Initial BLS data reference and DC/MD/VA unemployment dashboard
Includes: - API v1/v2 documentation, endpoints, request/response schemas - Complete survey catalog (60 surveys, live-fetched from API) - Series ID decode tables: LAUS, CES, SM, QCEW, OES, JOLTS, CPI, PPI - QCEW quarterly (47 fields) and annual (43 fields) CSV schemas - dc_md_va_unemployment.py: pulls 16 LAUS series for DC/MD/VA + DC MSA - Example API response and 5-year CSV output Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
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config.py
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__pycache__/
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*.pyc
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.env
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README.md
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# BLS Data Reference
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Bureau of Labor Statistics — API access, dataset catalog, series ID formats, and field schemas.
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## Quick Start
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1. Register for an API key: https://data.bls.gov/registrationEngine/
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2. Base API endpoint: `https://api.bls.gov/publicAPI/v2/timeseries/data/`
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3. Content-Type: `application/json` (POST)
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---
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## API Versions
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| Feature | v1 (no key) | v2 (registered) |
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|---------------------------|-------------|-----------------|
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| Daily query limit | 25 | 500 |
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| Series per query | 25 | 50 |
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| Years of history | 10 | 20 |
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| Net/percent changes | No | Yes |
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| Series descriptions | No | Yes |
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| Calculations | No | Yes |
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---
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## Registration
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- URL: https://data.bls.gov/registrationEngine/
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- Free, no approval needed — instant key via email
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- Key goes in the JSON payload as `"registrationkey": "YOUR_KEY"`
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---
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## Endpoints
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### GET: Survey List
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```
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GET https://api.bls.gov/publicAPI/v2/surveys
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GET https://api.bls.gov/publicAPI/v2/surveys/{survey_abbreviation}
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```
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Returns all survey codes and names. See `surveys.json` for full list.
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### GET: Popular Series
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```
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GET https://api.bls.gov/publicAPI/v2/timeseries/popular
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GET https://api.bls.gov/publicAPI/v2/timeseries/popular?survey={abbreviation}
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```
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Returns the 25 most-requested series IDs for a survey.
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### POST: Time Series Data
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```
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POST https://api.bls.gov/publicAPI/v2/timeseries/data/
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Content-Type: application/json
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```
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**Minimal request (unregistered):**
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```json
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{
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"seriesid": ["LAUST110000000000003", "CES0000000001"],
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"startyear": "2023",
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"endyear": "2025"
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}
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```
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**Full request (registered, v2 features):**
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```json
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{
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"seriesid": ["LAUST110000000000003", "CES0000000001"],
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"startyear": "2020",
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"endyear": "2025",
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"registrationkey": "YOUR_KEY",
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"catalog": true,
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"calculations": true,
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"annualaverage": true,
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"aspects": true
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}
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```
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**Response schema:**
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```json
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{
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"status": "REQUEST_SUCCEEDED",
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"responseTime": 114,
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"message": [],
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"Results": {
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"series": [
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{
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"seriesID": "LAUST110000000000003",
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"catalog": {
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"series_title": "...",
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"survey_name": "...",
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"measure_data_type": "..."
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},
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"data": [
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{
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"year": "2025",
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"period": "M12",
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"periodName": "December",
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"value": "6.4",
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"footnotes": [
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{ "code": "R", "text": "Data were subject to revision on April 8, 2026." }
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],
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"calculations": {
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"net_changes": { "1": "0.1", "3": "-0.2", "6": "0.5", "12": "-0.3" },
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"pct_changes": { "1": "1.6", "3": "-3.0", "6": "8.3", "12": "-4.5" }
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}
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}
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]
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}
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]
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}
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}
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```
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**Period codes:**
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- Monthly: `M01`–`M12`, `M13` (annual average)
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- Quarterly: `Q01`–`Q04`, `Q05` (annual average)
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- Annual: `A01`
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**Status codes:** `REQUEST_SUCCEEDED`, `REQUEST_FAILED`, `REQUEST_NOT_PROCESSED`
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**Error footnote codes:**
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- `R` — Revised
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- `P` — Preliminary
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- `X` — Data unavailable (e.g., government shutdown gap)
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- `N` — Not available
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---
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## Python Example
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```python
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import requests
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API_KEY = "YOUR_KEY"
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BASE_URL = "https://api.bls.gov/publicAPI/v2/timeseries/data/"
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def get_series(series_ids, start_year, end_year):
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payload = {
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"seriesid": series_ids,
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"startyear": str(start_year),
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"endyear": str(end_year),
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"registrationkey": API_KEY,
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"catalog": True,
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"calculations": True,
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"annualaverage": True,
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}
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r = requests.post(BASE_URL, json=payload)
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r.raise_for_status()
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data = r.json()
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if data["status"] != "REQUEST_SUCCEEDED":
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raise ValueError(f"BLS API error: {data['message']}")
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return data["Results"]["series"]
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# DC unemployment rate (LAUS)
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series = get_series(["LAUST110000000000003"], 2020, 2025)
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for obs in series[0]["data"]:
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print(obs["year"], obs["periodName"], obs["value"])
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```
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---
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## Bulk Download (flat files)
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Base URL: `https://download.bls.gov/pub/time.series/`
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Each survey folder contains:
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- `{prefix}.series` — master list of all series IDs with metadata
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- `{prefix}.data.{N}.{name}` — actual observations, split by category
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- `{prefix}.{dimension}` — lookup/decode tables (area, industry, measure, etc.)
