# BLS Occupation Projections Scraper (`parseforge/bls-occupation-projections-scraper`) Actor Export the U.S. Bureau of Labor Statistics 2024-2034 occupational projections for 1,100+ occupations. Pull SOC code, occupation title, employment 2024 and 2034, numeric and percent change, annual openings, median annual wage, typical education, work experience, and OOH link. CSV, JSON, Excel, XML. - **URL**: https://apify.com/parseforge/bls-occupation-projections-scraper.md - **Developed by:** [ParseForge](https://apify.com/parseforge) (community) - **Categories:** Jobs, Other - **Stats:** 2 total users, 1 monthly users, 80.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ![ParseForge Banner](https://github.com/ParseForge/apify-assets/blob/ad35ccc13ddd068b9d6cba33f323962e39aed5b2/banner.jpg?raw=true) ## ๐Ÿ’ผ BLS Occupation Projections Scraper > ๐Ÿš€ **Export U.S. BLS 2024-2034 occupational projections in seconds.** SOC code, occupation title, 2024 and 2034 employment, numeric and percent change, annual openings, median wage, typical education, and work experience - straight to CSV, Excel, JSON, or XML. > ๐Ÿ•’ **Last updated:** 2026-05-26 ยท **๐Ÿ“Š 16 fields** per record ยท **1,100+ occupations** ยท **2024-2034 projection horizon** The BLS Occupation Projections Scraper turns the public [Bureau of Labor Statistics Occupational Projections and Worker Characteristics table](https://www.bls.gov/emp/tables/occupational-projections-and-characteristics.htm) into a clean, structured dataset. It hits the same static HTML page the BLS publishes and parses every row - Summary, Major, Minor, Broad, and Detailed SOC levels - into a flat record per occupation. Coverage spans the complete 2024-2034 employment projection: all 1,100+ occupations the BLS National Employment Matrix tracks, with employment counts (in thousands), growth numbers (numeric and percent), annual openings, median annual wage (USD), and the typical education and experience requirements for entry. | ๐ŸŽฏ Target Audience | ๐Ÿ’ก Primary Use Cases | |---|---| | ๐ŸŽ“ Career counselors | Recommend high-growth, high-wage occupations | | ๐Ÿ“Š Workforce analysts | Track decade-out projections by SOC sector | | ๐Ÿข HR / talent teams | Plan hiring against projected supply changes | | ๐Ÿ“ฐ Journalists | Cite official BLS figures for occupation trend pieces | | ๐Ÿค– AI / ML teams | Train a career-recommendation engine on official numbers | | ๐Ÿ›๏ธ Policy researchers | Model labor market shifts by sector | ### ๐Ÿ“‹ What the BLS Occupation Projections Scraper does - Fetches the public BLS Occupational Projections HTML table. - Parses every row (Summary, Major, Minor, Broad, and Detailed levels). - Flattens each occupation into a 16-field record with `occupationTitle` first and `error` last. - Applies optional filters: `occupationType`, `minMedianWage`, `minEmploymentChangePercent`. - Exports as CSV, Excel, JSON, JSONL, XML, RSS, or HTML. > ๐Ÿ’ก **Why it matters:** the BLS publishes the projections as a 70-column wide HTML table that is painful to copy by hand. This actor flattens it into a structured dataset you can query, filter, and join. ### ๐ŸŽฌ Full Demo _๐Ÿšง Coming soon._ ### โš™๏ธ Input
FieldTypeRequiredDescription
maxItemsintegerNoFree users: 10. Paid users: up to 1,000,000. The BLS table has ~1,100 rows total. Prefill: 10.
occupationTypeenumNoFilter by SOC hierarchy level: Summary, Major, Minor, Broad, Detailed, Line item. Leave empty for all.
minMedianWageintegerNoSkip occupations whose median annual wage is below this value (USD).
minEmploymentChangePercentnumberNoSkip occupations growing slower than this percent over 2024-2034.
