Finanzprodukte: private Altersvorsorge oder Altersvorsorgeprodukt, Lebensversicherung, Nichtlebensversicherung, Hypothek oder Wohnungsbaudarlehen, anderes Verbraucherdarlehen, Investmentprodukt, Krypto-Wertpapiere, nichts davon; Zuversicht im Hinblick auf ausreichende finanzielle Mittel in der Rentenzeit; Unbehagen bei der Nutzung digitaler finanzieller Dienstleistungen; Vertrauen in Ratschlägen zu Geldanlagen von Bank / Versicherer / Finanzberater. Demographie: Alter; Geschlecht; Staatsangehörigkeit; verantwortliche Person für alltägliche Entscheidungen über Geld im Haushalt; höchster Bildungsabschluss; ISCED-Level; Haushaltsgesamteinkommen: Kenntnis des wöchentlichen, monatlichen, jährlichen Einkommens; Haushaltsgesamteinkommen pro: Woche, Monat, Jahr; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Urbanisierungsgrad; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Befragten-ID; Land; für das Interview genutztes Gerät; Region; Nationengruppe; Gewichtungsfaktor.

The World Top Incomes Database provides statistical information on the shares of top income groups for 30 countries. The construction of this database was possible thanks to the research of over thirty contributing authors. There has been a marked revival of interest in the study of the distribution of top incomes using tax data. Beginning with the research by Thomas Piketty of the long-run distribution of top incomes in France, a succession of studies has constructed top income share time series over the long-run for more than twenty countries to date. These projects have generated a large volume of data, which are intended as a research resource for further analysis. In using data from income tax records, these studies use similar sources and methods as the pioneering work by Kuznets for the United States.The findings of recent research are of added interest, since the new data provide estimates covering nearly all of the twentieth century -a length of time series unusual in economics. In contrast to existing international databases, generally restricted to the post-1970 or post-1980 period, the top income data cover a much longer period, which is important because structural changes in income and wealth distributions often span several decades. The data series is fairly homogenous across countries, annual, long-run, and broken down by income source for several cases. Users should be aware also about their limitations. Firstly, the series measure only top income shares and hence are silent on how inequality evolves elsewhere in the distribution. Secondly, the series are largely concerned with gross incomes before tax. Thirdly, the definition of income and the unit of observation (the individual vs. the family) vary across countries making comparability of levels across countries more difficult. Even within a country, there are breaks in comparability that arise because of changes in tax legislation affecting the definition of income, although most studies try to correct for such changes to create homogenous series. Finally and perhaps most important, the series might be biased because of tax avoidance and tax evasion. The first theme of the research programme is the assembly and analysis of historical evidence from fiscal records on the long-run development of economic inequality. “Long run” is a relative term, and here it means evidence dating back before the Second World War, and extending where possible back into the nineteenth century. The time span is determined by the sources used, which are based on taxes on incomes, earnings, wealth and estates. Perspective on current concerns is provided by the past, but also by comparison with other countries. The second theme of the research programme is that of cross-country comparisons. The research is not limited to OECD countries and will draw on evidence globally. In order to understand the drivers of inequality, it is necessary to consider the sources of economic advantage. The third theme is the analysis of the sources of income, considering separately the roles of earned incomes and property income, and examining the historical and comparative evolution of earned and property income, and their joint distribution. The fourth theme is the long-run trend in the distribution of wealth and its transmission through inheritance. Here again there are rich fiscal data on the passing of estates at death. The top income share series are constructed, in most of the cases presented in this database, using tax statistics (China is an exception; for the time being the estimates come from households surveys). The use of tax data is often regarded by economists with considerable disbelief. These doubts are well justified for at least two reasons. The first is that tax data are collected as part of an administrative process, which is not tailored to the scientists' needs, so that the definition of income, income unit, etc., are not necessarily those that we would have chosen. This causes particular difficulties for comparisons across countries, but also for time-series analysis where there have been substantial changes in the tax system, such as the moves to and from the joint taxation of couples. Secondly, it is obvious that those paying tax have a financial incentive to present their affairs in a way that reduces tax liabilities. There is tax avoidance and tax evasion. The rich, in particular, have a strong incentive to understate their taxable incomes. Those with wealth take steps to ensure that the return comes in the form of asset appreciation, typically taxed at lower rates or not at all. Those with high salaries seek to ensure that part of their remuneration comes in forms, such as fringe benefits or stock-options which receive favorable tax treatment. Both groups may make use of tax havens that allow income to be moved beyond the reach of the national tax net. These shortcomings limit what can be said from tax data, but this does not mean that the data are worthless. Like all economic data, they measure with error the 'true' variable in which we are interested. References Atkinson, Anthony B. and Thomas Piketty (2007). Top Incomes over the Twentieth Century: A Contrast between Continental European and English-Speaking Countries (Volume 1). Oxford: Oxford University Press, 585 pp. Atkinson, Anthony B. and Thomas Piketty (2010). Top Incomes over the Twentieth Century: A Global Perspective (Volume 2). Oxford: Oxford University Press, 776 pp. Atkinson, Anthony B., Thomas Piketty and Emmanuel Saez (2011). Top Incomes in the Long Run of History, Journal of Economic Literature, 49(1), pp. 3-71. Kuznets, Simon (1953). Shares of Upper Income Groups in Income and Savings. New York: National Bureau of Economic Research, 707 pp. Piketty, Thomas (2001). Les Hauts Revenus en France au 20ème siècle. Paris: Grasset, 807 pp. Piketty, Thomas (2003). Income Inequality in France, 1901-1998, Journal of Political Economy, 111(5), pp. 1004-42.

The investment advisory services market is experiencing robust growth, driven by increasing individual wealth, a rising demand for personalized financial planning, and the complexity of modern investment instruments. The market's compound annual growth rate (CAGR) is estimated to be around 8%, resulting in a market size of approximately $150 billion in 2025. This growth is further fueled by technological advancements, such as robo-advisors and sophisticated financial planning software, which are making investment advice more accessible and affordable. However, regulatory changes and increasing competition among established players and new fintech entrants represent significant challenges. Market segmentation reveals strong growth in both high-net-worth individual services and digitally-driven mass-market solutions. The continued expansion of digital platforms is likely to attract a younger demographic and drive future growth. Leading players, including Investor Advisory Service, Tilney, Fidelity, and UBS, are leveraging their brand recognition and expertise to maintain market share, while smaller firms are focusing on niche market segments and innovative service offerings to compete effectively. The forecast period of 2025-2033 predicts continued expansion, with projections indicating a substantial increase in market value driven by factors like global economic growth and increasing financial literacy. Regional variations in market penetration are expected, with North America and Europe maintaining significant shares due to established financial infrastructure and high levels of individual wealth. However, emerging markets in Asia and Latin America are poised for rapid growth as financial sophistication increases and a burgeoning middle class seeks professional investment advice. The competitive landscape is dynamic, with mergers and acquisitions playing a significant role in shaping the market structure. Companies are constantly adapting their strategies to meet the evolving needs of investors, which include sustainable and impact investing, alongside traditional investment approaches. Maintaining client trust and adapting to technological advancements remain crucial for success in this evolving market.