2d:null 2e:["$","title",null,{"children":"Tech Layoffs Analysis | Shanfan Huang | Observable"}] 33:T406,Data Source: https://www.kaggle.com/datasets/swaptr/layoffs-2022 --- # **Introduction** This analysis explores **layoff patterns across various industry sectors**, segmented by a company’s **funding stage**, using data collected between **March 2020** and **January 2025**. To ensure clarity and consistency in the visualizations, the original dataset was transformed in several key ways. See **Appendix** for details. ## Data Encoding for Visual Analysis This analysis features **two distinct charts** that use the **same dataset** but present it differently to highlight various aspects of the data. Both charts share the same **color encoding**, **X-axis**, and **filtering UI** for consistency. However, they differ in how they represent the **number of employees laid off**: encoded as **circle size** in **Chart 1**, and plotted along **Y-axis** in **Chart 2**. ### Color: Average Percentage of Workforce Laid Off This **color scale** represents the **percentage of layoffs**, ranging from **1% to 100%**. 34:T4c0,visualizeBubbleLine = (svgNode, data, div) => { const tooltip = new Tooltip(div); data.sort((a, b) => b.total_layoff - a.total_layoff); xScale.domain(d3.extent(data, (d) => d.funds_raised)); const rScale = d3 .scaleSqrt() .domain([0, d3.max(data, (d) => d.total_layoff)]) .range([0, boundRect.height / 2]); const dataVis = svgNode .append("g") .attr("class", "dataVis") .style( "transform", `translate(${dimensions.marginLeft}px, ${boundRect.height / 2}px)` ) .selectAll("circle") .data(data) .join("circle") .classed("bubble", true) .attr("cx", (d) => xScale(d.funds_raised)) .attr("cy", 0) .attr("r", (d) => rScale(d.total_layoff)) .attr("fill", (d) => colorScale(d.avg_percentage)) .on("mouseover", (event, d) => { tooltip.display(d, tooltipContents); }) .on("mouseout", (event, d) => { tooltip.hide(); }) .on("mousemove", (event) => { tooltip.move(event); }); const axis = svgNode .append("g") .style( "transform", `translate(${dimensions.marginLeft}px, ${ dimensions.height - dimensions.marginBottom }px)` ) .call(d3.axisBottom(xScale)); }35:T7b9,visualizeCorrelation = (svgNode, data, div) => { const tooltip = new Tooltip(div); xScale.domain(d3.extent(data, (d) => d.funds_raised)); const yScale = d3 .scaleLinear() .domain(d3.extent(data, (d) => d.total_layoff)) .range([boundRect.height, 0]); // const colorScale = d3 // .scaleSequential(d3.interpolate("#c4e460", "#f08b5f")) // .domain([1, 100]); const x_axis = svgNode .append("g") .style( "transform", `translate(${dimensions.marginLeft / 5}px, ${ dimensions.height - dimensions.marginBottom }px)` ) .call(d3.axisBottom(xScale)); const y_axis = svgNode .append("g") .style( "transform", `translate(${boundRect.width + dimensions.marginLeft / 5}px, ${dimensions.marginTop}px)` ) .call(d3.axisRight(yScale)); const grid = svgNode .append("g") .attr("class", "grid-lines") .selectAll("line") .data(yScale.ticks()) .enter() .append("line") .attr("x1", 0) .attr("x2", boundRect.width) .attr("y1", (d) => yScale(d)) .attr("y2", (d) => yScale(d)) .style( "transform", `translate(${dimensions.marginLeft / 5}px, ${dimensions.marginTop}px)` ) .attr("stroke", "#ccc") .attr("stroke-width", 1); const dataVis = svgNode .append("g") .attr("class", "dataVis") .style( "transform", `translate(${dimensions.marginLeft / 5}px, ${dimensions.marginTop}px)` ) .selectAll("circle") .data(data) .join("circle") .classed("bubble", true) .attr("cx", (d) => xScale(d.funds_raised)) .attr("cy", (d) => yScale(d.total_layoff)) .attr("r", 5) .attr("fill", (d) => colorScale(d.avg_percentage)) .style("opacity", "0.85") .on("mouseover", (event, d) => { tooltip.display(d, tooltipContents); }) .on("mouseout", (event, d) => { tooltip.hide(); }) .on("mousemove", (event) => { tooltip.move(event); }); }36:Ta1d,### About Original Dataset Tech firms around the globe are fighting the economic slowdown. The slow consumer spending, higher interest rates by central banks and strong dollars overseas are hinting towards possible recession and tech firms have started laying employees off. This economic slowdown has made Meta recently fire 13% of its workforce, which amounts to more than 11,000 employees. This [dataset](https://www.kaggle.com/datasets/swaptr/layoffs-2022) was made with the hope to enable Kaggle community to look into analyzing recent tech turmoil and discover useful insights. Data obtained from the tech layoffs reported on the following platforms: - Bloomberg - San Francisco Business