28:null 29:["$","title",null,{"children":"How to Become a Billionaire 💸: A Billionaire's Occupations Network Analysis | JASMINE CHU's Workspace | Observable"}] 2e:T41a,# Introduction Have you ever wondered what makes a billionaire a billionaire? According to [this Forbes article](https://www.forbes.com/sites/richardjchang/2023/04/09/the-path-to-billions-the-industries-with-the-most-billionaires-2023/?sh=77b08e013ada), getting involved in certain industries may increase your chances of becoming one. This network analysis investigates billionaires’ primary sources of income with a network graph—based on their occupations—connecting billionaires from all over the world and uncovering some of the biggest industries in the world. Additionally, we will look at the gender distribution of the dataset, the spread of billionaires on a map, and discuss significant gaps in the data. The project delves into several questions, including: - What are some of the most common jobs billionaires have? - What are the biggest industries (in terms of money)? - What’s up with the gender distribution? Now, are you ready to learn how to become a billionaire? Let’s go! 🤑2f:T9d7,chart = { const links = data.links.map(d => Object.create(d)); const nodes = data.nodes.map(d => Object.create(d)); const simulation = d3.forceSimulation(nodes) .force("link", d3.forceLink(links).id(d => d.id)) .force("charge", d3.forceManyBody().strength(-400)) .force("x", d3.forceX()) .force("y", d3.forceY()); const svg = d3.create("svg") .attr("viewBox", [-width / 2, -height / 2, width, height]) .style("font", "12px sans-serif"); // Create a container to enable mousewheel zoom var g = svg.append("g") .attr("class", "everything"); // Per-type markers, as they don't inherit styles. svg.append("defs").selectAll("marker") .data(types) .join("marker") .attr("id", d => `arrow-${d}`) .attr("viewBox", "0 -5 10 10") .attr("refX", 15) .attr("refY", -0.5) .attr("markerWidth", 6) .attr("markerHeight", 6) .attr("orient", "auto") .append("path") .attr("fill", color) .attr("d", "M0,-5L10,0L0,5"); const link = g.append("g") .attr("fill", "none") .attr("stroke-width", 1.5) .selectAll("path") .data(links) .join("path") .attr("stroke", d => color(d.type)) .attr("marker-end", d => `url(${new URL(`#arrow-${d.type}`, location)})`); link.append("title") .text(d => d.id); const node = g.append("g") .attr("fill", "currentColor") .attr("stroke-linecap", "round") .attr("stroke-linejoin", "round") .selectAll("g") .data(nodes) .join("g") .call(drag(simulation)); node.append("circle") .attr("stroke", "white") .attr("stroke-width", 1.5) .attr("r", 4); node.append("title") .text(d => d.id); const textElems = g.append('g') .selectAll('text') .data(nodes) .join('text') .text(d => d.id) .attr('font-size',12) .attr('font-size',12) .call(drag(simulation)) textElems.append("title") .text(d => d.id); simulation.on("tick", () => { link.attr("d", linkArc); node.attr("transform", d => `translate(${d.x},${d.y})`); textElems .attr("x", d => d.x + 10) .attr("y", d => d.y); }); invalidation.then(() => simulation.stop()); // Add mousewheel zoom functionality svg.call(d3.zoom() .extent([[0, 0], [width, height]]) .scaleExtent([0, 8]) .on("zoom", zoomed)); function zoomed({transform}) { g.attr("transform", transform); } return svg.node(); } 30:Tdcc,# Takeaways from the Network Graph ## Observations * The top 5 billionaires' occupations are businessperson, entrepreneur, politician, business executive, and computer scientist. * * **Businessperson**: an individual who has founded, owns, or holds shares in a private-sector company * **Entrepreneur**: a person who organizes and operates a business or businesses, taking on greater than normal financial risks in order to do so * **Politician**: a person who has political power in the government of a state, a person active in party politics, or a person holding or seeking an elected office in government * **Business executive**: a person responsible for running an organization; they run companies or government agencies and create plans to help their organizations grow * **Computer scientist**: an individual who uses expert technical knowledge like utilizing data, developing new software and analyzing trends to create solutions for organizations * The individual in the center of the clusters are ones with the greatest net worth within the cluster. * Elon Musk has the most career connections. * Others with an extensive network of connections include Mark Zuckerberg, Gautam Adani, Jeff Bezos, Jim Walton, Warren Buffett, Michael Bloomberg, and Bill Gates. \*Although listed as an occupation in Wikidata, *international forum participant* was excluded because it is not exactly a career. Participants are primarily business leaders who have responsibilities for a global business. They come from more than 15 countries around the world and over 200 companies to learn more about what is happening in the world. ## A quick analysis The most