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

  Join our Community









  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

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

This dataset contains the Forbes Billionaire List for 2024, featuring the top individuals ranked by their net worth. It includes essential details such as their names, rankings, and net worth. This data is ideal for quick analysis and insights into the wealthiest people globally in 2024.

Analysis of ‘World's Billionaires’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/seriadiallo1/world-billionaires on 14 February 2022.

--- Dataset description provided by original source is as follows ---

The richest people in the world, yearly rank from 2002 to 2021

This dataset contains 200 rows and 7 columns.

The World's Billionaires is an annual ranking by documented net worth of the world's wealthiest billionaires compiled and published in March annually by the American business magazine Forbes. The list was first published in March 1987. The total net worth of each individual on the list is estimated and is cited in United States dollars, based on their documented assets and accounting for debt. Royalty and dictators whose wealth comes from their positions are excluded from these lists. This ranking is an index of the wealthiest documented individuals, excluding and ranking against those with wealth that is not able to be completely ascertained. (wikipedia)

--- Original source retains full ownership of the source dataset ---

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. in recent years, 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 20 richest people in the world, only one was a woman. 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.

Dataset

This dataset was created by JR

Contents

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.

Context

The Original dataset had some incongruencies in the collection of the data for the feature "Source".

The feature "Source" aims at capturing the main origin of the billionaires' accumulated wealth. However, Forbes had adopted an inconsistent approach in collecting these data. When the source of wealth is connected with a popular or well-known company, then the Company name is reported (i.e. Amazon, Microsoft, Google and so forth). In other instances, the economic sector where the billionaire business operates is reported (i.e., software, machinery, food and beverage, and so forth).

For instance, Bill Gates' source of wealth is recorded as "Microsoft", while for Larry Ellison (founder of Oracle), the dataset mentions a generic "software". Jeff Bezos's source is Amazon, but e-commerce is the provenience of Jack Ma's wealth. This approach creates series of difficulties when aggregating the data. When billionaires are grouped by the "Source" as it appears in the original dataset, billionaires like Jeff Bezos or Jack Ma would be classified in two different buckets. However, they both operate in the same economic sector.

Therefore, the dataset was reviewed to standardize the "Source" entries. As a result, taking the first six wealthiest people in the world as an example, Amazon becomes e-commerce, Tesla is replaced by "electric vehicles", LVMH by "luxury good", Microsoft by "software", Facebook by "social media" and Berkshire Hathaway by "finance".

Acknowledgements Skimmed the data from forbes.com Based on the original dataset uploaded here by Alexander Bader : Link

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.

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.

Abstract

Dataset used in World Bank Policy Research Working Paper #2876, published in World Bank Economic Review, No. 1, 2005, pp. 21-44.

The effects of globalization on income distribution in rich and poor countries are a matter of controversy. While international trade theory in its most abstract formulation implies that increased trade and foreign investment should make income distribution more equal in poor countries and less equal in rich countries, finding these effects has proved elusive. The author presents another attempt to discern the effects of globalization by using data from household budget surveys and looking at the impact of openness and foreign direct investment on relative income shares of low and high deciles. The author finds some evidence that at very low average income levels, it is the rich who benefit from openness. As income levels rise to those of countries such as Chile, Colombia, or Czech Republic, for example, the situation changes, and it is the relative income of the poor and the middle class that rises compared with the rich. It seems that openness makes income distribution worse before making it better-or differently in that the effect of openness on a country's income distribution depends on the country's initial income level.

Kind of data

Aggregate data [agg]

The Mapping Ocean Wealth data viewer is a live online resource for sharing understanding of the value of marine and coastal ecosystems to people. It includes global maps, regionally-specific studies, reference data, and a number of “apps” providing key data analytics. Maps and apps can be opened according to key themes or geographies. The navigator the left of the maps enables you to add or remove any additional map layers as you explore. Information keys explain how the maps were made and provide additional links. Further information and resources can be found on Oceanwealth.org

• Recreation and Tourism App - Explore the value of healthy ecosystems to the tourism industry
• Natural Coastal Protection App - Discover the coastal protection benefits of coral reefs around the world
• Blue Carbon App - View Mangrove Carbon Storage
• Coral Reef Fisheries App - Learn about the status of coral reef fisheries
• Regional Planning
• Mangrove Restoration

