# SalaryBench IQ (`constructive_calm/salarybench-iq`) Actor SalaryBench IQ turns official US Department of Labor wage disclosure filings into defensible salary benchmarks, percentile ranges, sample wage records, and employer pay intelligence. - **URL**: https://apify.com/constructive\_calm/salarybench-iq.md - **Developed by:** [Omar Eldeeb](https://apify.com/constructive_calm) (community) - **Categories:** Developer tools, News, Jobs - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 2 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing Pay per event 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 ## SalaryBench IQ SalaryBench IQ turns official US Department of Labor wage disclosure data into defensible salary benchmarks, sample wage records, and employer pay intelligence for compensation teams, recruiters, immigration counsel, and founders. The actor reads H-1B, H-1B1, E-3, and PERM public filing files, normalizes wage units into annual USD, and returns either percentile benchmark rows or individual salary sample filings. This is useful when you need salary ranges grounded in employer-filed wage records rather than self-reported compensation surveys. ### What This Actor Does - Computes P10, P25, median, P75, and P90 salary benchmarks for a role. - Supports nationwide, state, city, and employer-specific salary filters. - Returns top local buckets when no city or state is supplied, with an all-US benchmark first. - Returns raw salary sample filings for auditability and CSV exports. - Converts hourly, weekly, biweekly, and monthly wages into annual USD. - Supports H-1B, H-1B1, E-3, and PERM green-card labor certification wage data. - Discovers available DOL files at run time and uses run storage for cache-friendly downloads when available. ### Data Sources The actor uses official Department of Labor Office of Foreign Labor Certification disclosure files: https://www.dol.gov/agencies/eta/foreign-labor/performance H-1B, H-1B1, and E-3 records come from LCA disclosure files. PERM records come from permanent labor certification disclosure files. ### Input #### Which Mode Should You Use? Use `salary-samples` when you need exact salary data. This mode returns individual DOL filing records with employer, job title, work location, wage offered, wage unit, decision date, and source file. Use `salary-benchmark` when you need market ranges. This mode scans matching DOL source filings, groups them by location, and returns percentile rows such as P10, P25, median, P75, and P90. The actor may scan thousands or millions of source rows to calculate those percentiles, but it only writes up to `maxItems` benchmark rows to the dataset. The prefilled marketplace run uses `salary-samples`, H-1B only, the latest available fiscal year, and a small `maxItems` value. That keeps first tests and Apify automated quality checks fast. Switch to `salary-benchmark`, add PERM, or add more fiscal years when you intentionally want deeper benchmark coverage. #### Modes | Mode | Description | | --- | --- | | `salary-benchmark` | Aggregated salary ranges. Scans source filings first, then returns percentile rows for the requested role and location. | | `salary-samples` | Exact salary records. Returns individual wage filings matching the same filters. | #### Filters | Field | Description | | --- | --- | | `role` | Required job title keyword, such as `software engineer`, `data scientist`, or `registered nurse`. | | `employer` | Optional company keyword for company-specific salary benchmarks. | | `city` | Optional worksite city. If set, benchmark rows are city-specific. | | `state` | Optional two-letter state code. If set without city, benchmark rows are state-specific. | | `visaTypes` | Any mix of `H-1B`, `H-1B1`, `E-3`, and `PERM`. Defaults to `H-1B`; add `PERM` when you need green-card wage data. | | `decisions` | Filing outcomes to include. Defaults to `Approved`. | | `fiscalYears` | US fiscal years. Leave empty for the latest available year. | | `minSalary` | Minimum annualized offered salary in USD. | | `maxSalary` | Maximum annualized offered salary in USD. | | `maxItems` | Maximum dataset output rows. It does not limit source rows scanned in benchmark mode. | | `newFilingsOnly` | For `salary-samples`, skip filings at or before the saved decision-date watermark. | | `stateKeyValueStoreId` | Optional key-value store for persistent `newFilingsOnly` watermarks across runs. | ### Example Inputs #### Nationwide Benchmark ```json { "mode": "salary-benchmark", "role": "data scientist", "visaTypes": ["H-1B"], "decisions": ["Approved"], "fiscalYears": ["2024"], "maxItems": 5 } ```` #### State Benchmark ```json { "mode": "salary-benchmark", "role": "software engineer", "state": "CA", "visaTypes": ["H-1B", "PERM"], "decisions": ["Approved"], "fiscalYears": ["2024"] } ``` #### Raw Salary Samples ```json { "mode": "salary-samples", "role": "registered nurse", "state": "TX", "visaTypes": ["H-1B"], "decisions": ["Approved"], "fiscalYears": ["2024"], "maxItems": 25 } ``` ### Output Every dataset item includes a `type` field. #### `salary-distribution` Returned by `salary-benchmark`. ```json { "type": "salary-distribution", "role": "data scientist", "city": null, "state": null, "count": 6467, "p10Salary": 85540, "p25Salary": 107682, "medianSalary": 135886, "p75Salary": 168301, "p90Salary": 199234, "topEmployer": "WAL-MART ASSOCIATES, INC.", "visaTypeBreakdown": { "H-1B": 6467 }, "scrapedAt": "2026-05-13T00:00:00.000Z" } ``` When no city or state is supplied, the first row is the all-US benchmark and the remaining rows are the top local buckets by filing count. #### `filing` Returned by `salary-samples`. ```json { "type": "filing", "caseNumber": "I-200-24267-357561", "visaType": "H-1B", "decision": "Approved", "decisionDate": "2024-09-30", "filedDate": "2024-09-23", "fiscalYear": 2024, "employer": "GOOGLE LLC", "jobTitle": "Software Engineer", "socCode": "15-1252.00", "wageOffered": 188601, "wageUnit": "year", "wageOfferedRaw": 188601, "prevailingWage": 172931, "prevailingWageLevel": "II", "numberOfWorkers": 1, "workCity": "Sunnyvale", "workState": "CA", "sourceProgram": "LCA", "sourceFile": "LCA_Disclosure_Data_FY2024_Q4.xlsx", "sourceUrl": "https://www.dol.gov/sites/dolgov/files/ETA/oflc/pdfs/LCA_Disclosure_Data_FY2024_Q4.xlsx", "scrapedAt": "2026-05-13T00:00:00.000Z" } ``` ### Pricing This actor is configured for pay-per-event usage in `.actor/pay_per_event.json`. | Event | Price | When it is charged | | --- | --- | --- | | Actor start | $0.01 | Synthetic start event charged automatically by Apify when a run starts. | | Salary benchmark row | $0.004 | Each percentile benchmark row returned. | | Salary sample filing | $0.003 | Each individual salary sample filing returned. | The first 10 chargeable output events in each run are treated as a free trial inside the actor logic before `Actor.charge()` is called. ### Notes And Limitations - Salaries are official DOL filing wages, not total compensation. They do not include bonus, stock, or benefits. - When DOL publishes hourly, weekly, biweekly, or monthly wages, this actor annualizes them for comparison. - For wage ranges, the actor uses the lower offered wage field as the normalized offered salary. - Filing volumes vary heavily by role and location. Small counts should be treated as directional, not definitive. - Latest available data depends on when DOL publishes quarterly disclosure files. ### Support For issues or feature requests, use the actor's Apify listing or contact omar.eldeeb@remotegrowthpartners.com. # Actor input Schema ## `mode` (type: `string`): Need exact salary records? Choose Raw salary sample filings. The prefilled run uses this mode because it returns a quick sample and stops at Max output rows. Need market ranges? Choose Benchmark percentiles; this scans matching DOL source filings first, then returns aggregated P10/P25/median/P75/P90 salary rows. ## `role` (type: `string`): Job title keyword to benchmark. Examples: software engineer, data scientist, product manager, registered nurse. ## `employer` (type: `string`): Optional company keyword for employer-specific salary benchmarks. Examples: Google, Amazon, Infosys. ## `city` (type: `string`): Optional worksite city filter. Leave empty for all cities or for top local buckets. ## `state` (type: `string`): Optional two-letter US state filter. Examples: CA, NY, TX, WA. Leave empty for nationwide. ## `visaTypes` (type: `array`): Choose