common jobs among billionaires like computer scientist, engineer, banker, investor, and financier are in the technology, finance, investment, and banking industries, suggesting these are some of the biggest industries in the world. Furthermore, over 1/2 of these billionaires have the title *businessperson*. Along with the information provided in [this Forbes article](https://www.forbes.com/sites/richardjchang/2023/04/09/the-path-to-billions-the-industries-with-the-most-billionaires-2023/?sh=77b08e013ada), we can conclude that these include industries such as manufacturing, fashion and retail, and food and beverage. If we look at Elon Musk's involvement in various domains such as technology, finance, investments, and more, it justifies his status as the richest person in the world. In short, consider looking into some of these industries. It might just put you on the road to riches! ## A few notes about the data and network graph * Only occupations with 3 or more edges (connections) are included, with the exception of individuals who have only one occupation listed and is not connected to anyone else. (For example, someone with *entrepreneur, comedian,* and *saxophonist* listed in their occupations will only have *entrepreneur* included as the other jobs are not as relevant when focusing on jobs they have in common) * Some occupations were merged together to reduce clutter. (e.g. changed voice actor to actor, changed different types of engineers—chemical, civil, etc.—to engineers) * There were some errors in the data, so they were manually fixed with some additional research. (e.g. same name, different person—there was an entry with a Seo Jung-jin whose a soccer player, but the billionaire Seo jung-jin is a co-founder of Celltrion, a biopharmaceutical firm that makes drugs to treat cancer, influenza and rheumatoid arthritis.)31:T6f6,## Analysis From this pie chart, we can see a significant gender gap in the data. Out of the 560 billionaires in the dataset, only 64 of them are females—that's 11.4%! This could be the true gap or it could be a problem in the data. We'll first take a look at the data... The number of billionaires generated from the query is notably less than the whole population. According to [Statista](https://www.statista.com/statistics/778577/billionaires-gender-distribution/), there were 3,194 billionaires world-wide in 2022, while this query only generated 560 people. Some possible reasons: * Only 560 billionaires are added into Wikidata * Some billionaires’ pages don’t have their net worth listed or they are not updated (the query filters by net worth greater than or equal to 1 billion) Since there are some problems with the data, it'll be hard to make solid conclusions about the gender gap based on this dataset alone. However, upon comparing the gender ratios derived from this data with those from [Statista](https://www.statista.com/statistics/778577/billionaires-gender-distribution/), it becomes apparent that they are quite similar (Statista: 12.5% females, this dataset: 11.4% females). This indicates a substantial gender disparity among billionaires, with fewer female billionaires compared to their male counterparts. Based on the content of this [Forbes article](https://www.forbes.com/2010/06/18/richest-women-billionaires-entrepreneurs-forbes-woman-power-women-oprah-winfrey.html?sh=581792777e5a), this could be due to factors like discrimination, differences in inherent motivation between men and women, women choosing to prioritize other aspects of life (e.g. family life over financial success), or lack of opportunities like educational limitations. 32:Tb67,## Analysis At a glance, there are 4 main clusters of billionaires' hometowns, which are in the USA, Europe, and Asia (particularly South and East Asia). This could mean two things: 1. These regions offer the most opportunities for wealth accumulation. 2. The wealthiest families are situated in these areas. For the first case, we found that the biggest industries in the world are finance & investments, technology, manufacturing, fashion & retail, and food & beverage. * **Finance & Investments**: In this [Investopedia article](https://www.investopedia.com/articles/financialcareers/top-10-financial-career-cities.asp#:~:text=New%20York%20City%20is%20typically,headquartered%20in%20New%20York%20City.), the top 10 cities for a career in finance are are all part of the 4 groups as observed in the map.
* **Technology**: According to [this ranking](https://worldpopulationreview.com/country-rankings/most-technologically-advanced-countries), Japan, United States, and South Korea are the top 3 most technologically advanced countries, which are also all part of the clusters.
* **Manufacturing**: [This ranking](https://www.safeguardglobal.com/resources/top-10-manufacturing-countries-in-the-world-2023/) of the top 10 manufacturing countries in the world is also in line with the concentration of the clusters in the map.