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Key folder prefixes:
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```
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la/ — LAUS (local area unemployment)
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ce/ — CES national employment
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sm/ — State & Metro employment (CES state)
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en/ — QCEW
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oe/ — OES (occupational employment)
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jt/ — JOLTS
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cu/ — CPI-U
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wp/ — PPI commodities
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```
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Files are tab-delimited. Useful for bulk PostgreSQL ingest.
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---
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## Files in this folder
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- `README.md` — this file
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- `surveys.json` — complete survey list from the API
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- `series_id_formats.md` — series ID decode tables for each key dataset
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- `qcew_field_schema.md` — QCEW quarterly and annual CSV field layouts
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- `api_response_example.json` — real API response sample
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1212
api_response_example.json
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api_response_example.json
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config.example.py
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config.example.py
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# BLS API Configuration
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# ----------------------
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# 1. Register for a free API key at: https://data.bls.gov/registrationEngine/
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# Required fields: first name, last name, organization, email address.
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# Your key will be emailed within a few minutes.
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#
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# 2. Copy this file to config.py:
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# cp config.example.py config.py
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#
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# 3. Paste your key below and save.
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BLS_API_KEY = "YOUR_KEY_HERE"
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BLS_API_BASE = "https://api.bls.gov/publicAPI/v2"
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dc_md_va_unemployment.csv
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geography,geo_code,measure,series_id,year,period,period_name,value,mom_chg,qoq_chg,yoy_chg
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District of Columbia,DC,rate,LAUST110000000000003,2026,M04,April,5.5,-0.2,-0.7,0.0
|
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District of Columbia,DC,rate,LAUST110000000000003,2026,M03,March,5.7,-0.4,-0.7,-0.4
|
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District of Columbia,DC,rate,LAUST110000000000003,2026,M02,February,6.1,-0.1,-0.7,0.1
|
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District of Columbia,DC,rate,LAUST110000000000003,2026,M01,January,6.2,-0.2,—,0.4
|
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District of Columbia,DC,rate,LAUST110000000000003,2025,M12,December,6.4,-0.4,-0.4,1.3
|
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District of Columbia,DC,rate,LAUST110000000000003,2025,M11,November,6.8,—,-0.3,1.6
|
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District of Columbia,DC,rate,LAUST110000000000003,2025,M09,September,6.8,-0.3,0.4,1.5
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2025,M08,August,7.1,0.3,1.3,1.0
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2025,M07,July,6.8,0.4,1.3,0.8
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2025,M06,June,6.4,0.6,0.3,0.7
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2025,M05,May,5.8,0.3,-0.2,0.8
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2025,M04,April,5.5,-0.6,-0.3,1.0
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2025,M03,March,6.1,0.1,1.0,1.1
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2025,M02,February,6.0,0.2,0.8,0.8
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2025,M01,January,5.8,0.7,0.6,0.6
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2024,M12,December,5.1,-0.1,-0.2,0.4
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2024,M11,November,5.2,0.0,-0.9,0.6
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2024,M10,October,5.2,-0.1,-0.8,0.2
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2024,M09,September,5.3,-0.8,-0.4,0.3
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2024,M08,August,6.1,0.1,1.1,0.7
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2024,M07,July,6.0,0.3,1.5,0.9
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2024,M06,June,5.7,0.7,0.7,0.5
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2024,M05,May,5.0,0.5,-0.2,0.4
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2024,M04,April,4.5,-0.5,-0.7,0.4
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2024,M03,March,5.0,-0.2,0.3,0.1
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2024,M02,February,5.2,0.0,0.6,0.3
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2024,M01,January,5.2,0.5,0.2,0.4
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2023,M12,December,4.7,0.1,-0.3,0.6