**Example 1 - every detailed occupation:** ```json { "maxItems": 1000, "occupationType": "Detailed" } ```` **Example 2 - high-growth, high-wage occupations:** ```json { "maxItems": 200, "minMedianWage": 80000, "minEmploymentChangePercent": 5 } ``` > โš ๏ธ **Good to Know:** employment figures (`employment2024`, `employment2034`, `employmentChangeNumeric`, `annualOpenings`) are in **thousands**. `medianAnnualWage` is in USD. A `-` value in the source table is normalised to `null`. ### ๐Ÿ“Š Output Each record is a flat object. `occupationTitle` is first, `error` is last. | Field | Type | Description | |---|---|---| | ๐Ÿ’ผ `occupationTitle` | string | Occupation title (BLS National Employment Matrix). | | ๐Ÿ†” `socCode` | string | Standard Occupational Classification code. | | ๐Ÿท๏ธ `occupationType` | string | SOC hierarchy level: Summary, Major, Minor, Broad, Detailed, Line item. | | ๐Ÿ‘ท `employment2024` | number | Employment in 2024, in thousands. | | ๐Ÿ‘ท `employment2034` | number | Projected employment in 2034, in thousands. | | ๐Ÿ“ˆ `employmentChangeNumeric` | number | Numeric change 2024-2034, in thousands. | | ๐Ÿ“ˆ `employmentChangePercent` | number | Percent change 2024-2034. | | ๐Ÿง‘โ€๐Ÿ’ผ `percentSelfEmployed` | number | Percent self-employed, 2024. | | ๐Ÿ“… `annualOpenings` | number | Annual average occupational openings 2024-2034, in thousands. | | ๐Ÿ’ฐ `medianAnnualWage` | number | Median annual wage, USD, 2024. | | ๐ŸŽ“ `typicalEducation` | string | Typical education needed for entry. | | ๐Ÿ› ๏ธ `workExperience` | string | Work experience in a related occupation. | | ๐Ÿ“š `onJobTraining` | string | Typical on-the-job training needed. | | ๐Ÿ”— `oohUrl` | string | Link to the Occupational Outlook Handbook page. | | ๐Ÿ”— `sourceUrl` | string | The BLS source URL. | | ๐Ÿ•’ `scrapedAt` | ISO 8601 | When this row was scraped. | | โŒ `error` | string|null | Per-record error (null on success). | **Sample record:** ```json { "occupationTitle": "Marketing managers", "socCode": "11-2021", "occupationType": "Detailed", "employment2024": 414.5, "employment2034": 441.7, "employmentChangeNumeric": 27.2, "employmentChangePercent": 6.6, "annualOpenings": 35.9, "medianAnnualWage": 161030, "typicalEducation": "Bachelor's degree", "workExperience": "5 years or more", "onJobTraining": "None", "oohUrl": "https://www.bls.gov/ooh/management/advertising-promotions-and-marketing-managers.htm", "sourceUrl": "https://www.bls.gov/emp/tables/occupational-projections-and-characteristics.htm" } ``` ### โœจ Why choose this Actor | โœจ Feature | What it gets you | |---|---| | ๐Ÿ‡บ๐Ÿ‡ธ Official BLS source | Straight from `bls.gov`, no third-party scraping. | | ๐Ÿ“Š 1,100+ occupations | Every SOC level the BLS publishes. | | ๐Ÿ“… 2024-2034 horizon | Latest decade projection. | | ๐Ÿ’ฐ Wage + education + experience | Filter by what matters to you. | | ๐Ÿ”— OOH links included | Jump straight to the official career page for any row. | | ๐Ÿ†“ No API key | Public government data. | ### ๐Ÿ“ˆ How it compares to alternatives | Approach | Free? | Fields | Coverage | Effort | |---|---|---|---|---| | Manually copy from bls.gov | โœ… | All | All | Hours of clicking | | BLS Public Data API | โœ… | Time-series only | Per-series, complex query | API key + series ID lookup | | Commercial labor data vendor | โŒ | Full | Full | Subscription + integration | | **BLS Occupation Projections Scraper** | โœ… ($5 trial credit) | **16** | **All 1,100+ occupations** | **1 click** | ### ๐Ÿš€ How to use 1. **Sign up.** [Create a free Apify account w/ $5 credit](https://console.apify.com/sign-up?fpr=vmoqkp). 2. **Open** the BLS Occupation Projections Scraper page. 3. **Set inputs.** Set `maxItems` and any filters. 4. **Run.** Click Start. Free users get 10 items, paid users up to 1,000,000. 5. **Export.