* **Fashion & retail**: It says [here](https://www.statista.com/statistics/1202694/fashion-industry-leading-countries-worldwide/#:~:text=Leading%20countries%20for%20the%20fashion%20industry%20worldwide%202022&text=France%20is%20the%20leading%20country,the%20U.S.%20(2%2C235%20points) that "France is the leading country for the fashion industry in the world." France is located in the biggest set of points in Europe.
* **Food & beverage**: Lastly, once again, the [biggest f&b companies](https://vinut.com.vn/2023/blogs/top-10-largest-food-and-beverage-companies/#:~:text=Nestl%C3%A9%20is%20the%20largest%20food,operates%20in%20191%20countries%20worldwide.) are consistent with the clusters on the map. Most of these companies are located in the United States. From this, we can conclude that growing up in countries with the wealthiest industries could be a key factor in attaining wealth. In the second case, billionaires who inherit their wealth could be living in these regions. Although it has been found that [most of the world's billionaires are self-made](https://www.statista.com/statistics/621426/sources-of-wealth-of-global-billionaires/), wealthy families usually stay rich or get richer, so this second speculation can not be ruled out. ## Notes * Some of them had multiple locations listed, so only the most precise location was kept. * There were some entries that were not locations, so those were manually fixed with additional research. * What's next: update the map so that it incorporates net worth (e.g. different sized bubbles, gradient colors)33:[{"id":"77fa976e4aac80307f18124bec1f2294a7179826a86451d451e9ca197620245572e416d66f72b6214ea7671dc8ef9b29afd7d87db31254835a9feb2f5bcde236","href":"https://static.observableusercontent.com/files/77fa976e4aac80307f18124bec1f2294a7179826a86451d451e9ca197620245572e416d66f72b6214ea7671dc8ef9b29afd7d87db31254835a9feb2f5bcde236","name":"billionaires_query@1.txt","type":"text/plain","lastModified":1702682966485.071,"size":477},{"id":"68f197a3675a3a2085003d87ab1f6840fb205379ca68c5f6d83fb72873dd5760a27c462d591a0bcd85cf12637571514beab9aa838ca795125539c4c9489931e6","href":"https://static.observableusercontent.com/files/68f197a3675a3a2085003d87ab1f6840fb205379ca68c5f6d83fb72873dd5760a27c462d591a0bcd85cf12637571514beab9aa838ca795125539c4c9489931e6","name":"billionaire_network.csv","type":"text/csv","lastModified":1702710364174.605,"size":49847}] 2c:{"id":"f5bb6ee872abfa82","slug":null,"title":"How to Become a Billionaire 💸: A Billionaire's Occupations Network Analysis","thumbnail":"2daed2b9d71fa80420dc5a66922001968bea0d9c64bc30e0403a0e82f8270abc","access_level":"public","create_time":"$D2023-11-28T22:32:02.551Z","update_time":"$D2023-12-17T02:52:00.432Z","view_time":null,"first_public_time":"$D2023-11-28T22:32:02.551Z","fork":{"id":"a6862bdc94780e4c","slug":null,"title":"Roman Republican Networks - The Gens Aelius","owner":{"id":"7dff22f07f8f5363","login":"jasmine-chu","name":"JASMINE CHU"}},"stars":1,"starred":false,"owner":{"type":"team","id":"6046f507f84ab4c4","login":"jasmine-chu-ws","name":"JASMINE CHU's Workspace","avatar_url":"https://avatars-next.observableusercontent.com/7acbbee8738e897bb99814a10bd6a585d08de746e7c8a48a92a5e3195f193384","tier":"starter_2024"},"version":661,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"50ff860ccc38d237","login":"jasmine-chu","name":"JASMINE CHU","avatar_url":"https://avatars-next.observableusercontent.com/01dc8b59652f588a38e6574b6076d7d5ced6bf23e946ef627859079f90c54200","tier":null}],"members":[{"type":"individual","id":"50ff860ccc38d237","login":"jasmine-chu","name":"JASMINE CHU","avatar_url":"https://avatars-next.observableusercontent.com/01dc8b59652f588a38e6574b6076d7d5ced6bf23e946ef627859079f90c54200","tier":null,"role":"editor"},{"type":"team","id":"6046f507f84ab4c4","login":"jasmine-chu-ws","name":"JASMINE CHU's Workspace","avatar_url":"https://avatars-next.observableusercontent.com/7acbbee8738e897bb99814a10bd6a585d08de746e7c8a48a92a5e3195f193384","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"How to Become a Billionaire 💸: A Billionaire's Occupations Network Analysis","cells":[{"id":1,"value":"md`# How to Become a Billionaire 💸: A Billionaire's Occupations Network Analysis\n\nA network graph that depicts the relationships of billionaires based on their occupations. Data: [Wikidata](https://query.wikidata.org/)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":474,"value":"![](https://newsin.asia/wp-content/uploads/2022/05/dollar.jpg)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":410,"value":"$2e","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":498,"value":"# Network Graph\nBased on billionaires' occupations (nodes: billionaires, edges: occupations)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":206,"value":"swatches({color})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":7,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":532,"value":"## Supplementary Zoomable Bar Chart\nVisualize how many billionaires have each job. This bar chart shows the number of billionaires on the x-axis and the different occupations on the y-axis. Double-click on the bar chart below or use the mouse wheel (or pinch) to zoom.