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2023,M11,November,4.6,-0.4,-0.8,0.4
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2023,M10,October,5.0,0.0,-0.1,0.6
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2023,M09,September,5.0,-0.4,-0.2,0.8
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2023,M08,August,5.4,0.3,0.8,0.7
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2023,M07,July,5.1,-0.1,1.0,0.4
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2023,M06,June,5.2,0.6,0.3,0.3
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2023,M05,May,4.6,0.5,-0.3,0.3
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2023,M04,April,4.1,-0.8,-0.7,-0.2
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2023,M03,March,4.9,0.0,0.8,-0.2
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2023,M02,February,4.9,0.1,0.7,-0.6
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2023,M01,January,4.8,0.7,0.4,-1.3
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2022,M12,December,4.1,-0.1,-0.1,-1.1
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2022,M11,November,4.2,-0.2,-0.5,-2.0
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2022,M10,October,4.4,0.2,-0.3,-2.0
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2022,M09,September,4.2,-0.5,-0.7,-2.6
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2022,M08,August,4.7,0.0,0.4,-2.8
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2022,M07,July,4.7,-0.2,0.4,-2.8
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2022,M06,June,4.9,0.6,-0.2,-3.1
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2022,M05,May,4.3,0.0,-1.2,-2.4
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2022,M04,April,4.3,-0.8,-1.8,-2.3
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2022,M03,March,5.1,-0.4,-0.1,-1.8
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2022,M02,February,5.5,-0.6,-0.7,-1.4
|
||||
District of Columbia,DC,rate,LAUST110000000000003,2022,M01,January,6.1,0.9,-0.3,-0.5
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2026,M04,April,22184,-694,-2728,-676
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2026,M03,March,22878,-1865,-3209,-2412
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2026,M02,February,24743,-169,-3122,-328
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2026,M01,January,24912,-1175,—,872
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2025,M12,December,26087,-1778,-1510,5018
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2025,M11,November,27865,—,-848,6442
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2025,M09,September,27597,-1116,917,5778
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2025,M08,August,28713,380,4901,3542
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2025,M07,July,28333,1653,5473,3254
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2025,M06,June,26680,2868,1390,3060
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2025,M05,May,23812,952,-1259,3524
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2025,M04,April,22860,-2430,-1180,4356
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2025,M03,March,25290,219,4221,4463
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2025,M02,February,25071,1031,3648,3369
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2025,M01,January,24040,2971,2399,2856
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2024,M12,December,21069,-354,-750,1819
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2024,M11,November,21423,-218,-3748,2367
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2024,M10,October,21641,-178,-3438,1125
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2024,M09,September,21819,-3352,-1801,1346
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2024,M08,August,25171,92,4883,3146
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2024,M07,July,25079,1459,6575,4223
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2024,M06,June,23620,3332,2793,2620
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2024,M05,May,20288,1784,-1414,1987
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2024,M04,April,18504,-2323,-2680,2340
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2024,M03,March,20827,-875,1577,1310
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2024,M02,February,21702,518,2646,1853
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2024,M01,January,21184,1934,668,1985
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2023,M12,December,19250,194,-1223,2941
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2023,M11,November,19056,-1460,-2969,2468
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2023,M10,October,20516,43,-340,3384
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2023,M09,September,20473,-1552,-527,3801
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2023,M08,August,22025,1169,3724,3404
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2023,M07,July,20856,-144,4692,1902
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2023,M06,June,21000,2699,1483,1488
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2023,M05,May,18301,2137,-1548,1294
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2023,M04,April,16164,-3353,-3035,-523
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2023,M03,March,19517,-332,3208,-528
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2023,M02,February,19849,650,3261,-1560
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2023,M01,January,19199,2890,2067,-4450
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2022,M12,December,16309,-279,-363,-3654
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2022,M11,November,16588,-544,-2033,-7755
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2022,M10,October,17132,460,-1822,-7586
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2022,M09,September,16672,-1949,-2840,-9527
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2022,M08,August,18621,-333,1614,-10185