** Download the dataset as CSV, Excel, JSON, JSONL, XML, RSS, or HTML. ### ๐Ÿ’ผ Business use cases #### ๐ŸŽ“ Career advisory products Power a "best jobs of the decade" widget on a career-advice site: filter for `minEmploymentChangePercent: 10` and `minMedianWage: 70000`, then surface the top 25. #### ๐Ÿ“Š Workforce planning Pull every Detailed-level occupation, join against your internal headcount by SOC, and forecast hiring gaps decade-out. #### ๐Ÿค– AI career recommender Use the dataset as a feature store for a career-recommendation model - wages, growth, education, experience, all standardised. #### ๐Ÿ“ฐ Editorial / journalism Cite official BLS figures with a single dataset URL instead of screenshotting a 70-column HTML table. ### ๐Ÿ”Œ Automating BLS Occupation Projections Scraper - **Make (Integromat)** - annual refresh when BLS publishes new projections. - **Zapier** - push to Google Sheets on a schedule. - **Slack** - share the dataset link with a team channel. - **Airbyte** - sync into your warehouse. - **GitHub Actions** - commit annual snapshots to a repo. - **Google Drive / Dropbox / S3** - drop the export into a folder. - **Webhooks** - POST dataset URLs anywhere. ### ๐ŸŒŸ Beyond business use cases #### ๐Ÿ”ฌ Research Compare 10-year projection accuracy across BLS releases. Study which sectors over- or under-projected. #### ๐ŸŽฎ Personal projects "Should I switch careers?" calculators, side-by-side career comparison tools. #### ๐Ÿค Non-profit / education Teach students about the labor market with real BLS numbers. Workforce nonprofits can baseline interventions against projections. #### ๐Ÿงช Experimentation Cluster occupations by growth + wage + education to discover non-obvious peer groups. ### ๐Ÿค– Ask an AI assistant about this scraper Paste this into ChatGPT, Claude, Perplexity, or Copilot: > "Using the ParseForge BLS Occupation Projections Scraper dataset (filtered to Detailed-level occupations), shortlist the top 20 careers by employment growth percent that require only a Bachelor's degree and pay above $80,000 median." ### โ“ Frequently Asked Questions #### ๐Ÿ”‘ Do I need a BLS API key? No. The actor uses the public HTML table on bls.gov. No API key, no registration. #### ๐Ÿ“… What projection horizon does this cover? 2024-2034 - the BLS's current 10-year projection. #### ๐Ÿงฎ Are employment numbers in absolute jobs? No - they are in **thousands**, as published by BLS. Multiply by 1,000 for raw counts. #### ๐Ÿ’ฐ What currency is the median wage? USD, 2024 dollars. #### ๐Ÿ†“ What do free users get? Up to 10 items per run on a $5 trial credit. Paid users can pull up to 1,000,000 items (the full table is ~1,100 rows). #### ๐Ÿ”„ How often is the data refreshed? Live on every run, but BLS itself updates the table on an annual cycle. The projection horizon shifts every release. #### ๐Ÿท๏ธ What does `occupationType` mean? SOC hierarchy level: Summary (totals), Major (2-digit), Minor (3-digit), Broad (4-digit), Detailed (6-digit), or Line item (special rollups). #### ๐Ÿ”— What is the `oohUrl`? Link to the BLS Occupational Outlook Handbook page for that occupation - duties, work environment, pay distribution, similar occupations. #### ๐ŸŒ Does this use proxies? No proxies needed. bls.gov is a public government site. #### โš ๏ธ Is this affiliated with the BLS? No. This is an independent tool. It is not endorsed by or affiliated with the U.S. Bureau of Labor Statistics or the Department of Labor. #### ๐Ÿ“ฅ What export formats are available? CSV, Excel (XLSX), JSON, JSONL, XML, RSS, and HTML. ### ๐Ÿ”Œ Integrate with any app Apify natively integrates with Make, Zapier, n8n, Slack, Discord, Airbyte, Keboola, Google Drive, Google Sheets, Dropbox, AWS S3, GitHub, GitLab, generic webhooks, and the Apify API / SDK (Python, JavaScript, CLI). ### ๐Ÿ”— Recommended Actors | Actor | What it does | |---|---| | [OurAirports