We provide a dense interaction dataset, InterHub, derived from extensive naturalistic driving records to address the scarcity of real-world datasets capturing rich interaction events.The dataset provided on this page include:A CSV file (Interactive_Segments_Index.csv) containing the indexed list of the extracted interaction events. In addition to indexing and tracing information about interaction scenarios, we also provide some interesting labels to facilitate more targeted retrieval and utilization of interaction scenarios.(For detailed information, please refer to https://github.com/zxc-tju/InterHub.)Relevant unified data cache files (InterHub_cache_files.zip that includes cache files of lyft_train_full, nuplan_train).The Python codes used to process and analyze the dataset can be found at https://github.com/zxc-tju/InterHub. The tools for navigating InterHub involve the following three parts:0_data_unify.py converts various data resources into a unified format for seamless interaction event extraction.1_interaction_extract.py extracts interactive segments from unified driving records.2_case_visualize.py showcases interaction scenarios in InterHub.You can refer to the data structure of cache files presented in dataset.md, and after extracting the InterHub_cache_files.zip file, put it in the corresponding folder.Statement: All third-party data redistributions included in the interhub_cache_files.zip repository are carried out in full compliance with the original licensing terms of the respective source datasets, as required by their mandatory licensing conditions. This portion of the data remains subject to its original licenses, and users of the data are required to comply with these original licensing terms in any subsequent use or redistribution.

Our proprietary People Data is a mobile user dataset that connects anonymous IDs to a wide range of attributes, including demographics, device ownership, audience segments, key locations, and more. This rich dataset allows our partner brands to gain a comprehensive view of consumers based on their personas, enabling them to derive actionable insights swiftly.

People Data Graph

• Record Count: 900 Million
• Capturing Frequency: Once per Event
• Delivering Frequency: Once per Month
• Updated: Monthly

People Data

Reach Our extensive data reach covers a variety of categories, encompassing user demographics, Mobile Advertising IDs (MAID), device details, locations, affluence, interests, traveled countries, and more. Data Export Methodology We dynamically collect and provide the most updated data and insights through the best-suited method at appropriate intervals, whether daily, weekly, monthly, or quarterly.

Business Needs

Our People Data caters to various business needs, offering valuable insights for consumer analysis, data enrichment, sales forecasting, and retail analytics, empowering brands to make informed decisions and optimize their strategies.

Credit risk assessment remains a critical function within financial services, influencing lending decisions, portfolio risk management, and regulatory compliance. It integrates multiple categories of financial, transactional, and behavioral data to enable advanced machine learning applications in the domain of financial risk modeling.

Data Composition and Structure

The dataset comprises a total of 1,212 distinct features, systematically grouped into four principal categories, alongside a binary target variable. Each feature category represents a specific dimension of credit risk assessment, reflecting both internal transactional data and externally sourced credit bureau information.

Target Variable

The dependent variable, denoted as bad_flag, represents a binary risk classification outcome associated with each customer account. The variable takes the following values:

• 0: Denotes a low-risk, creditworthy customer
• 1: Denotes a high-risk, default-prone customer

This variable serves as the target for binary classification models aimed at predicting credit risk propensity.

Feature Groups

Each feature within a category follows a systematic sequential naming convention (e.g., transaction_attribute_1, bureau_1), facilitating programmatic identification and group-level analysis.

Data Characteristics

The dataset exhibits several characteristics that mirror operational credit risk data environments:

• High Dimensionality: The feature space exceeds 1,200 variables
• Mixed Data Types: Numerical values (continuous and discrete), binary indicators
• High Sparsity: A substantial proportion of features contain zero values or missing entries
• Value Range Disparity: Feature values exhibit significant variance, with magnitudes ranging from small ratios (0.001) to large transaction amounts (288,500)

Methodological Rationale

The dataset was constructed by simulating data generation processes typical within financial services institutions. Transactional behaviors, bureau records, and inquiry histories were aggregated and engineered into derivative features.

The Our World in Data COVID vaccination data

To bring this pandemic to an end, a large share of the world needs to be immune to the virus. The safest way to achieve this is with a vaccine. Vaccines are a technology that humanity has often relied on in the past to bring down the death toll of infectious diseases.

Within less than 12 months after the beginning of the COVID-19 pandemic, several research teams rose to the challenge and developed vaccines that protect from SARS-CoV-2, the virus that causes COVID-19.

Now the challenge is to make these vaccines available to people around the world. It will be key that people in all countries — not just in rich countries — receive the required protection. To track this effort we at Our World in Data are building the international COVID-19 vaccination dataset that we make available on this page.

