In 2024, roughly 1.55 billion adults worldwide had a net worth of less than 10,000 U.S. dollars. By comparison, 60 million adults had a net worth of more than one million U.S. dollars in the same year. Wealth distribution The distribution of wealth is an indicator of economic inequality. The United Nations says that wealth includes the sum of natural, human, and physical assets. Wealth is not synonymous with income, however, because having a large income can be depleted if one has significant expenses. In 2024, nearly 1,770 billionaires had a total wealth between one and two billion U.S. dollars. Wealth worldwide The United States had the highest number of billionaires in 2025, followed by China. That same year, New York had the most billionaires worldwide.

Graph and download economic data for Share of Net Worth Held by the Top 1% (99th to 100th Wealth Percentiles) (WFRBST01134) from Q3 1989 to Q3 2025 about net worth, wealth, percentile, Net, and USA.

The world's richest 10 percent holds more than three quarters of the world's total wealth. Although their share decreased by around five percentage points since 1995, this underlines the massive wealth inequalities existing around the world. By comparison, the poorest half of the world population holds less than two percent of global wealth. The richest percent holds more than 40 percent of the global wealth.

In 2024, Central Asia was the region 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.

Explore the dynamic landscape of global wealth with our meticulously curated dataset sourced from the Forbes Billionaires List. Delve into the lives and fortunes of the individuals, uncovering key insights into their net worth, age, country or territory of origin, primary sources of wealth, and respective industries. This dataset, meticulously web scraped from Forbes, provides a comprehensive snapshot of the world's financial elite, offering a unique lens into the diverse sectors that contribute to their staggering fortunes. From tech moguls to fashion tycoons, this dataset presents a detailed panorama of the wealthiest personalities shaping the global economic stage.

This feature shows the global wealth distribution for the years 1995, 2000, and 2005. Feature published and hosted by Esri Canada © 2013. Content Sources: Countries, Esri Maps and DataThe World Bank, The Changing Wealth of Nations: http://data.worldbank.org/data-catalog/wealth-of-nations Coordinate System: Web Mercator Auxiliary Sphere (WKID 102100) Update Frequency: As Required Publication Date: October 2013 OECD stands for Organisation for Economic Co-operation and Development and is a global organization created to "promote policies that will improve the economic and social well-being of people around the world".

This dataset provides a synthetic overview of the 1,000 wealthiest individuals in the world, offering insights into the distribution of wealth across industries and regions. It is designed to help analysts, researchers, and data enthusiasts explore global wealth trends, industry dominance, and regional wealth concentration.

Whether you're conducting market research, financial analysis, or data modeling, this dataset serves as a valuable resource for understanding the characteristics of the world's top billionaires.

📊 Key Features: Name 👤: The name of the billionaire. Country 🌍: Country of residence or primary business operation. Industry 🏭: Industry in which the individual has built their wealth. Net Worth (in billions) 💵: Estimated net worth in billions of USD. Company 🏢: The primary company or business associated with the billionaire. ⚠️ Important Note: This dataset is 100% synthetic and does not contain real financial or personal data. It is artificially generated for educational, analytical, and research purposes.

Certainly! Here's a description of each column:

Rank: The numerical ranking of a person or entity in a list or category.

finalWorth: The final worth or net worth of a person or entity, typically in terms of monetary value.

category: The category or classification of a person or entity, such as "entrepreneur", "investor", "celebrity", etc.

personName: The name of a person.

age: The age of a person.

country: The country of residence or origin of a person or entity.

city: The city of residence or origin of a person or entity.

source: The source or origin of wealth or fame for a person or entity.

industries: The industries or sectors in which a person or entity operates or is associated with.

countryOfCitizenship: The country of citizenship of a person.

organization: The organization or company with which a person is associated.

selfMade: Indicates whether a person is self-made or inherited wealth/fame.

**status: **The status or position of a person or entity, such as "CEO", "founder", "chairman", etc.

gender: The gender of a person.

**birthDate: **The date of birth of a person.

lastName: The last name or surname of a person.

**firstName: **The first name of a person.

title: The title or honorific used for a person, such as "Mr.", "Mrs.", "Dr.", etc.

date: The date associated with a particular event or data entry.

**state: **The state or region of residence or origin of a person or entity.

residenceStateRegion: The state or region of residence of a person or entity.

birthYear: The year of birth of a person.

birthMonth: The month of birth of a person.

**birthDay: **The day of birth of a person.

**cpi_country: **Consumer Price Index (CPI) for a specific country.

cpi_change_country: Change in Consumer Price Index (CPI) for a specific country.

**gdp_country: **Gross Domestic Product (GDP) for a specific country.

**gross_tertiary_education_enrollment: **Gross tertiary education enrollment rate for a specific country.

gross_primary_education_enrollment_country: Gross primary education enrollment rate for a specific country.

**life_expectancy_country: **Life expectancy for a specific country.

tax_revenue_country_country: Tax revenue for a specific country.

**total_tax_rate_country: **Total tax rate for a specific country.

population_country: Population of a specific country.