Times - TechCrunch - The New York Times Author: **Roger Lee** ### **Data Transformation** The original dataset recorded each **layoff event** as a separate row. This means that if a company conducted multiple layoffs between 2020 and 2025, it appeared multiple times in the data. For the purpose of this analysis, I **aggregated the data at the company level**. In the visualizations, **each circle represents a single company**, not individual layoff events. The **total number of laid-off employees** was summed for each company, and the **average percentage laid off** was calculated to provide a more holistic view of each company’s overall layoff impact. ### **Data Cleaning & Filtering** The dataset contained **incomplete records**, where some layoff events reported the *number of employees laid off* but omitted key information, such as the *percentage of the workforce affected*. To maintain data integrity and ensure meaningful comparisons, only records containing **complete information**—specifically, *funds raised*, *total number of employees laid off*, and *percentage laid off*—were included in the final analysis. ### **Funding Stage Classification** To simplify the dataset and enhance the clarity of the visualizations, I **grouped the companies** into broader **funding stage categories**: - **Publicly Traded**: Companies that have completed an **IPO**. - **Early Stage**: Companies in the **Seed**, **Series A**, or **Series B** funding rounds. - **Mid Stage**: Companies in **Series C** or **Series D** funding rounds. - **Late Stage**: Companies in **Series E** through **Series J** funding rounds. - **Privately Owned**: Includes companies that are **recently acquired**, backed by **private equity**, or operate as **subsidiaries** of other firms. - **Stage Unknown**: Companies labeled as **“Unknown”** or **“null”** in the original dataset.37:T498,style = 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31:{"id":"26d831eefb5cbb1b","slug":"tech-layoffs-analysis","title":"Tech Layoffs Analysis","thumbnail":"dd68decd4c56c99ddf5901ac2efd67b35f75db0012253d644a3dc0454f0052e6","access_level":"public","create_time":"$D2025-02-20T04:26:07.733Z","update_time":"$D2025-05-07T15:55:34.900Z","view_time":null,"first_public_time":"$D2025-03-06T16:27:06.490Z","fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"3bca0bfc26128f08","login":"shanfan","name":"Shanfan Huang","avatar_url":"https://avatars-next.observableusercontent.com/b9efc74ae4028ffed709d83651a51c10657689fa9e8819ecf1ae0fa07deff692","tier":"starter_2024"},"version":1232,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"abda57acd85fd431","login":"shanfan","name":"Shanfan Huang","avatar_url":"https://avatars-next.observableusercontent.com/b9efc74ae4028ffed709d83651a51c10657689fa9e8819ecf1ae0fa07deff692","tier":null}],"members":[{"type":"team","id":"3bca0bfc26128f08","login":"shanfan","name":"Shanfan Huang","avatar_url":"https://avatars-next.observableusercontent.com/b9efc74ae4028ffed709d83651a51c10657689fa9e8819ecf1ae0fa07deff692","tier":"starter_2024","role":"editor"},{"type":"individual","id":"abda57acd85fd431","login":"shanfan","name":"Shanfan Huang","avatar_url":"https://avatars-next.observableusercontent.com/b9efc74ae4028ffed709d83651a51c10657689fa9e8819ecf1ae0fa07deff692","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":{"id":"b73e972ce62d24d5","slug":"prototypes-fuzzy-work-viz","title":"Prototypes - Fuzzy Work Viz","owner":{"id":"3bca0bfc26128f08","login":"shanfan","name":"Shanfan Huang"}},"type":"notebook","body":{"title":"Tech Layoffs Analysis","cells":[{"id":347,"value":"# Tech Layoffs Analysis\n## between March 2020 and January 2025","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":0,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":843,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1140,"value":"colorScale = d3\n .scaleSequential(d3.interpolate(\"#c4e460\", \"#f08b5f\"))\n .domain([1, 100])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":934,"value":"\n\nThe circles in the charts are **slightly translucent**, allowing overlaps to create more intense color regions. When you see areas where red circles overlap, resulting in a **deep red hue**, it indicates that many companies in that group experienced layoffs **close to 100%**. This often suggests that several of these companies may have been **dissolved** or **completely shut down** as a result.