******* of the ** million U.S. dollar millionaires worldwide in 2022 had a wealth of more than ** million U.S. dollars. The vast majority of the global millionaires had a wealth between *** and **** million U.S. dollars.

Iceland was the country with the highest rate of millionaires worldwide in 2022, with more than ***percent of the adult population owning assets worth more than one million U.S. dollars. Luxembourg followed behind with ** percent of the population being millionaires, with Switzerland in third.

In 2024, there were nearly 24 million people with a net worth of over one million U.S. dollars in the United States, which put the country on the top of the ranking. China was ranked second in that year, with more than six million individuals with wealth exceeding one million U.S. dollars. France followed in third with around three million millionaires.

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.

Through 2022 and 2023, inflation rates rose worldwide as a result of the COVID-19 pandemic and the Russian invasion of Ukraine in *************. This also had an impact on millionaires worldwide, with over **** million millionaires in the United States when using a nominal U.S. dollar, but **** when adjusting for inflation.

1) Data Introduction • The Billionaires Statistics Dataset (2023) is a comprehensive set of personal and business information, including rankings of billionaires worldwide, net assets, industries, businesses, nationalities, birth and residence information, and asset sources.

2) Data Utilization (1) Billionaires Statistics Dataset (2023) has characteristics that: • The dataset consists of more than 35 columns, including the billionaire's rank, final Worth, industry, country, age, country of residence, source of assets, related industries, citizenship, organization, selfMade, birth information, data collection date, economic and social indicators (GDP, CPI, education enrollment, life expectancy, tax revenue, population, etc.). • In addition to individual asset information, economic indicators and demographic data by country are combined, allowing a three-dimensional analysis of billionaires and each country's economic and social environment. (2) Billionaires Statistics Dataset (2023) can be used to: • Wealth Distribution and Industry Analysis: Using billionaires' net worth, industry, and national data, we can analyze global wealth concentration and wealth distribution by industry and region. • A study linking demographics and economic indicators: Billionaire data can be combined with various economic and social indicators such as GDP, CPI, tax revenue, education, and life expectancy to be used for in-depth research on wealth formation, social background, ratio of self-made and inherited wealth, and regional characteristics.

The statistic shows the top 20 cities according to the number of millionaires in each in 2012. In 2012, there were around ******* millionaires living in Tokyo in Japan.

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.

The dataset presents a glimpse into the elite echelons of global wealth, featuring prominent billionaires from diverse backgrounds and industries. At the helm is Bernard Arnault, a French titan in the consumer sector, followed closely by Jeff Bezos and Elon Musk, trailblazers in the technology realm hailing from the United States. These innovators, alongside luminaries like Mark Zuckerberg, Bill Gates, and Larry Page, have reshaped industries, propelled technological advancements, and amassed staggering fortunes. Notably, Warren Buffett, Larry Ellison, and Michael Dell represent the seasoned veterans of the tech and investment spheres, each leaving an indelible mark on their respective domains.

Diverse geographical representation is evident, with Mukesh Ambani from India and Carlos Slim from Mexico showcasing global wealth distribution. Ambani's influence extends across energy and telecommunications, while Slim's conglomerate spans telecommunications, real estate, and healthcare. Gautam Adani represents India's industrial prowess, steering his conglomerate through energy and infrastructure sectors.

This dataset underscores the intersection of wealth, innovation, and entrepreneurship, reflecting the evolving landscape of global capitalism. From disruptive tech visionaries to seasoned investors and industrialists, these billionaires symbolize the dynamism and influence of economic elites in shaping our world.

According to the Hurun Global Rich List 2025, the United States housed the highest number of billionaires worldwide in 2025. In detail, there were *** billionaires living in the United States as of January that year. By comparison, *** billionaires resided in China. India, the United Kingdom, and Germany were also the homes of a significant number of billionaires that year. United States has regained its first place As the founder and exporter of consumer capitalism, it is no surprise that the United States is home to a large number of billionaires. Although China had briefly overtaken the U.S. recently, the United States has reclaimed its position as the country with the most billionaires in the world. Moreover, North America leads the way in terms of the highest number of ultra high net worth individuals – those with a net worth of more than ***** million U.S. dollars. The prominence of Europe and North America is a reflection of the higher degree of economic development in those states. However, this may also change as China and other emerging economies continue developing. Female billionaires Moreover, the small proportion of female billionaires does little to counter critics claiming the global economy is dominated by an elite comprised mainly of men. On the list of the richest people in the world, only *** were women. Moreover, recent political discourse has put a great amount of attention on the wealth held by the super-rich, with the wealth distribution of the global population being heavily unequal.

Context

This dataset contains a list of around 3000 billionaires in the world and their basic information available on Wikipedia.