**latitude_country: **Latitude coordinates of a specific country.

**longitude_country: **Longitude coordinates of a specific country.

These columns appear to contain various attributes and metrics related to individuals, countries, and economic indicators.

This Web map shows the global wealth distribution for the years 1995, 2000, and 2005. Web map published and hosted by Esri Canada © 2013. Content Sources: Countries, Esri Maps and DataThe World Bank, The Changing Wealth of Nations: http://data.worldbank.org/data-catalog/wealth-of-nations Coordinate System: Web Mercator Auxiliary Sphere (WKID 102100) Update Frequency: As Required Publication Date: October 2013 OECD stands for Organisation for Economic Co-operation and Development and is a global organization created to "promote policies that will improve the economic and social well-being of people around the world".

This dataset offers a comprehensive overview of the wealthiest individuals in the world, spanning nearly three decades from 1996 to 2024. It provides detailed information on the net worth, ranking, and other relevant data of the richest people globally, capturing their financial journeys over the years. Whether you are a researcher, data enthusiast, or just curious about the evolution of global wealth, this dataset serves as a valuable resource for analyzing trends, understanding economic shifts, and exploring the factors contributing to the rise and fall of the world's wealthiest individuals.

The world's wealthiest individuals by net worth.

This dataset provides a comprehensive overview of global wealth distribution in 2021. It includes data on total wealth, GDP per adult, wealth per adult, and the share of world wealth for various countries. This data can be used for various analyses, such as studying wealth inequality, economic development, and global financial trends.

This dataset offers a comprehensive look at the billionaires in the world from the year 2002 to the present day, amidst the backdrop of widespread layoffs and economic turmoil.

The data includes information such as: python - Name - Last Name - Age - Gender - Permanent Residence Country - Company - Main Industry - Source of Wealth, and more. The dataset provides insights into the demographic and successes of the world's wealthiest individuals. It can be useful for understanding on how some have managed to thrive during difficult economic times. On the other side, it can also be used to explore the widening inequality gaps worldwide.

Potential use cases: 1. Predict future growth of wealth globally among the wealthiest individuals. 2. Visualize global wealth trends across industries. 3. Cluster individuals based on their demographics, geographical location and/or wealth sources. 4. Predict inequality gap between wealthiest and poorest individuals (if used with poverty datasets).

Description

This dataset contains statistics on the world's billionaires, including information about their businesses, industries, and personal details. It provides insights into the wealth distribution, business sectors, and demographics of billionaires worldwide.

Key Features

• rank: The ranking of the billionaire in terms of wealth.
• finalWorth: The final net worth of the billionaire in U.S. dollars.
• category: The category or industry in which the billionaire's business operates.
• personName: The full name of the billionaire.
• age: The age of the billionaire.
• country: The country in which the billionaire resides.
• city: The city in which the billionaire resides.
• source: The source of the billionaire's wealth.
• industries: The industries associated with the billionaire's business interests.
• countryOfCitizenship: The country of citizenship of the billionaire.
• organization: The name of the organization or company associated with the billionaire.
• selfMade: Indicates whether the billionaire is self-made (True/False).
• status: "D" represents self-made billionaires (Founders/Entrepreneurs) and "U" indicates inherited or unearned wealth.
• gender: The gender of the billionaire.
• birthDate: The birthdate of the billionaire.
• lastName: The last name of the billionaire.
• firstName: The first name of the billionaire.
• title: The title or honorific of the billionaire.
• date: The date of data collection.
• state: The state in which the billionaire resides.
• residenceStateRegion: The region or state of residence of the billionaire.
• birthYear: The birth year of the billionaire.
• birthMonth: The birth month of the billionaire.
• birthDay: The birth day of the billionaire.
• cpi_country: Consumer Price Index (CPI) for the billionaire's country.
• cpi_change_country: CPI change for the billionaire's country.
• gdp_country: Gross Domestic Product (GDP) for the billionaire's country.
• gross_tertiary_education_enrollment: Enrollment in tertiary education in the billionaire's country.
• gross_primary_education_enrollment_country: Enrollment in primary education in the billionaire's country.
• life_expectancy_country: Life expectancy in the billionaire's country.
• tax_revenue_country_country: Tax revenue in the billionaire's country.
• total_tax_rate_country: Total tax rate in the billionaire's country.
• population_country: Population of the billionaire's country.
• latitude_country: Latitude coordinate of the billionaire's country.
• longitude_country: Longitude coordinate of the billionaire's country.

Potential Use Cases

• Wealth distribution analysis: Explore the distribution of billionaires' wealth across different industries, countries, and regions.
• Demographic analysis: Investigate the age, gender, and birthplace demographics of billionaires.
• Self-made vs. inherited wealth: Analyze the proportion of self-made billionaires and those who inherited their wealth.
• Economic indicators: Study correlations between billionaire wealth and economic indicators such as GDP, CPI, and tax rates.
• Geospatial analysis: Visualize the geographical distribution of billionaires and their wealth on a map.
• Trends over time: Track changes in billionaire demographics and wealth over the years.

