# ETA Labor Market Intelligence (`ryanclinton/eta-labor-market-intel`) Actor US labor market intelligence from 4 DOL Employment & Training Administration datasets: weekly unemployment claims, apprenticeship programs, and trade adjustment assistance. - **URL**: https://apify.com/ryanclinton/eta-labor-market-intel.md - **Developed by:** [ryan clinton](https://apify.com/ryanclinton) (community) - **Categories:** Other, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $20.00 / 1,000 result returneds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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 ## ETA Labor Market Intelligence US labor market intelligence from 4 DOL Employment & Training Administration datasets: weekly unemployment claims, apprenticeship programs, and trade adjustment assistance. ### What does this actor do? This actor queries the DOL ETA datasets to build a comprehensive labor market intelligence report: 1. **Unemployment Insurance Claims (National)** — Weekly initial and continued claims, insured unemployment rate, week-over-week changes, 4-week moving averages 2. **Unemployment Insurance Claims (State)** — State-level breakdown with summaries per state including average rates and latest data 3. **Registered Apprenticeship Programs** — Apprentice demographics, occupations, sponsors, completion rates by state and fiscal year 4. **Trade Adjustment Assistance (TAA) Petitions** — Companies petitioning for worker retraining due to trade displacement, with certification decisions and worker counts Each report includes a **market health score** (0–100) based on unemployment trends, apprenticeship health, and trade displacement indicators. ### Input parameters | Parameter | Type | Description | |-----------|------|-------------| | `apiKey` | string | DOL API key (free at https://dataportal.dol.gov/registration) | | `dryRun` | boolean | Return sample data without API calls (default: true unless apiKey provided) | | `searchMode` | string | `all`, `unemployment`, `apprenticeship`, or `tradeAdjustment` | | `state` | string | Two-letter state abbreviation filter | | `dateFrom` | string | Filter from date (YYYY-MM-DD) | | `dateTo` | string | Filter to date (YYYY-MM-DD) | | `companyName` | string | Search TAA petitions by company name | | `occupation` | string | Filter apprenticeship by occupation | | `includeStateBreakdown` | boolean | Include state-level UI data (default: true) | | `includeApprenticeship` | boolean | Include apprenticeship data (default: true) | | `includeTradeAdjustment` | boolean | Include TAA petitions (default: true) | | `maxResults` | integer | Max records per dataset (1–5000, default: 200) | ### Output format The actor outputs a single comprehensive report: ```json { "reportType": "comprehensive", "generatedAt": "2024-12-07T...", "unemployment": { "nationalTrend": [ { "weekEnded": "2024-12-07", "initialClaims": 224000, "continuedClaims": 1871000, "insuredUnemploymentRate": 1.2, "weekOverWeekChange": -11000, "fourWeekMovingAverage": 230000 } ], "stateBreakdown": [ { "state": "CA", "totalInitialClaims": 42000, "averageInsuredRate": 2.1 } ], "trendDirection": "Slightly declining", "trendDescription": "..." }, "apprenticeship": { "records": [...], "summary": { "totalActiveApprentices": 8000, "overallCompletionRate": 71.01, "topOccupations": [...] } }, "tradeAdjustment": { "records": [...], "summary": { "totalPetitions": 3, "totalCertified": 2, "totalWorkersAffected": 625, "certificationRate": 66.67 } }, "marketInsight": { "score": 62, "level": "Moderate", "factors": [ "Low insured unemployment rate: 1.2% (+15)", "Initial claims declining: -11,000 week-over-week (+10)" ] } } ```` ### Market health scoring The market health score (0–100) assesses labor market conditions: | Factor | Impact | |--------|--------| | Insured unemployment rate >3% | -20 | | Insured unemployment rate >2% | -10 | | Insured unemployment rate <1% | +15 | | Claims surging (>50K week-over-week) | -15 | | Claims declining (>10K drop) | +10 | | High initial claims (>300K) | -15 | | Low initial claims (<200K) | +10 | | Many states >3% IUR | -5 to -10 | | Strong apprenticeship completion rate | +8 | | High new apprenticeship enrollment | +5 | | Large trade displacement (>50K workers) | -12 | | High TAA certification rate | -5 | **Levels:** Strong (75+), Moderate (50–74), Weak (25–49), Stressed (<25) ### Use cases - **Labor economists**: Track weekly unemployment trends with moving averages - **Workforce development**: Monitor apprenticeship program performance and demographics - **Trade analysts**: Track industries and companies affected by trade displacement - **Policy researchers**: Analyze labor market health across states and time periods - **HR professionals**: Understand labor market conditions for hiring planning - **Economic journalists**: Track claims trends and trade adjustment patterns ### API key Register for a free DOL API key at https://dataportal.dol.gov/registration Without an API key, the actor returns realistic sample data in dry run mode. ### Data sources All