Database Scraper](https://apify.com/parseforge/ourairports-scraper) | Global airport reference dataset. | | [MagicBricks India Scraper](https://apify.com/parseforge/magicbricks-india-scraper) | Indian property listings, sale + rent. | | [GitHub Repo Scraper](https://apify.com/parseforge/github-repo-scraper) | Repo metadata, stars, releases. | | [CurseForge Mods Scraper](https://apify.com/parseforge/curseforge-mods-scraper) | Minecraft mod metadata. | | [Steam Store Scraper](https://apify.com/parseforge/steam-store-scraper) | Game prices and metadata. | > ๐Ÿ’ก **Pro Tip:** browse the complete [ParseForge collection](https://apify.com/parseforge) for more reference and labor-market scrapers. **๐Ÿ†˜ Need Help?** [Open our contact form](https://tally.so/r/BzdKgA) > **โš ๏ธ Disclaimer:** this is an independent tool, not affiliated with the U.S. Bureau of Labor Statistics or the Department of Labor. Only publicly available data from bls.gov is collected. # Actor input Schema ## `maxItems` (type: `integer`): Free users: Limited to 10 items (preview). Paid users: Optional, max 1,000,000. The BLS table has ~1,100 occupations total. ## `occupationType` (type: `string`): Filter by SOC hierarchy level. ## `minMedianWage` (type: `integer`): Skip occupations whose median annual wage is below this value. ## `minEmploymentChangePercent` (type: `number`): Skip occupations growing slower than this percent over 2024-2034. ## Actor input object example ```json { "maxItems": 10, "occupationType": "" } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("parseforge/bls-occupation-projections-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "maxItems": 10 } # Run the Actor and wait for it to finish run = client.actor("parseforge/bls-occupation-projections-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "maxItems": 10 }' | apify call parseforge/bls-occupation-projections-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=parseforge/bls-occupation-projections-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "BLS Occupation Projections Scraper", "description": "Export the U.S. Bureau of Labor Statistics 2024-2034 occupational projections for 1,100+ occupations. Pull SOC code, occupation title, employment 2024 and 2034, numeric and percent change, annual openings, median annual wage, typical education, work experience, and OOH link. CSV, JSON, Excel, XML.", "version": "0.1", "x-build-id": "eCAfdfH3GzfyQIltZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/parseforge~bls-occupation-projections-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-parseforge-bls-occupation-projections-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/parseforge~bls-occupation-projections-scraper/runs": { "post": { "operationId": "runs-sync-parseforge-bls-occupation-projections-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/parseforge~bls-occupation-projections-scraper/run-sync": { "post": { "operationId": "run-sync-parseforge-bls-occupation-projections-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 1000000, "type": "integer", "description": "Free users: Limited to 10 items (preview). Paid users: Optional, max 1,000,000. The BLS table has ~1,100 occupations total." }, "occupationType": { "title": "Occupation Type", "enum": [ "", "Summary", "Major", "Minor", "Broad", "Detailed", "Line item" ], "type": "string", "description": "Filter by SOC hierarchy level.", "default": "" }, "minMedianWage": { "title": "Minimum Median Wage (USD)", "minimum": 0, "maximum": 1000000, "type": "integer", "description": "Skip occupations whose median annual wage is below this value." }, "minEmploymentChangePercent": { "title": "Minimum % Employment Change", "type": "number", "description": "Skip occupations growing slower than this percent over 2024-2034." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```