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.

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 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/

• The billionaires satatistic dataset 2023 is a ranking of billionaires from 1st (with the highest wealth) to the 2640th (the less wealthy) in year 2023. the columns of the dataset are some attributs of the billionaires as follow:

rank: The ranking of the billionaire in terms of wealth. finalWorth: The final net worth of the billionaire in Million 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.

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.

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Patterns of educational attainment vary greatly across countries, and across population groups within countries. In some countries, virtually all children complete basic education whereas in others large groups fall short. The primary purpose of this database, and the associated research program, is to document and analyze these differences using a compilation of a variety of household-based data sets: Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS); Living Standards Measurement Study Surveys (LSMS); as well as country-specific Integrated Household Surveys (IHS) such as Socio-Economic Surveys.

As shown at the website associated with this database, there are dramatic differences in attainment by wealth. When households are ranked according to their wealth status (or more precisely, a proxy based on the assets owned by members of the household) there are striking differences in the attainment patterns of children from the richest 20 percent compared to the poorest 20 percent.

In Mali in 2012 only 34 percent of 15 to 19 year olds in the poorest quintile have completed grade 1 whereas 80 percent of the richest quintile have done so. In many countries, for example Pakistan, Peru and Indonesia, almost all the children from the wealthiest households have completed at least one year of schooling. In some countries, like Mali and Pakistan, wealth gaps are evident from grade 1 on, in other countries, like Peru and Indonesia, wealth gaps emerge later in the school system.

The EdAttain website allows a visual exploration of gaps in attainment and enrollment within and across countries, based on the international database which spans multiple years from over 120 countries and includes indicators disaggregated by wealth, gender and urban/rural location. The database underlying that site can be downloaded from here.

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

The Jobs of the World Database (JWD) is a publicly available database constructed by harmonizing the available micro-datasets which contain information on jobs and labor market activities in low- and middle-income countries to create a multi-country macro-dataset. The current version of the data harmonizes census data (IPUMS) and Demographic and Health Surveys (DHS). This provides coverage of countries representing about 81 percent of the world’s population, and more than 90 percent of the population in low- and middle-income countries. The database focuses on a wide range of labor market characteristics including, but not limited to: labor force participation, type of employment (e.g., waged or self-employment), sector of employment, skill level, etc. The data also contains information about internal and external migration patterns. All these aspects can be shown as aggregate at the level of country year, but also split along different characteristics including gender, education level, age groups, urban vs rural regions, etc. A major advantage of the database is the use of detailed data about household assets and dwelling characteristics to estimate a wealth density at the household level. This density was used to create wealth quintiles which can be used to investigate the labor market characteristic for different socio-economic groups (very poor, poor, average, rich, and very rich).

Dataset

This dataset was created by Ravi_kr

Contents

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.

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Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info