If this was helpful, a vote is appreciated 🙂❤️

The level of global financial assets was expected to increase from ***** trillion U.S. dollars in 2023 to roughly *** trillion U.S. dollars by 2028. The United States is forecast to make up the largest portion of this global wealth, with the Asia-Pacific ranking ******.

The World Inequality Database (WID.world) aims to provide open and convenient access to the most extensive available database on the historical evolution of the world distribution of income and wealth, both within countries and between countries.

HISTORY OF WID.WORLD During the past fifteen years, the renewed interest for the long-run evolution of income and wealth inequality gave rise to a flourishing literature. In particular, a succession of studies has constructed top income share series for a large number of countries (see Thomas Piketty 2001, 2003, T. Piketty and Emmanuel Saez 2003, and the two multi-country volumes on top incomes edited by Anthony B. Atkinson and T. Piketty 2007, 2010; see also A. B. Atkinson et al. 2011 and Facundo Alvaredo et al. 2013 for surveys of this literature). These projects generated a large volume of data, intended as a research resource for further analysis, as well as a source to inform the public debate on income inequality. To a large extent, this literature follows the pioneering work of Simon Kuznets 1953, and A. B. Atkinson and Alan Harrison 1978, and extends it to many more countries and years.

for more https://wid.world/wid-world/

GDP, GDP per capita, and PPP rankings for the world's wealthiest nations with historical data and 2030 projections.

Description:

The "Billionaires_2023_Dataset" is a meticulously curated and up-to-date compilation of data on the world's wealthiest individuals as of the year 2023, sourced directly from Forbes.com. This dataset offers comprehensive insights into the financial, demographic, and professional characteristics of billionaires, making it an invaluable resource for researchers, analysts, journalists, and data enthusiasts. Explore the economic trends, wealth disparities, and entrepreneurial achievements of the globe's most affluent individuals with this dataset.

Contents:

Rank: The ranking of billionaires based on their net Name: The full name of each Net Worth: Estimated net worth in billions of Age: Age of each billionaire at the time of data Country/Territory: The location associated with each billionaire's residence or business Source: The primary source of wealth for each individual . Industry: The specific industry or sector linked to the source of wealth .

How to Use:

• Data Analysis: Perform in-depth analysis to uncover trends and patterns in billionaire demographics, wealth distribution, and industry concentrations.
• Visualizations: Create visualizations such as charts, graphs, and maps to represent the data visually.
• Research: Utilize the dataset for academic research, market analysis, or investigative journalism.
• Comparative Studies: Compare the 2023 dataset with previous years to observe changes in billionaire profiles and wealth distribution.
• Data Enrichment: Enhance your own datasets or research projects with demographic and financial data from this dataset.

Important Notes:

• Data Source: The data has been sourced directly from Forbes.com, a trusted and reputable platform for billionaire rankings and wealth estimates.
• Updates: The dataset may be periodically updated to reflect changes in billionaire rankings and net worth estimates.
• Privacy and Ethical Considerations: Respect privacy and ethical considerations when using the data, and avoid using it for harmful purposes.

In 2023, 225,000 individuals with net assets of at least 30 million U.S. dollars were residing in the United States, by far the highest number of any country. By comparison, China, which had the second highest number of ultra high net worth individuals (UHNWIs), had less than 100,000 individuals with assets amounting to 30 million U.S. dollars or more.Place of residence of ultra high net worth individuals The residency of almost half of the world’s ultra high net worth individuals in the United States explains the dominance of North America in regard to the number of ultra high net worth individuals by region. Hong Kong was the city with the most UHNWIs in 2022, followed by New York, London, and Los Angeles. Source of wealth and gender differences A majority of the world's UHNWIs are self-made. However, looking at billionaires, there is a clear difference between men and women; whereas a majority of billionaire men were self-made, a majority of the women had inherited their fortune.

Almost universally, wealth is not distributed uniformly within societies or economies. Even though wealth data have been collected in various forms for centuries, the origins for the observed wealth-disparity and social inequality are not yet fully understood. Especially the impact and connections of human behavior on wealth could so far not be inferred from data. Here we study wealth data from the virtual economy of the massive multiplayer online game (MMOG) Pardus. This data not only contains every player's wealth at every point in time, but also all actions over a timespan of almost a decade. We find that wealth distributions in the virtual world are very similar to those in Western countries. In particular we find an approximate exponential distribution for low wealth levels and a power-law tail for high levels. The Gini index is found to be , which is close to the indices of many Western countries. We find that wealth-increase rates depend on the time when players entered the game. Players that entered the game early on tend to have remarkably higher wealth-increase rates than those who joined later. Studying the players' positions within their social networks, we find that the local position in the trade network is most relevant for wealth. Wealthy people have high in- and out-degrees in the trade network, relatively low nearest-neighbor degrees, and low clustering coefficients. Wealthy players have many mutual friendships and are socially well respected by others, but spend more time on business than on socializing. Wealthy players have few personal enemies, but show animosity towards players that behave as public enemies. We find that players that are not organized within social groups are significantly poorer on average. We observe that “political” status and wealth go hand in hand.