# BLS Occupation Projections Scraper (`parseforge/bls-occupation-projections-scraper`) Actor

Scrapes BLS occupation projections for 2024-2034, filtered by occupation type, minimum median wage, and minimum employment change percent. Returns each projection as a flat row.

- **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, 86.2% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $19.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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](https://raw.githubusercontent.com/ParseForge/apify-assets/main/banner.jpg)](https://apify.com/parseforge?fpr=vmoqkp)

### BLS Occupation Projections Scraper

**Scrape BLS occupation projections for any SOC level, filtered by wage and growth, up to a million rows per run.** Each projection includes median wage, employment change, and job outlook. No API key. Export to CSV, JSON, Excel, or XML.

The Bureau of Labor Statistics publishes 10-year employment projections for every occupation, but the data sits in HTML tables and downloadable files that are hard to query. This Actor reads the public projections directly, filters by occupation type, minimum median wage, and minimum employment change, and returns each match as one flat row.

| Who uses it | What they scrape BLS Occupation Projections for |
|---|---|
| Labor market analysts | Which occupations will add the most jobs in the next decade |
| Workforce planners | Which occupations in a region are projected to grow fastest |
| Career counselors | Which occupations pay above a given wage and are growing |
| Policy researchers | How employment projections differ across SOC levels |
| Job board operators | Which occupations to feature based on projected demand |

### What it does

This Actor collects BLS occupation projections by SOC hierarchy level and optional wage and growth filters, and returns each projection as a flat row.

- 📊 **Projection rows:** median annual wage, employment change percent, and numeric change for 2024-2034.
- 🔎 **SOC level filter:** Summary, Major, Minor, Broad, Detailed, or Line item.
- 💰 **Wage floor:** skip occupations below a minimum median annual wage.
- 📈 **Growth floor:** skip occupations below a minimum employment change percent.
- 📦 **Bulk export:** collect up to 1,000,000 projections per run.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

### What you can do with BLS Occupation Projections data

**📈 Track fastest-growing occupations.**

A workforce board sets minEmploymentChangePercent to 20 and collects detailed occupations to publish a local hot jobs list.

**💰 Find high-wage, high-growth roles.**

A career counselor sets minMedianWage to 80000 and minEmploymentChangePercent to 10 to build a list of promising careers for students.

**🏛️ Compare SOC levels for policy.**

A state labor department collects Major and Detailed projections to see how broad trends break down into specific occupations.

**📊 Feed a job board with demand data.**

A job platform collects all Detailed projections above a wage floor and joins them to posted jobs to show projected growth.

**🧪 Build a research dataset.**

An economist collects all Line item projections for 2024-2034 to run regressions on wage and employment change.

### Why choose this scraper

|  | What you get |
|---|---|
| **No API key** | Reads the public BLS projection tables directly, no registration or token. |
| **One fixed schema** | Every projection returns the same flat fields, ready for analysis. |
| **Filter before export** | Wage and growth floors cut noise before it reaches your dataset. |
| **Any SOC level** | From broad summary groups down to detailed line items. |

### How it compares

No other Store actor targets BLS Occupation Projections the same way, so the honest comparison is with the alternatives teams actually weigh.

| | BLS Occupation Projections Scraper | Build it in-house | By hand |
|---|---|---|---|
| Setup | Run it now, zero config | Days of engineering | None, but hours per pull |
| When BLS Occupation Projections changes | Maintained for you | You fix it | You re-learn the page |
| Proxies, retries, anti-bot | Built in | Your problem | Browser only |
| Output | Fixed JSON schema, CSV/Excel export | Whatever you build | Copy-paste |
| Cost | Pay per result | Engineering time | Analyst hours |

### Configure the run

Drive the Actor from occupation type, minimum median wage, and minimum employment change percent, alone or together, and filters run as each projection is read so only matches reach your dataset. The Input tab lists every parameter.