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2022,M07,July,18954,-558,2267,-10647
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2022,M06,June,19512,2505,-533,-11197
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2022,M05,May,17007,320,-4402,-8279
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2022,M04,April,16687,-3358,-6962,-8231
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2022,M03,March,20045,-1364,82,-6040
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2022,M02,February,21409,-2240,-2934,-4839
|
||||
District of Columbia,DC,unemployed,LAUST110000000000004,2022,M01,January,23649,3686,-1069,-850
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2026,M04,April,379272,-495,1763,-9795
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2026,M03,March,379767,-1161,-3114,-12190
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2026,M02,February,380928,3419,-488,-13066
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2026,M01,January,377509,-5372,—,-13288
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2025,M12,December,382881,1465,4212,-8738
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2025,M11,November,381416,—,3246,-10502
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2025,M09,September,378669,499,-10950,-10488
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2025,M08,August,378170,-11522,-6728,-8349
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2025,M07,July,389692,73,625,-6156
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2025,M06,June,389619,4721,-2338,-1667
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2025,M05,May,384898,-4169,-9096,-3589
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2025,M04,April,389067,-2890,-1730,-4376
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2025,M03,March,391957,-2037,338,-4957
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2025,M02,February,393994,3197,2076,2075
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2025,M01,January,390797,-822,-118,924
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2024,M12,December,391619,-299,2462,998
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2024,M11,November,391918,1003,5399,193
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2024,M10,October,390915,1758,-4933,2058
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2024,M09,September,389157,2638,-2129,1892
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2024,M08,August,386519,-9329,-1968,2608
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2024,M07,July,395848,4562,2405,5458
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2024,M06,June,391286,2799,-5628,6091
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2024,M05,May,388487,-4956,-3432,5204
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2024,M04,April,393443,-3471,3570,11671
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2024,M03,March,396914,4995,6293,14711
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2024,M02,February,391919,2046,194,10614
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2024,M01,January,389873,-748,1016,10781
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2023,M12,December,390621,-1104,3356,13654
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2023,M11,November,391725,2868,7814,15734
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2023,M10,October,388857,1592,-1533,12842
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2023,M09,September,387265,3354,2070,8353
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2023,M08,August,383911,-6479,628,5040
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2023,M07,July,390390,5195,8618,5099
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2023,M06,June,385195,1912,2992,7360
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2023,M05,May,383283,1511,1978,7895
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2023,M04,April,381772,-431,2680,6894
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2023,M03,March,382203,898,5236,7094
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2023,M02,February,381305,2213,5314,10079
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2023,M01,January,379092,2125,3077,16728
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2022,M12,December,376967,976,-1945,13110
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2022,M11,November,375991,-24,-2880,10770
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2022,M10,October,376015,-2897,-9276,15131
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2022,M09,September,378912,41,1077,20651
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2022,M08,August,378871,-6420,3483,22820
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2022,M07,July,385291,7456,10413,22093
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2022,M06,June,377835,2447,2726,22965
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2022,M05,May,375388,510,4162,23846
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2022,M04,April,374878,-231,12514,24442
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2022,M03,March,375109,3883,11252,23199
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2022,M02,February,371226,8862,6005,19480
|
||||
District of Columbia,DC,employed,LAUST110000000000005,2022,M01,January,362364,-1493,1480,12860
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2026,M04,April,401456,-1189,-965,—
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2026,M03,March,402645,-3026,—,—
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2026,M02,February,405671,3250,—,—
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2026,M01,January,402421,—,—,—
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2025,M12,December,408968,-313,2702,-3720
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2025,M11,November,409281,—,2398,-4060
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2025,M09,September,406266,-617,-10033,-4710