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":530,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":570,"value":"## Supplementary Treemap\n\nVisualize the ranking of the wealthiest people in the world by their net worths. The bigger the box, the greater the net worth.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":568,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":339,"value":"$30","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":143,"value":"links = d3.csvParse(await FileAttachment(\"billionaire_network.csv\").text())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":343,"value":"## The relationships\n\nThese relationships are all generated directly from the SPARQL query.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":196,"value":"types = Array.from(new Set(links.map(d => d.type)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3,"value":"data = ({nodes: Array.from(new Set(links.flatMap(l => [l.source, l.target])), id => ({id})), links})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":32,"value":"height = 1000","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":202,"value":"color = d3.scaleOrdinal(types, d3.schemeCategory10)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":159,"value":"function linkArc(d) {\n const r = Math.hypot(d.target.x - d.source.x, d.target.y - d.source.y);\n return `\n M${d.source.x},${d.source.y}\n A${r},${r} 0 0,1 ${d.target.x},${d.target.y}\n `;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":76,"value":"drag = simulation => {\n \n function dragstarted(event, d) {\n if (!event.active) simulation.alphaTarget(0.3).restart();\n d.fx = d.x;\n d.fy = d.y;\n }\n \n function dragged(event, d) {\n d.fx = event.x;\n d.fy = event.y;\n }\n \n function dragended(event, d) {\n if (!event.active) simulation.alphaTarget(0);\n d.fx = null;\n d.fy = null;\n }\n \n return d3.drag()\n .on(\"start\", dragstarted)\n .on(\"drag\", dragged)\n .on(\"end\", dragended);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":11,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":217,"value":"import {swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":560,"value":"# Pie Chart\nThis chart shows the gender distribution in the dataset.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":486,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":576,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":608,"value":"# Clickable World Map\nVisualize the spread of billionaires around the world. Locations of the points are based on their place of birth. Click on countries to reveal country names.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":606,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":613,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":326,"value":"# SPARQL Query\n\nThis query searches Wikidata for the occupations of individuals whose net worth is at least 1 billion.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":328,"value":"FileAttachment(\"billionaires_query@1.txt\").text()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":484,"value":"## Query Breakdown\n * **a wikibase:BestRank** -- displays the individual's most recent net worth record\n * **P2218** -- net worth\n * **P106** -- occupation\n * **P19** -- place of birth\n * **p21** -- sex or gender\n * **FILTER (?netWorth >= 1000000000)** -- filters the dataset to only show people whose net worth is at least 1 billion\n * **ORDER BY DESC(?netWorth)** -- sort the dataset by net worth from greatest to least\n\n## Notes\n * did not include place of birth (POB) in query for the network graph dataset because some people don't have their POB listed on their Wikidata page (including POB in the query would've resulted in those people not showing up)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":460,"value":"# Conclusion\nWhile the information retrieved from querying Wikidata might lack the level of detail or completeness we desire, we can still extract essential insights from this dataset. Through the network graph, bar chart, and tree map, we discovered that there is a correlation between net worth and certain industries. Individuals who have the greatest net worth are involved in some of the biggest industries such as finance, investments, and technology. \n\nLastly, this dataset emphasizes the gender gap we see all the time. It shows that there’s gender disparity even in the top percentage of people in the world. And if it’s because the data is incomplete, then it highlights how problematic this gap is in a world where data is used to make policies and decisions. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"}],"files":"$W33","stdlib":"1","titling":"auto"}} 2d:"$2c:body"