Dataset Card for 100 Richest People In World

  Dataset Summary

This dataset contains the list of Top 100 Richest People in the World Column Information:-

Name - Person Name NetWorth - His/Her Networth Age - Person Age Country - The country person belongs to Source - Information Source Industry - Expertise Domain

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[More Information Needed]… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/100-richest-people-in-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.

As of March 2025, Elon Musk had a net worth valued at 328.5 billion U.S. dollars, making him the richest man in the world. Amazon founder Jeff Bezos followed in second, with Marc Zuckerberg, the founder of Facebook, in third. The list is dominated by Americans, and Alice Walton and Francoise Bettencourt Meyers are the only women among the 20 richest people worldwide.

Welcome to the Forbes Billionaire List Dataset! 🌟

Context: This comprehensive dataset presents a wealth of information about the world's billionaires, curated from the prestigious Forbes Billionaires List. The Forbes list is widely recognized as a reliable source for tracking the net worth and profiles of the wealthiest individuals globally. It provides valuable insights into the distribution of wealth, entrepreneurial success stories, and the industries and countries where billionaires thrive.

Inspiration: This dataset was inspired by a desire to analyze and explore the fascinating world of billionaires. It provides enthusiasts with a rich resource to study wealth distribution patterns, demographic trends, entrepreneurial endeavors, and global economic landscapes. By examining the Forbes Billionaires List, we can gain valuable insights into the factors that contribute to extreme wealth and the industries driving economic growth.

Potential Applications: The Forbes Billionaire List Dataset offers numerous avenues for analysis and exploration. Here are a few potential applications: - Analyzing wealth distribution across countries and industries - Studying the relationship between age and net worth of billionaires - Identifying the top sources of wealth and the most successful industries - Exploring the demographic characteristics of billionaires - Examining the economic impact of billionaires on specific countries or regions

Source: https://www.forbes.com/billionaires/

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.

Topics: Social background and good relations as most important prerequisites for success in the society; most important criteria for social mobility (scale: personal effort, intelligence or corruption); reasons for and acceptance of social inequality; Self-assessment of payment suitable for performance; estimation of actual and adequate monthly income for occupational groups; responsibility of government to reduce income differences; attitude to a progressive tax rate; assessment of the economic differences between poor and rich countries; attitude towards compensation by additional taxes in the wealthy countries (Redistribution); justification of better medical supply and better education for people with higher income; assumption of conflicts between social groups in the country; self-assessment on a top-bottom-scale and expectation of the individual level in 10 years; social mobility; criteria for the classification of payment for work (scale: responsibility, education, supervisor function, needed support for family and children or quality of job performance); feeling of a just payment; characterisation of the actual and the desired social system of the country, measured by classification on pyramid diagrams; Self-assessment of the respondent as well as classification of an unskilled factory worker and a chairman of a large corporation on a top-bottom-scale; number of books in the parental home in the respondent’s youth.

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at GESIS at https://doi.org/10.4232/1.3430. We highly recommend using the GESIS version as they have made this dataset available in multiple data formats.

A dataset listing Illinois counties by population for 2024.

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

Dataset

This dataset was created by Ravi_kr

Contents

Introduction: The "Forbes Billionaires Evolution" dataset provides a comprehensive examination of the financial growth and status of global billionaires from 1997 to 2024. It meticulously chronicles the transformations, both subtle and substantial, in the fortunes of these financial titans over nearly three decades.

The cover image used for this dataset is sourced from Forbes, with the illustration credited to Geoff J. Kim for Forbes.

Content: This dataset offers a plethora of valuable information, including: - Yearly rankings. - Net worth details and evolution. - Personal details like names, age, gender, and birth dates. - Residence details spanning country, city, and citizenship. - In-depth business category and industry classifications. - Details about affiliations with various organizations, including positions held. - Indicators like "self-made" and the current wealth status.