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":980,"value":"### **Filter: Tech Companies Across 30 Industry Sectors**\n\nBy default, the charts below display **tech companies** across **30 different industry sectors**. Use the toggles below to **include or exclude** specific sectors and explore how layoff patterns vary across different industries.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1084,"value":"viewof selectAll = Inputs.button([\n [\n \"Select All\",\n () => {\n viewof industrySelection.value = allIndustries;\n // Trigger reactivity\n viewof industrySelection.dispatchEvent(new Event(\"input\"));\n }\n ],\n [\n \"Clear Selection\",\n () => {\n viewof industrySelection.value = [];\n // Trigger reactivity\n viewof industrySelection.dispatchEvent(new Event(\"input\"));\n }\n ]\n])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":998,"value":"viewof industrySelection = Inputs.checkbox(allIndustries, {\n label: html`Industries`,\n value: allIndustries,\n format: (x) =>\n html`

${x.industry} (${x.count})

`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1160,"value":"### X Axis: Average Funds Raised between 2020 and 2025 \n\nThe **X-axis** represents the average funds raised by each company, in **$ Millions**. By default, it uses a **linear scale**, but you can use the toggle below to switch to a **logarithmic scale** for a different perspective on the data distribution.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":921,"value":"viewof logScale = Inputs.toggle({\n label: html`Logarithmic Scale`,\n value: false\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1188,"value":"xScale = logScale\n ? d3.scaleLog().range([0, boundRect.width])\n : d3.scaleLinear().range([0, boundRect.width])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":914,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":632,"value":"# Chart 1. Severity of Layoffs at a Glance\n\nThis chart visualizes the severity of layoffs by representing each company as a circle, where:\n\n- The **size** of the circle reflects the **total number of employees laid off**, scaled within the company's cohort. \n- The **color** indicates the **percentage of employees laid off**, following a gradient from **green** (lower percentage) to **red** (higher percentage).\n- The **X-axis** displays the **funds raised** by each company, offering insight into the relationship between fundraising levels and layoff severity.\n\nA **large green circle** represents a company with a **high number of layoffs** but a **low percentage** relative to its total workforce. Conversely, a **small red circle** highlights a company where even a **smaller number of layoffs** constituted a **significant portion** of its staff.\n\nUse the **industry sector filters** above to explore how layoff patterns vary across different company cohorts.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":694,"value":"fundingSpread = {\n const containerDiv = htl.html`

`;\n const container = d3.select(containerDiv);\n allStages.forEach((d) => {\n const headerDiv = container\n .append(\"div\")\n .classed(\"header\", true)\n .html(d.header);\n\n const svgContainer = headerDiv.append(\"div\").classed(\"chart\", true);\n\n const toolTipDiv = svgContainer.append(\"div\").classed(\"tooltip\", true);\n\n const svg = svgContainer\n .append(\"svg\")\n .attr(\"id\", `${d.header.toLowerCase().replace(/\\s+/g, \"-\")}`)\n .attr(\"viewBox\", `0 0 ${dimensions.width} ${dimensions.height}`)\n .attr(\"width\", \"100%\")\n .attr(\"height\", \"100%\");\n\n const filteredData = d.data.filter((e) =>\n industrySelection.some((sel) => sel.industry === e.industry)\n );\n\n visualizeBubbleLine(svg, filteredData, toolTipDiv);\n });\n\n return containerDiv;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":761,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":634,"value":"# Chart 2. Funding and Layoffs\n\nThis chart builds upon the previous one by plotting the **exact number of employees laid off** along the **Y-axis**, allowing for a deeper exploration of the relationship between a company cohort’s **funding** and its **layoff patterns**. \n\nBy mapping both **funds raised** (X-axis) and **total layoffs** (Y-axis), this chart offers clearer insights into how funding levels may influence the scale of layoffs across different company cohorts.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":792,"value":"fundingCorrelation = {\n const containerDiv = htl.html`