Advani, Hughson and Tarrant (2021) model the revenue that could be raised from an annual and a one-off wealth tax of the design recommended by Advani, Chamberlain and Summers in the Wealth Tax Commission’s Final Report (2020). This deposit contains the code required to replicate the revenue modelling and distributional analysis. The modelling draws on data from the Wealth and Assets Survey, supplemented with the Sunday Times Rich List, which we use to implement a Pareto correction for the under-coverage of wealth at the top.Around the world, the unprecedented public spending required to tackle COVID-19 will inevitably be followed by a debate about how to rebuild public finances. At the same time, politicians in many countries are already facing far-reaching questions from their electorates about the widening cracks in the social fabric that this pandemic has exposed, as prior inequalities become amplified and public services are stretched to their limits. These simultaneous shocks to national politics inevitably encourage people to 'think big' on tax policy. Even before the current crisis there were widespread calls for reforms to the taxation of wealth in the UK. These proposals have so far focused on reforming existing taxes. However, other countries have begun to raise the idea of introducing a 'wealth tax'-a new tax on ownership of wealth (net of debt). COVID-19 has rapidly pushed this idea higher up political agendas around the world, but existing studies fall a long way short of providing policymakers with a comprehensive blueprint for whether and how to introduce a wealth tax. Critics point to a number of legitimate issues that would need to be addressed. Would it be fair, and would the public support it? Is this type of tax justified from an economic perspective? How would you stop the wealthiest from hiding their assets? Will they all simply leave? How can you value some assets? What happens to people who own lots of wealth, but have little income with which to pay a wealth tax? And if wealth taxes are such a good idea, why have many countries abandoned them? These are important questions, without straightforward answers. The UK government last considered a wealth tax in the mid-1970s. This was also the last time that academics and policymakers in the UK thought seriously about how such a tax could be implemented. Over the past half century, much has changed in the mobility of people, the structure of our tax system, the availability of data, and the scope for digital solutions and coordination between tax authorities. Old plans therefore cannot be pulled 'off the shelf'. This project will evaluate whether a wealth tax for the UK would be desirable and deliverable. We will address the following three main research questions: (1) Is a wealth tax justified in principle, on economic or other grounds? (2) How should a wealth tax be designed, including definition of the tax base and solutions to administrative challenges such as valuation and liquidity? (3) What would be the revenue and distributional effects of a wealth tax in the UK, for a variety of design options and at specified rates/thresholds? To answer these questions, we will draw on a network of world-leading exports on tax policy from across academia, policy spheres, and legal practice. We will examine international experience, synthesising a large body of existing research originating in countries that already have (or have had) a wealth tax. We will add to these resources through novel research that draws on adjacent fields and disciplines to craft new solutions to the practical problems faced in delivering a wealth tax. We will also review common objections to a wealth tax. These new insights will be published in a series of 'evidence papers' made available directly to the public and policymakers. We will also publish a final report that states key recommendations for government and (if appropriate) delivers a 'ready to legislate' design for a wealth tax. We will not recommend specific rates or thresholds for the tax. Instead, we will create an online 'tax simulator' so that policymakers and members of the public can model the revenue and distributional effects of different options. We will also work with international partners to inform debates about wealth taxes in other countries. The modelling draws on data from the Wealth and Assets Survey, supplemented with the Sunday Times Rich List, which we use to implement a Pareto correction for the under-coverage of wealth at the top.

REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly

📋 Introduction

Robotic manipulation remains a core challenge in robotics, particularly for contact-rich tasks such as industrial assembly and disassembly. Existing datasets have significantly advanced learning in manipulation but are primarily focused on simpler tasks like object rearrangement, falling short of capturing the complexity and physical dynamics involved in assembly and disassembly. To bridge this gap, we present REASSEMBLE (Robotic assEmbly disASSEMBLy datasEt), a new dataset designed specifically for contact-rich manipulation tasks. Built around the NIST Assembly Task Board 1 benchmark, REASSEMBLE includes four actions (pick, insert, remove, and place) involving 17 objects. The dataset contains 4,551 demonstrations, of which 4,035 were successful, spanning a total of 781 minutes. Our dataset features multi-modal sensor data including event cameras, force-torque sensors, microphones, and multi-view RGB cameras. This diverse dataset supports research in areas such as learning contact-rich manipulation, task condition identification, action segmentation, and more. We believe REASSEMBLE will be a valuable resource for advancing robotic manipulation in complex, real-world scenarios.

{"Hama1": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ], 
 "Hama2": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ], 
 "DAVIS346": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ], 
 "NIST_Board1": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ]
}

[x, y, z] represent the position of the object, and [qx, qy, qz, qw] represent its orientation as a quaternion.