Use Cases: The potential applications for this dataset are vast and varied: 1. Financial Analysis: Understand the wealth patterns and trajectories of the world's richest individuals. 2. Sociological Studies: Examine the dynamics of wealth in relation to factors like age, gender, and nationality. 3. Business Strategy: Identify industries and regions with the most billionaire activity and growth. 4. Academic Research: Serve as a foundational resource for theses, dissertations, or coursework related to finance, business, or socio-economics. 5. Journalism: Craft compelling stories around the ascent or decline of billionaires in different epochs.

Leverage this dataset to extract trends, narratives, and crucial data-driven insights into the financial landscape of our world's most affluent individuals.

COVID-19 in Muslim vs. Non-Muslim Countries

A Comparative Study of 50 Muslim-Majority and 50 Richest Non-Muslim Countries

About Dataset

This dataset contains information on COVID-19 cases and deaths in 50 Muslim-majority countries compared to the 50 richest non-Muslim countries. The aim of the dataset is to investigate the differences in COVID-19 incidence between these two groups and to explore potential reasons for these disparities. The Muslim-majority countries in the sample had more than 50.0% Muslims, while the non-Muslim countries were selected based on their GDP, excluding any Muslim-majority countries listed. The data was collected on September 18, 2020, and includes information on the percentage of Muslim population per country, GDP, population count, and total number of COVID-19 cases and deaths. The dataset was transferred via an Excel spreadsheet on September 23, 2020 and analyzed using three different Average Treatment Methods (ATE) to validate the results. The dataset was published as a preprint and is associated with a manuscript titled "Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countries". The manuscript can be accessed via the following Link The sources of the data are also provided in the manuscript. The percentage of Muslim population per country was obtained from World Population Review and can be accessed at Link The GDP per country, population count, and total number of COVID-19 cases and deaths were obtained from Worldometers and can be accessed at Link

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Columns

Research Ideas / Data Use

• Comparative analysis: Researchers can use this dataset to compare the COVID-19 cases and deaths between Muslim-majority and non-Muslim countries. This can help to identify any disparities or differences in the response to the pandemic.
• Trend analysis: Over time, this dataset can be used to track the changes in the COVID-19 cases and deaths in Muslim-majority and non-Muslim countries. This can help to identify trends and patterns that may inform future research.
• Geographical analysis: This dataset can be used to explore the geographical distribution of COVID-19 cases and deaths in Muslim-majority and non-Muslim countries. This can help to identify hotspots and areas that may require special attention.
• Demographic analysis: Researchers can use the data to explore the impact of demographic factors on the spread and severity of the pandemic in Muslim-majority and non-Muslim countries. This can help to identify any patterns or correlations that may inform future research and policy decisions.
• Economic analysis: The data can be used to explore the economic impact of the pandemic on Muslim-majority and non-Muslim countries. By comparing the GDP and other economic indicators in these countries, researchers can identify any patterns or trends that may inform economic policy decisions.

Acknowledgements

if this dataset was used in your work or studies, please credit the original source Please Credit ↑ ⠀

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. More Information

Coverage of Social Insurance Programs in Richest Quintile

Percent of Population Eligible

By data.world's Admin [source]

About this dataset

This dataset offers a unique insight into the coverage of social insurance programs for the wealthiest quintile of populations around the world. It reveals how many individuals in each country are receiving support from old age contributory pensions, disability benefits, and social security and health insurance benefits such as occupational injury benefits, paid sick leave, maternity leave, and more. This data provides an invaluable resource to understand the health and well-being of those most financially privileged in society – often having greater impact on decision making than other groups. With up-to-date figures from 2019-05-11 this dataset is invaluable in uncovering where there is work to be done for improved healthcare provision in each country across the world

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Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Understand the context: Before you begin analyzing this dataset, it is important to understand the information that it provides. Take some time to read the description of what is included in the dataset, including a clear understanding of the definitions and scope of coverage provided with each data point.