`;\n const container = d3.select(containerDiv);\n\n container\n .style(\"display\", \"grid\")\n .style(\"grid-template-columns\", \"1fr 1fr\")\n .style(\"gap\", \"0.5em\");\n\n allStages.forEach((d) => {\n const headerDiv = container\n .append(\"div\")\n .classed(\"header\", true)\n .html(d.header);\n\n const svgContainer = headerDiv.append(\"div\").classed(\"chart\", true);\n\n const toolTipDiv = svgContainer.append(\"div\").classed(\"tooltip\", true);\n\n const svg = svgContainer\n .append(\"svg\")\n .attr(\"viewBox\", `0 0 ${dimensions.width} ${dimensions.height}`)\n .attr(\"width\", \"100%\")\n .attr(\"height\", \"100%\");\n\n const filteredData = d.data.filter((e) =>\n industrySelection.some((sel) => sel.industry === e.industry)\n );\n\n visualizeCorrelation(svg, filteredData, toolTipDiv);\n });\n\n return containerDiv;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":797,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"\n\n# Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1167,"value":"$36","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1174,"value":"\n---\n\n### Visual elements:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":125,"value":"$37","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":14,"value":"dimensions = {\n return {\n width: 800,\n height: 250,\n marginTop: 20,\n marginLeft: 50,\n marginRight: 50,\n marginBottom: 20\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"boundRect = {\n return {\n width: dimensions.width - dimensions.marginLeft - dimensions.marginRight,\n height: dimensions.height - dimensions.marginTop - dimensions.marginBottom\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":502,"value":"### Functions & Helpers:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":409,"value":"rollupCompanies = (data) => {\n return Array.from(\n d3.rollup(\n data,\n (v) => {\n const totalLayoffSum = d3.sum(v, (d) => d.total_layoff);\n const avgPercentage = d3.mean(v, (d) =>\n parseFloat(d.percentage_layoff)\n );\n const avgFundsRaised = d3.mean(v, (d) => d.funds_raised);\n const { stage, industry, country } = v[0];\n return {\n total_layoff: totalLayoffSum,\n avg_percentage: avgPercentage,\n funds_raised: avgFundsRaised,\n stage,\n industry,\n country\n };\n },\n (d) => d.company\n ),\n\n ([company, values]) => ({ company, ...values })\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":857,"value":"function tooltipContents(datum) {\n const { company, funds_raised, total_layoff, avg_percentage, industry } =\n datum;\n return `Company: ${company}
\n Funds Raised: $${funds_raised}M
\n Total Layoff: ${total_layoff}
\n Avg % Laid-off: ${avg_percentage}%
\n Sector: ${industry}\n `;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":555,"value":"import { Tooltip } from \"@clhenrick/tooltip-component\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":83,"value":"### Datasets:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":990,"value":"allIndustries = {\n const combined = d3.merge([\n dataEarlyStage,\n dataMidStage,\n dataLateStage,\n dataPrivatelyOwned,\n dataPublic,\n dataStageUnknown\n ]);\n\n const industryCounts = d3.rollup(\n combined,\n (v) => v.length,\n (d) => d.industry\n );\n\n const industryCountsArray = Array.from(\n industryCounts,\n ([industry, count]) => ({ industry, count })\n );\n\n industryCountsArray.sort((a, b) => d3.ascending(a.industry, b.industry));\n\n return industryCountsArray;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":814,"value":"allStages = [\n { header: \"Publicly Traded\", data: rollupCompanies(dataPublic) },\n { header: \"Early Stage\", data: rollupCompanies(dataEarlyStage) },\n { header: \"Mid Stage\", data: rollupCompanies(dataMidStage) },\n { header: \"Late Stage\", data: rollupCompanies(dataLateStage) },\n { header: \"Privately Owned\", data: rollupCompanies(dataPrivatelyOwned) },\n { header: \"Stage Unknown\", data: rollupCompanies(dataStageUnknown) }\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"dataPrivatelyOwned = FileAttachment(\"PrivatelyOwned@2.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":88,"value":"dataPublic = FileAttachment(\"PostIPO@1.csv\").csv({ typed: true });","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"dataEarlyStage = FileAttachment(\"EarlyStage@2.csv\").csv({ typed: true });","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":270,"value":"dataMidStage = FileAttachment(\"MidStage@1.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":277,"value":"dataLateStage = FileAttachment(\"LateStage@1.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":279,"value":"dataStageUnknown = FileAttachment(\"StageUnknown@1.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W38","stdlib":"1","titling":"auto"}} 32:"$31:body"