which official DOL wage disclosure programs to include. More programs mean more source files to scan. H-1B, H-1B1, and E-3 come from LCA files. PERM comes from permanent labor certification files. The prefilled run uses H-1B only for a fast sample; add PERM when you need green-card wage data. ## `decisions` (type: `array`): Approved means certified by DOL. Denied and Withdrawn can be included for research, but approved filings are best for salary benchmarks. ## `fiscalYears` (type: `array`): US fiscal years to include. Leave empty for the latest available fiscal year. More years mean more source filings to scan, especially in benchmark mode. Examples: 2026, 2025, 2024. ## `minSalary` (type: `integer`): Skip filings with an annualized offered wage below this value. 0 means no minimum. ## `maxSalary` (type: `integer`): Skip filings with an annualized offered wage above this value. 0 means no maximum. ## `maxItems` (type: `integer`): Limits rows written to the dataset, not source rows scanned. Benchmark mode may scan thousands or millions of filings to calculate accurate percentiles, then returns up to this many aggregated benchmark rows. Raw salary sample mode returns up to this many exact filing records. ## `newFilingsOnly` (type: `boolean`): For salary-samples, skip filings at or before the saved decision-date watermark. Add a state key-value store below when you need this to persist across runs. ## `stateKeyValueStoreId` (type: `string`): Optional. Select a key-value store for persistent newFilingsOnly watermarks. Leave empty to use the run's default storage. ## `proxyConfiguration` (type: `object`): The US Department of Labor public data portal usually does not require proxies. Leave the default unless your network blocks government XLSX downloads. ## Actor input object example ```json { "mode": "salary-samples", "role": "software engineer", "employer": "", "city": "", "state": "", "visaTypes": [ "H-1B" ], "decisions": [ "Approved" ], "fiscalYears": [], "minSalary": 0, "maxSalary": 0, "maxItems": 10, "newFilingsOnly": false, "proxyConfiguration": { "useApifyProxy": false } } ``` # Actor output Schema ## `dataset` (type: `string`): Dataset containing salary-distribution rows and optional individual filing samples. # 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 = { "mode": "salary-samples", "role": "software engineer", "employer": "", "city": "", "state": "", "visaTypes": [ "H-1B" ], "fiscalYears": [], "maxItems": 10, "proxyConfiguration": { "useApifyProxy": false } }; // Run the Actor and wait for it to finish const run = await client.actor("constructive_calm/salarybench-iq").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 = { "mode": "salary-samples", "role": "software engineer", "employer": "", "city": "", "state": "", "visaTypes": ["H-1B"], "fiscalYears": [], "maxItems": 10, "proxyConfiguration": { "useApifyProxy": False }, } # Run the Actor and wait for it to finish run = client.actor("constructive_calm/salarybench-iq").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 '{ "mode": "salary-samples", "role": "software engineer", "employer": "", "city": "", "state": "", "visaTypes": [ "H-1B" ], "fiscalYears": [], "maxItems": 10, "proxyConfiguration": { "useApifyProxy": false } }' | apify call constructive_calm/salarybench-iq --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=constructive_calm/salarybench-iq", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SalaryBench IQ", "description": "SalaryBench IQ turns official US Department of Labor wage disclosure filings into defensible salary benchmarks, percentile ranges, sample wage records, and employer pay intelligence.", "version": "1.0", "x-build-id": "FCPzGn9HaKIbXgJBt" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/constructive_calm~salarybench-iq/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-constructive_calm-salarybench-iq", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/constructive_calm~salarybench-iq/runs": { "post": { "operationId": "runs-sync-constructive_calm-salarybench-iq", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/constructive_calm~salarybench-iq/run-sync": { "post": { "operationId": "run-sync-constructive_calm-salarybench-iq", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "mode", "role" ], "properties": { "mode": { "title": "Choose