• Examine the data: Once you have a general understanding of this dataset's contents, take some time to explore its contents in more depth. What specific questions does this dataset help answer? What kind of insights does it provide? Are there any missing pieces?

• Clean & Prepare Data: After you've preliminarily examined its content, start preparing your data for further analysis and visualization. Clean up any formatting issues or irregularities present in your data set by correcting typos and eliminating unnecessary rows or columns before working with your chosen programming language (I prefer R for data manipulation tasks). Additionally, consider performing necessary transformations such as sorting or averaging values if appropriate for the findings you wish to draw from your analysis.

• Visualize Results: Once you've cleaned and prepared your data, use visualizations such as charts, graphs or tables to reveal patterns within it that support specific conclusions about how insurance coverage under social programs vary among different groups within society's quintiles - based on age groups etc.. This type of visualization allows those who aren't familiar with programming to process complex information quickly and accurately than when displayed numerically in tabular form only!

5 Final Analysis & Export Results: Finally export your visuals into presentation-ready formats (e.g., PDFs) which can be shared with colleagues! Additionally use these results as part of a narrative conclusion report providing an accurate assessment and meaningful interpretation about how social insurance programs vary between different members within society's quintiles (i..e., accordingest vs poorest), along with potential policy implications relevant for implementing effective strategies that improve access accordingly!

Research Ideas

• Analyzing the effectiveness of social insurance programs by comparing the coverage levels across different geographic areas or socio-economic groups;
• Estimating the economic impact of social insurance programs on local and national economies by tracking spending levels and revenues generated;
• Identifying potential problems with access to social insurance benefits, such as racial or gender disparities in benefit coverage

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: coverage-of-social-insurance-programs-in-richest-quintile-of-population-1.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit data.world's Admin.

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.

Context

Income inequality is a global issue reflecting the uneven distribution of wealth within and between countries. Developed nations exhibit varying income levels due to economic policies and labor dynamics, resulting in Gini coefficients of around 0.3 to 0.4. Conversely, developing nations often experience higher income disparities due to limited access to education, healthcare, and jobs, leading to Gini coefficients exceeding 0.4, exacerbating poverty cycles and social tensions. This inequality hampers economic growth, social cohesion, and upward mobility. Addressing it requires comprehensive policies, including progressive taxation and equitable resource distribution, to promote a more just and inclusive society.

Content

This dataset comprises historical information encompassing various indicators concerning Inequality in Income on a global scale. The dataset prominently features: ISO3, Country, Continent, Hemisphere, Human Development Groups, UNDP Developing Regions, HDI Rank (2021), and Inequality in Income from 2010 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Inequality in Income from 2010 to 2021 - Inequality in Income from year 2010 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

https://i.imgur.com/LIrXWPP.png" alt="">

Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by Image by pch.vector on Freepik

Thumbnail by: Image by Salary icons created by Freepik - Flaticon

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

There are two tables :- 1) World_Richest.csv - It includes the Forbes Top 950 richest people by Name, Age, Asset and Country. For 2015. 2) Human Development Index (HDI).csv - It the Global Human Development Index (1990-2015). From (http://hdr.undp.org/en/data).

Acknowledgements

Forbes - https://www.forbes.com/billionaires/list/ United Nations Development Programme - http://hdr.undp.org/en/data

Inspiration

I organised these datasets because I believe leaders come from the people. So I am looking at the success of individuals versus the success of a group (Country).

Based on Bloomberg's Billionaires index...

The Bloomberg Billionaires Index is a daily ranking of the world's richest people. In calculating net worth, Bloomberg News strives to provide the most transparent calculations available, and each individual billionaire profile contains a detailed analysis of how that person's fortune is tallied.

The index is a dynamic measure of personal wealth based on changes in markets, the economy and Bloomberg reporting. Each net worth figure is updated every business day after the close of trading in New York. Stakes in publicly traded companies are valued using the share's most recent closing price. Valuations are converted to U.S. dollars at current exchange rates.