Content

The dataset contains name, nationality, date of birth, and occupation of the Billionaires.

Acknowledgements

The dataset is scrapped from Wikipedia using DBpedia resource with the help of SPARQL querying.

Inspiration

The major inspiration behind this data is to understand the distribution demographics of wealth in the world.

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 🙂❤️

I gathered data from Forbes.com on the world's richest people: this data includes their net worth, rank, and the main field for this value, as well as their countries.

Rank: The position on the list of the world's wealthiest people. Name: Their given name or the name of the family to which they belong. Net Worth: Their present net worth is measured in billions of dollars. Change: the change in their net worth from the previous day, Age: This is their current age. Source: The primary source of their financial worth. Country/Territory: the location where they now reside.

This comprehensive dataset encapsulates a detailed snapshot of the wealthiest individuals globally, as listed by Forbes in 2024. Compiled through meticulous web scraping and data aggregation, the dataset includes a wide range of attributes for each billionaire. Fields encompass basic personal information such as name, age, and gender, alongside financial details including net worth and sources of wealth. The dataset further delves into aspects like industry involvement, organizational affiliations, philanthropic endeavors, and educational backgrounds.

Key attributes in this dataset include:

Name: Full legal name of the billionaire. Age: Age of the individual. 2024 Net Worth: Estimated net worth in USD for the year 2024. Industry: Primary industry or sector of operation. Source of Wealth: Origin of the billionaire’s wealth. Title: Professional title or position. Organization: Name of the associated organization. Self-Made: Indicator if the wealth is self-made. Self-Made Score: A quantitative score assessing how self-made their wealth is. Philanthropy Score: A score reflecting the extent of their philanthropic activities. Residence: Main residence of the individual. Citizenship: Legal citizenship. Gender: Gender identity. Marital Status: Current marital status. Children: Number of children. Education: Highest level of education attained.

This dataset is ideal for analysis, offering insights into the distribution of wealth, the influence of education on wealth accumulation, and trends across different industries. It also provides a foundation for exploring the impact of socioeconomic factors on personal wealth. The data were collected and formatted with careful consideration to ensure accuracy, making it a valuable resource for researchers, economists, and anyone interested in the dynamics of wealth and success.

Please note that some data is missing in this dataset, primarily due to the unavailability of information from Forbes. This issue becomes more prevalent beyond the top 400 entries. Many individuals lack a self-made score, a philanthropy score, or specific details regarding their title or organization as per Forbes' listings. I am currently working to update the dataset with this missing information. However, this update process is quite tedious and time-consuming since it is mostly manual. I appreciate your patience and understanding as I work through these details.

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.

In 2024, North America had the highest number of high-net-worth individuals, with 8.45 million HNWIs living there. In Asia Pacific, there were also 7.59 billion HNWIs registered in 2024. High-net-worth individuals (HNWIs) are people with wealth exceeding one million U.S. dollars.

Graph and download economic data for Net Worth Held by the Top 0.1% (99.9th to 100th Wealth Percentiles) (WFRBLTP1246) from Q3 1989 to Q2 2025 about net worth, wealth, percentile, Net, and USA.

In 2023, roughly 1.49 billion adults worldwide had a net worth of less than 10,000 U.S. dollars. By comparison, 58 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 2023, nearly 1,700 billionaires had a total wealth between one to two billion U.S. dollars. Wealth worldwide China had the highest number of billionaires in 2023, with the United States following behind. That same year, New York had the most billionaires worldwide.

Bloomberg Billionaires Index View profiles for each of the world’s 500 richest people, see the biggest movers, and compare fortunes or track returns. As of December 12, 2024 The Bloomberg Billionaires Index is a daily ranking of the world’s richest people. Details about the calculations are provided in the net worth analysis on each billionaire’s profile page. The figures are updated at the close of every trading day in New York. Rank Name Total net worth $ Last change $ YTD change Country / Region

Content

This dataset consists of top most billionaires in the world and respective their names, whether it is a finance company or any software company, how much money they have ,these all the details which are in the dataset

Researchers have compiled a multi-decade database of the super-rich. Building off the Forbes World’s Billionaires lists from 1996-2014, scholars at Peterson Institute for International Economics have added a couple dozen more variables about each billionaire - including whether they were self-made or inherited their wealth. (Roughly half of European billionaires and one-third of U.S. billionaires got a significant financial boost from family, the authors estimate.)

Acknowledgements

Reference : https://corgis-edu.github.io/corgis/csv/billionaires/

Inspiration

Some of the datasets which I have seen in the kaggle or somewhere but it is limited to less number of columns . Kagglers are not able to get an insights from very low amount of data. so that I decided that to be more helpful to them or we can able to get an more insights from this dataset