Billionaires CSV File from From the CORGIS Dataset Project

This dataset is for demo purposes for this blog post - How to directly access 150k+ Hugging Face Datasets with DuckDB and query using GPT-4o and originated from the CORGIS dataset project.

  Dataset Details





  Dataset Description

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… See the full description on the dataset page: https://huggingface.co/datasets/chilijung/Billionaires.

This dataset is about books. It has 2 rows and is filtered where the book is Millionaire traders : how everyday people are beating Wall Street at its own game. It features 7 columns including author, publication date, language, and book publisher.

This article introduces an original dataset of formal political participation for over 2,000 individuals included in the Forbes Billionaires List. We find that billionaire politicians are a surprisingly common phenomenon: Over 11% of the world’s billionaires have held or sought political office. Even compared to other elite groups known for producing politicians from their ranks, this is a high rate of political participation. Moreover, billionaires focus their political ambitions on influential positions, have a strong track record of winning elections, and lean to the right ideologically. We also document substantial cross-national variation: A country’s number of billionaire politicians is not simply a product of its total number of billionaires but is instead related to regime type. Indeed, billionaires formally enter the political sphere at a much higher rate in autocracies than in democracies. We conclude by discussing the normative implications of our findings and outlining a new research agenda on billionaire politicians.

Introduction

In order to develop effective crowdsourcing aggregation methods for multiple choice question answering(MCQA) and evaluate them empirically, we developed and deployed a crowdsourced system for playing the “Who wants to be a millionaire?” quiz show. Note that, as question and answer texts are originally in Turkish you should use UTF8 format at all times to avoid encoding problems.

Citation

Harvard Aydin BI, Yilmaz YS, Demirbas M. A crowdsourced “Who wants to be a millionaire?” player. Concurrency Computat.: Pract. Exper. 2017;e4168. https://doi.org/10.1002/cpe.4168

Data

Over the period of 9 months, we collected over 3 GB of data using our CrowdMillionaire app. In our dataset, there are 1908 questions and 214,658 unique answers to those questions from CrowdMillionaire participants. In addition, we have more than 5 million offline answers for archived live questions. Our dataset includes detailed information on the game play. For example, our exhaustive timestamps show (1) how much time it took for a question to arrive to a participant, (2) when the question is actually presented to the participant on her device, and (3) when exactly the participant answered the question. We shared this dataset in order to advance the understanding of the MCQA dynamics, after we cleaned and anonymized the data.

Acknowlegement

Foto von Jason Leung auf Unsplash

We rely on theories positing general resentment of the rich to argue that people who believe there are greater number of Justices who are millionaires will have more negative attitudes towards the Court than those who believe there are fewer millionaires on the Court. Analyzing the results to a nationally representative survey, we find that individuals who believe a larger number of the Justices are millionaires are more likely to believe the Court gives special rights to the wealth and overall are less likely to view the Court as legitimate. We supplement these results with a survey experiment which demonstrations that individuals believe the Court will become less fair if a millionaire nominee is confirmed to be a Justice and that individuals are less likely to support a millionaire nominee compared to nominees with a lower net worth. Our results have implications for perceptions of bias within the judiciary, the selection of judicial nominees, and how attitudes about the wealthy can influence attitudes towards institutions.

This paper estimates the degree of risk aversion from one of the most popular TV gameshows ever. The format of the show is straightforward; it involves no strategic decision making; we have a large number of observations; and the prizes are cash, which is paid immediately and covers a large range: from £100 up to £1 million. We provide non-parametric estimates of the utility function and then we test some parametric restrictions. We find that, although the restriction to CRRA utility is statistically rejected, a log function approximates the utility function quite well over a large range of potential winnings.

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.

The number of high networth individuals in Russia was forecast to continuously increase between 2024 and 2029 by in total 179.7 thousand individuals (+33.88 percent). After the ninth consecutive increasing year, the number of individuals is estimated to reach 710.01 thousand individuals and therefore a new peak in 2029. High Net Worth Individuals are here defined as persons with investible assets of at least one million U.S. dollars in current exchange rate terms.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the number of high networth individuals in countries like Northern Europe and Eastern Europe.