output type", "enum": [ "salary-benchmark", "salary-samples" ], "type": "string", "description": "Need exact salary records? Choose Raw salary sample filings. The prefilled run uses this mode because it returns a quick sample and stops at Max output rows. Need market ranges? Choose Benchmark percentiles; this scans matching DOL source filings first, then returns aggregated P10/P25/median/P75/P90 salary rows.", "default": "salary-samples" }, "role": { "title": "Job title or keyword", "type": "string", "description": "Job title keyword to benchmark. Examples: software engineer, data scientist, product manager, registered nurse.", "default": "" }, "employer": { "title": "Optional employer filter", "type": "string", "description": "Optional company keyword for employer-specific salary benchmarks. Examples: Google, Amazon, Infosys.", "default": "" }, "city": { "title": "Work location city", "type": "string", "description": "Optional worksite city filter. Leave empty for all cities or for top local buckets.", "default": "" }, "state": { "title": "Work location state", "type": "string", "description": "Optional two-letter US state filter. Examples: CA, NY, TX, WA. Leave empty for nationwide.", "default": "" }, "visaTypes": { "title": "Filing programs", "type": "array", "description": "Choose which official DOL wage disclosure programs to include. More programs mean more source files to scan. H-1B, H-1B1, and E-3 come from LCA files. PERM comes from permanent labor certification files. The prefilled run uses H-1B only for a fast sample; add PERM when you need green-card wage data.", "items": { "type": "string", "enum": [ "H-1B", "H-1B1", "E-3", "PERM" ], "enumTitles": [ "H-1B", "H-1B1", "E-3", "PERM green card" ] }, "default": [ "H-1B" ] }, "decisions": { "title": "Filing outcomes", "type": "array", "description": "Approved means certified by DOL. Denied and Withdrawn can be included for research, but approved filings are best for salary benchmarks.", "items": { "type": "string", "enum": [ "Approved", "Denied", "Withdrawn" ], "enumTitles": [ "Approved", "Denied", "Withdrawn" ] }, "default": [ "Approved" ] }, "fiscalYears": { "title": "Fiscal years", "type": "array", "description": "US fiscal years to include. Leave empty for the latest available fiscal year. More years mean more source filings to scan, especially in benchmark mode. Examples: 2026, 2025, 2024.", "items": { "type": "string", "pattern": "^[0-9]{4}$" }, "default": [] }, "minSalary": { "title": "Minimum annual salary", "minimum": 0, "maximum": 10000000, "type": "integer", "description": "Skip filings with an annualized offered wage below this value. 0 means no minimum.", "default": 0 }, "maxSalary": { "title": "Maximum annual salary", "minimum": 0, "maximum": 10000000, "type": "integer", "description": "Skip filings with an annualized offered wage above this value. 0 means no maximum.", "default": 0 }, "maxItems": { "title": "Max output rows", "minimum": 1, "maximum": 50000, "type": "integer", "description": "Limits rows written to the dataset, not source rows scanned. Benchmark mode may scan thousands or millions of filings to calculate accurate percentiles, then returns up to this many aggregated benchmark rows. Raw salary sample mode returns up to this many exact filing records.", "default": 25 }, "newFilingsOnly": { "title": "Only new sample filings", "type": "boolean", "description": "For salary-samples, skip filings at or before the saved decision-date watermark. Add a state key-value store below when you need this to persist across runs.", "default": false }, "stateKeyValueStoreId": { "title": "State key-value store", "type": "string", "description": "Optional. Select a key-value store for persistent newFilingsOnly watermarks. Leave empty to use the run's default storage." }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "The US Department of Labor public data portal usually does not require proxies. Leave the default unless your network blocks government XLSX downloads.", "default": { "useApifyProxy": false } } } }, "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 } } } } } } } } } } ```