data from the US Department of Labor Employment & Training Administration: - `ETA/ui_national_weekly_claims` — National weekly UI claims (updated weekly) - `ETA/ui_state_weekly_claims` — State-level weekly UI claims - `ETA/apprenticeship_data` — Registered apprenticeship program data - `ETA/otaa_petition` — Trade Adjustment Assistance petitions # Actor input Schema ## `apiKey` (type: `string`): Your DOL Open Data Portal API key. Register free at https://dataportal.dol.gov/registration ## `dryRun` (type: `boolean`): Return sample data without calling the API. Default: true (unless apiKey provided) ## `searchMode` (type: `string`): Which data to fetch: unemployment claims, apprenticeship, trade adjustment, or all ## `state` (type: `string`): Filter by two-letter state abbreviation ## `dateFrom` (type: `string`): Filter data from this date (YYYY-MM-DD) ## `dateTo` (type: `string`): Filter data up to this date (YYYY-MM-DD) ## `companyName` (type: `string`): Search Trade Adjustment Assistance petitions by company name (partial match) ## `occupation` (type: `string`): Filter apprenticeship records by occupation title (partial match) ## `includeStateBreakdown` (type: `boolean`): Include state-level unemployment claims breakdown ## `includeApprenticeship` (type: `boolean`): Include registered apprenticeship program data ## `includeTradeAdjustment` (type: `boolean`): Include Trade Adjustment Assistance petition data ## `maxResults` (type: `integer`): Maximum records per dataset (1-5000) ## Actor input object example ```json { "dryRun": true, "searchMode": "all", "includeStateBreakdown": true, "includeApprenticeship": true, "includeTradeAdjustment": true, "maxResults": 200 } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("ryanclinton/eta-labor-market-intel").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 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); }); // 📚 Want to learn more 📖? Go to → 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 = {} # Run the Actor and wait for it to finish run = client.actor("ryanclinton/eta-labor-market-intel").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call ryanclinton/eta-labor-market-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ryanclinton/eta-labor-market-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ETA Labor Market Intelligence", "description": "US labor market intelligence from 4 DOL Employment & Training Administration datasets: weekly unemployment claims, apprenticeship programs, and trade adjustment assistance.", "version": "0.1", "x-build-id": "MBgHZnagXmT6Tn8z6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ryanclinton~eta-labor-market-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ryanclinton-eta-labor-market-intel", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ryanclinton~eta-labor-market-intel/runs": { "post": { "operationId": "runs-sync-ryanclinton-eta-labor-market-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ryanclinton~eta-labor-market-intel/run-sync": { "post": { "operationId": "run-sync-ryanclinton-eta-labor-market-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "apiKey": { "title": "DOL API Key", "type": "string", "description": "Your DOL Open Data Portal API key. Register free at https://dataportal.dol.gov/registration" }, "dryRun": { "title": "Dry Run", "type": "boolean", "description": "Return sample data without calling the API. Default: true (unless apiKey provided)", "default": true }, "searchMode": { "title": "Search Mode", "enum": [ "all", "unemployment", "apprenticeship", "tradeAdjustment" ], "type": "string", "description": "Which data to fetch: unemployment claims, apprenticeship, trade adjustment, or all", "default": "all" }, "state": { "title": "State", "type": "string", "description": "Filter by two-letter state abbreviation" }, "dateFrom": { "title": "Date From", "type": "string", "description": "Filter data from this date (YYYY-MM-DD)" }, "dateTo": { "title": "Date To", "type": "string", "description": "Filter data up to this date (YYYY-MM-DD)" }, "companyName": { "title": "Company Name (TAA)", "type": "string", "description": "Search Trade Adjustment Assistance petitions by company name (partial match)" }, "occupation": { "title": "Occupation (Apprenticeship)", "type": "string", "description": "Filter apprenticeship records by occupation title (partial match)" }, "includeStateBreakdown": { "title": "Include State Breakdown", "type": "boolean", "description": "Include state-level unemployment claims breakdown", "default": true }, "includeApprenticeship": { "title": "Include Apprenticeship", "type": "boolean", "description": "Include registered apprenticeship program data", "default": true }, "includeTradeAdjustment": { "title": "Include Trade Adjustment", "type": "boolean", "description": "Include Trade Adjustment Assistance petition data", "default": true }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum records per dataset (1-5000)", "default": 200 } } }, "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 } } } } } } } } } } ```