Closely held companies are valued in several ways, such as by comparing the enterprise value-to-Ebitda or price-to-earnings ratios of similar public companies or by using comparable transactions. Calculations of closely held company debt -- if net debt cannot be determined -- are based on the net debt-to-Ebitda ratios of comparable peers. The value of closely held companies adjusts daily based on market moves for peer companies or by applying the market movement of a relevant industry index. The criteria used to choose peer companies is based on the closely held asset's industry and size.

When ownership of closely held assets cannot be verified, they aren't included in the calculations. The specific valuation methodology for each closely held company is included in the net worth analysis section of a billionaire's profile. Additional details included in the valuation notes for each asset are available to subscribers of the Bloomberg Professional Service.

A standard liquidity discount of 5 percent is applied to most closely held companies where assets may be hard to sell. When a different percentage is used an explanation is given. No liquidity discounts are applied to the values of public stakes. In some instances, a country risk discount is also applied based on a person's concentration of assets and ease of selling them in a given geography. A country's risk is assessed based on Standard & Poor's sovereign debt ratings.

If a billionaire has pledged as collateral shares he or she holds in a public company, the value of those shares or the value of a loan taken against them is removed from the net worth calculation. If reliable information can be obtained about the ultimate use of those borrowed funds, that value is added back into the calculation.

Hedge fund businesses are valued using the average market capitalization-to-assets under management ratios of the most comparable publicly traded funds. Fee income is not considered because it cannot be uniformly verified. Personal funds invested along with outside capital are not included in the calculation. A "key man" risk discount of 25 percent is applied to funds whose performance is tied to a single individual. Assets under management are updated using ADV forms filed with the federal government and news reports, and returns are factored when sourced to reports from credible news outfits, the HFRI Index and industry analysts.

Net worth calculations include dividend income paid and proceeds from the sale of public and closely held shares. Taxes are deducted based on prevailing income, dividend and capital gains tax rates in a billionaire's country of residence. Taxes are applied at the highest rate unless there is evidence to support a lower percentage, in which case an explanation is given in the net worth summary. For calculations of cash and other investable assets, a hybrid return based on holdings in cash, government bonds, equities and commodities is applied.

No assumptions are made about personal debt. Family members often hold a portion of a billionaire's assets. Such transfers don't change the nature of who ultimately controls the fortune. As a result, Bloomberg News operates under the rule that all billionaire fortunes are inherently family fortunes and credit family fortunes to the founders or ranking family members who are determined to have direct control over the assets. When individual stakes can be verified and adult family members have an active role in a business, the value is credited to each individual.

Each billionaire -- or a representative -- is given an opportunity to respond to questions regarding the net worth calculation, including assets and liabilities.

Bloomberg News editorial policy is to not cover Bloomberg L.P. As a result, Michael Bloomberg, the founder and majority owner of Bloomberg L.P., isn't considered for this ranking.

Because calculating net worth requires a degree of estimation, bull and bear case scenarios that would make a person's fortune higher or lower than the Bloomberg Billionaires Index valuation are included on the Bloomberg Professional Service. A confidence rating also is included on each profile:

The Commitment to Development Index (CDI), published annually by the Center for Global Development, ranks the world's richest countries on their dedication to policies that benefit the five billion people living in poorer nations. Rich and poor countries are linked in many ways; thus the Index looks beyond standard comparisons of foreign aid flows. It measures "development-friendliness" of 40 of the world's richest countries, all member nations of the OECD's Development Assistance Committee.