The HDF5 (.h5) format organizes data into two main types of structures: datasets, which hold the actual data, and groups, which act like folders that can contain datasets or other groups. In the diagram below, groups are shown as folder icons, and datasets as file icons. The main group of the file directly contains the video, audio, and event data. To save memory, video and audio are stored as encoded byte strings, while event data is stored as arrays. The robot’s proprioceptive information is kept in the robot_state group as arrays. Because different sensors record data at different rates, the arrays vary in length (signified by the N_xxx variable in the data shapes). To align the sensory data, each sensor’s timestamps are stored separately in the timestamps group. Information about action segments is stored in the segments_info group. Each segment is saved as a subgroup, named according to its order in the demonstration, and includes a start timestamp, end timestamp, a success indicator, and a natural language description of the action. Within each segment, low-level skills are organized under a low_level subgroup, following the same structure as the high-level annotations.

📁

The splits folder contains two text files which list the h5 files used for the traning and validation splits.

📌 Important Resources

The project website contains more details about the REASSEMBLE dataset. The Code for loading and visualizing the data is avaibile on our github repository.

📄 Project website: https://tuwien-asl.github.io/REASSEMBLE_page/
💻 Code: https://github.com/TUWIEN-ASL/REASSEMBLE

Atlas AI's Asset Wealth Index (AWI) layer estimates household asset wealth based on asset ownership.

Accurate and comprehensive measurements of economic well-being are fundamental inputs into commerce, research, and policy but such measures are unavailable at a local level in many parts of the world. Our Asset Wealth layer is produced from a deep learning model that predicts survey-based estimates from satellite imagery. To facilitate comparison within and across countries, we transformed Asset Wealth into a normalized index.

This data is available in multiple versions in raster format and is updated annually for several geographic regions. Yearly and regional coverage for the current versions are:

• v2022: 43 countries in Sub-Saharan Africa from 2003–2021 at 1km resolution

• v2022 (beta): Six countries in South Asia from 2015–2021 at 1km resolution

• v2021: Sub-Saharan Africa from 2003–2020 at 2km resolution

For details on methods, input data sources, validation, and background citations, please consult: https://docs.atlasai.co/economic%20well-being/asset_wealth/

This data is offered under a restricted license. Please see resource constraints section below for terms and conditions.

Supplemental Information:

Unit of measure: Index

Flags (including missing value):

• No-data value: 1.79769e+308 (v2022/v2022 beta) or -99999 (v2021)

• Index mean: 0

Citation:

Publications, books, articles, blogs, conference papers, reports or other derivative works employing data obtained from Atlas AI should cite the source and the dataset(s) as indicated below:

Atlas AI (year dataset accessed) "Dataset name (dataset version)”

e.g. Atlas AI (2021) "Asset Wealth Index, v.2021”

Contact points:

Resource Contact: Vivek Sakhrani

Metadata Contact: FAO-Data

Resource constraints:

The use of this dataset is restricted. The Atlas AI dataset is distributed to authorized users within the Food and Agriculture Organization of the UN. FAO grants a license to download, use and print the materials contained in the Atlas AI dataset solely for non-commercial purposes and in accordance with the conditions specified in the data license agreement. Sharing the dataset in raw or aggregated form without express prior consent from AtlasAI is not allowed.

For any further information, please contact FAO-Data at fao-data@fao.org.

Online resources:

Dataset documentation, default symbology, and code samples

LAC is the most water-rich region in the world by most metrics; however, water resource distribution throughout the region does not correspond demand. To understand water risk throughout the region, this dataset provides population and land area estimates for factors related to water risk, allowing users to explore vulnerability throughout the region to multiple dimensions of water risk. This dataset contains estimates of populations living in areas of water stress and risk in 27 countries in Latin America and the Caribbean (LAC) at the municipal level. The dataset contains categories of 18 factors related to water risk and 39 indices of water risk and population estimates within each with aggregations possible at the basin, state, country, and regional level. The population data used to generate this dataset were obtained from the WorldPop project 2020 UN-adjusted population projections, while estimates of water stress and risk come from WRI’s Aqueduct 3.0 Water Risk Framework. Municipal administrative boundaries are from the Database of Global Administrative Areas (GADM). For more information on the methodology users are invited to read IADB Technical Note IDB-TN-2411: “Scarcity in the Land of Plenty”, and WRIs “Aqueduct 3.0: Updated Decision-relevant Global Water Risk Indicators”.

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).