A first run with the defaults:

```json
{
  "maxItems": 10
}
```

A larger pull:

```json
{
  "maxItems": 200
}
```

### Pricing

Pay-per-result: **$0.021 per result** collected. You pay only for the results written to your dataset.

| Results collected | Approximate cost |
|---|---|
| 100 results | $2.10 |
| 1,000 results | $21.00 |
| 10,000 results | $210.00 |

New Apify accounts start with $5 in free credit.

### Free users

Free-plan runs return up to 10 results as a preview. [Upgrade your Apify plan](https://console.apify.com/sign-up?fpr=vmoqkp) to collect up to 1,000,000 results per run.

### Run it

1. [Create a free Apify account with $5 in credit](https://console.apify.com/sign-up?fpr=vmoqkp).
2. Open the [BLS Occupation Projections Scraper](https://apify.com/parseforge/bls-occupation-projections-scraper?fpr=vmoqkp).
3. Set your inputs and any filters, then click **Start**.
4. Export the results as CSV, Excel, JSON, or XML from the **Dataset** tab.

Run it programmatically through the [Apify API](https://docs.apify.com/api/v2) (`run-sync-get-dataset-items`) or the [ApifyClient](https://docs.apify.com/api/client/js) for JavaScript and Python.

### Use with AI agents (MCP)

Give an AI agent live access to BLS Occupation Projections through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

```bash
claude mcp add --transport http apify "https://mcp.apify.com/?tools=parseforge/bls-occupation-projections-scraper"
```

Then prompt it in plain language to run the scraper and read back the results.

### Troubleshooting

**Why am I getting no results?**

Check your filters. If minMedianWage or minEmploymentChangePercent is set too high, all occupations may be skipped. Try lowering the thresholds or leaving them blank.

**Why did the run stop before reaching maxItems?**

The Actor stops when it has read all projections that match your filters. If fewer than maxItems exist, you will get all of them.

**Why are some occupations missing?**

The BLS publishes projections only for occupations with sufficient data. Some detailed occupations may not have a projection for every field.

**Can I filter by a specific SOC code?**

Not directly. Use occupationType to narrow the level, then filter the output by SOC code in your own processing.

**Why is the median wage different from another source?**

The BLS updates wage data periodically. The Actor returns the value currently published in the projection tables.

### FAQ

| Question | Answer |
|---|---|
| What is the source of this data? | The U.S. Bureau of Labor Statistics Employment Projections program, which publishes 10-year projections for occupations by SOC code. |
| What years do the projections cover? | The current release covers 2024 to 2034. The Actor returns the projected employment change over that period. |
| Can I filter by occupation type? | Yes. Use the occupationType input to select Summary, Major, Minor, Broad, Detailed, or Line item, or leave it blank for all levels. |
| How do I filter by wage? | Set minMedianWage to the minimum annual median wage in USD. Occupations below that value are skipped. |
| How do I filter by growth? | Set minEmploymentChangePercent to the minimum projected employment change percent. Occupations growing slower are skipped. |
| What is the maximum number of projections I can collect? | Up to 1,000,000 per run, controlled by the maxItems input. |
| Do I need an API key? | No. The Actor reads the public BLS projection tables directly. |
| What format is the output? | Each projection is a flat row with fields like occupation title, SOC code, median wage, and employment change. You can export to CSV, JSON, Excel, or XML. |
| Can I get projections for a specific occupation? | The Actor does not filter by occupation title or SOC code directly. Use occupationType to narrow the level, then filter the results in your dataset. |
| How often is the data updated? | The Actor reads the live BLS tables, so it reflects the latest published projections whenever you run it. |

### Related actors

Browse the full [ParseForge collection](https://apify.com/parseforge?fpr=vmoqkp) for more scrapers.

🆘 **Need help?** Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ **Disclaimer.** This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by U.S. Bureau of Labor Statistics. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.

# Actor input Schema

## `maxItems` (type: `integer`):

How many projections to collect per run.

## `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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_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(`💾 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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# 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(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → 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": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,parseforge/bls-occupation-projections-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/Uq0UF0ZnhgUHEzf7Y/builds/ckGYIYWrWhbXiGtDl/openapi.json