COLUMN DESCRIPTION

• 'Rank': rank of the country
• 'Country': name of the country
• 'Investment': percentage dedicated on investment
• 'Development Finance': percentage dedicated on development finance
• 'Technology': percentage dedicated on technology
• 'Environment': percentage dedicated on environment
• 'Trade': percentage dedicated on trade
• 'Security': percentage dedicated on security
• 'Migration': percentage dedicated on migration
• 'Health': percentage dedicated on health
• 'overall': overall percentage of dedication on their polices for poorer countries

Photo by Greg Rosenke on Unsplash

Context

The list of richest Indians by net worth based on an annual assessment of wealth and assets compiled and published by Forbes magazine. As of April 2023, India has 167 billionaires, which put the country third in the world, after the United States and China. Mukesh Ambani the chairman and largest shareholder of Reliance Industries, has been the richest Indian for 14 consecutive years. He is currently world's 10th richest person in the world according to Forbes. Savitri Jindal is currently India's richest woman, topping the list at 6th position.

Content

The Dataset contains the following columns:

1. Rank
2. Name
3. Net Worth USD
4. Company
5. Sources of Wealth

Acknowledgement

The source of data https://en.wikipedia.org/wiki/List_of_Indians_by_net_worth.

The rich don’t always get richer. As a group, the 400 wealthiest Americans are worth $4 trillion—$500 billion less than last year. The minimum net worth to make The Forbes 400 dropped for the first time since the Great Recession, down $200 million to $2.7 billion amid the market selloff. No one has been hit harder than tech billionaires, who have lost a combined $315 billion. Still, it was a great year to be an oil-and-gas tycoon, a sports mogul or Elon Musk. And 42 people, spanning ages 29 to 90, joined or rejoined the ranks. Fortunes were calculated using stock prices from September 2, 2022.

FILE DESCRIPTION

File name: forbes_400 _richest.csv

COLUMN DESCRIPTION

• 'bio': bio of the person
• 'rank': rank of the person
• 'finalWorth': final net worth of the person
• 'personName': name of the person
• 'city': city where the person lives
• 'source': source of income
• 'countryOfCitizenship': country where the person lives
• 'gender': gender of the person
• 'lastName': last name of the person
• 'estWorthPrev': estimated previous net worth
• 'privateAssetsWorth': worth of their private assets
• 'archivedWorth': archived worth
• 'abouts': a little bit about them
• 'state': state where they live
• 'industries': industry they are in

I built this dataset to answer one big question: Can people in developing regions be happier without being rich? I combined data from trusted global reports to compare happiness, education, and money in 14 South Asian and Middle Eastern countries.

What’s Inside?

• Happiness Scores (0–10 scale from the 2023 World Happiness Report)
• Education Stats: Literacy rates, school enrollment (offline), and % of people using online learning (UNESCO + government surveys)
• Money Metrics: GDP per person, average income, unemployment, and poverty rates (World Bank)
• Social Support: How much people feel helped by friends/family

Why These Countries?

• Places like India and Bangladesh have booming online education but low incomes.
• Gulf nations like Qatar and UAE are rich but score lower on social freedom.
• Afghanistan and Lebanon show how wars and crises crush happiness.

Cool Things You Can Do

1. Compare “happy poor” vs. “unhappy rich” countries:
   • Nepal (happiness = 5.269 | GDP = $1,380) vs. Saudi Arabia (happiness = 6.494 | GDP = $24,500)
2. Test if online education beats traditional schools:
   • UAE has 38.2% online learning access vs. Pakistan’s 11.8%
3. Find hidden patterns: Why does Sri Lanka have 92.3% literacy but high poverty (25.6%)?

Data Sources

• Happiness Scores: World Happiness Report 2023
• Education & Economy: World Bank and UNESCO (2023 estimates)
• Missing Data: Afghanistan’s GDP/income stats are blank due to Taliban rule.

Who Should Use This?

• Teachers studying education’s role in happiness
• Economists exploring “money vs. joy” debates
• Students learning data analysis with real-world problems

Pro Tip: Use maps to compare regions! Saudi Arabia’s happiness (6.494) is double Afghanistan’s (1.859).