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2025,M08,August,406883,-11142,-1827,-4807
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2025,M07,July,418025,1726,6098,-2902
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2025,M06,June,416299,7589,-948,1393
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2025,M05,May,408710,-3217,-10355,-65
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2025,M04,April,411927,-5320,-2910,-20
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2025,M03,March,417247,-1818,4559,-494
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2025,M02,February,419065,4228,5724,5444
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2025,M01,January,414837,2149,2281,3780
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2024,M12,December,412688,-653,1712,2817
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2024,M11,November,413341,785,1651,2560
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2024,M10,October,412556,1580,-8371,3183
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2024,M09,September,410976,-714,-3930,3238
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2024,M08,August,411690,-9237,2915,5754
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2024,M07,July,420927,6021,8980,9681
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2024,M06,June,414906,6131,-2835,8711
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2024,M05,May,408775,-3172,-4846,7191
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2024,M04,April,411947,-5794,890,14011
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2024,M03,March,417741,4120,7870,16021
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2024,M02,February,413621,2564,2840,12467
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2024,M01,January,411057,1186,1684,12766
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2023,M12,December,409871,-910,2133,16595
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2023,M11,November,410781,1408,4845,18202
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2023,M10,October,409373,1635,-1873,16226
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2023,M09,September,407738,1802,1543,12154
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2023,M08,August,405936,-5310,4352,8444
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2023,M07,July,411246,5051,13310,7001
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2023,M06,June,406195,4611,4475,8848
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2023,M05,May,401584,3648,430,9189
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2023,M04,April,397936,-3784,-355,6371
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2023,M03,March,401720,566,8444,6566
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2023,M02,February,401154,2863,8575,8519
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2023,M01,January,398291,5015,5144,12278
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2022,M12,December,393276,697,-2308,9456
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2022,M11,November,392579,-568,-4913,3015
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2022,M10,October,393147,-2437,-11098,7545
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2022,M09,September,395584,-1908,-1763,11124
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2022,M08,August,397492,-6753,5097,12635
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2022,M07,July,404245,6898,12680,11446
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2022,M06,June,397347,4952,2193,11768
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2022,M05,May,392395,830,-240,15567
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2022,M04,April,391565,-3589,5552,16211
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2022,M03,March,395154,2519,11334,17159
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2022,M02,February,392635,6622,3071,14641
|
||||
District of Columbia,DC,laborforce,LAUST110000000000006,2022,M01,January,386013,2193,411,12010
|
||||
Maryland,MD,rate,LAUST240000000000003,2026,M04,April,4.4,0.0,-0.2,1.0
|
||||
Maryland,MD,rate,LAUST240000000000003,2026,M03,March,4.4,-0.5,0.7,0.6
|
||||
Maryland,MD,rate,LAUST240000000000003,2026,M02,February,4.9,0.3,0.7,1.0
|
||||
Maryland,MD,rate,LAUST240000000000003,2026,M01,January,4.6,0.9,—,1.0
|
||||
Maryland,MD,rate,LAUST240000000000003,2025,M12,December,3.7,-0.5,-0.6,0.7
|
||||
Maryland,MD,rate,LAUST240000000000003,2025,M11,November,4.2,—,-0.4,0.8
|
||||
Maryland,MD,rate,LAUST240000000000003,2025,M09,September,4.3,-0.3,0.1,1.2
|
||||
Maryland,MD,rate,LAUST240000000000003,2025,M08,August,4.6,0.2,0.9,1.0
|
||||
Maryland,MD,rate,LAUST240000000000003,2025,M07,July,4.4,0.2,1.0,0.8
|
||||
Maryland,MD,rate,LAUST240000000000003,2025,M06,June,4.2,0.5,0.4,0.7
|
||||
Maryland,MD,rate,LAUST240000000000003,2025,M05,May,3.7,0.3,-0.2,1.0
|
||||
Maryland,MD,rate,LAUST240000000000003,2025,M04,April,3.4,-0.4,-0.2,0.8
|
||||
Maryland,MD,rate,LAUST240000000000003,2025,M03,March,3.8,-0.1,0.8,0.8
|
||||
Maryland,MD,rate,LAUST240000000000003,2025,M02,February,3.9,0.3,0.5,0.7
|
||||
Maryland,MD,rate,LAUST240000000000003,2025,M01,January,3.6,0.6,0.3,0.6
|
||||
Maryland,MD,rate,LAUST240000000000003,2024,M12,December,3.0,-0.4,-0.1,0.6
|
||||
Maryland,MD,rate,LAUST240000000000003,2024,M11,November,3.4,0.1,-0.2,1.0
|
||||
Maryland,MD,rate,LAUST240000000000003,2024,M10,October,3.3,0.2,-0.3,0.8
|
||||
Maryland,MD,rate,LAUST240000000000003,2024,M09,September,3.1,-0.5,-0.4,0.8
|
||||
Maryland,MD,rate,LAUST240000000000003,2024,M08,August,3.6,0.0,0.9,1.2
|
||||
Maryland,MD,rate,LAUST240000000000003,2024,M07,July,3.6,0.1,1.0,1.4
|
||||
Maryland,MD,rate,LAUST240000000000003,2024,M06,June,3.5,0.8,0.5,1.5
|
||||
Maryland,MD,rate,LAUST240000000000003,2024,M05,May,2.7,0.1,-0.5,0.9
|
||||
Maryland,MD,rate,LAUST240000000000003,2024,M04,April,2.6,-0.4,-0.4,1.0
|
||||
Maryland,MD,rate,LAUST240000000000003,2024,M03,March,3.0,-0.2,0.6,0.9
|
||||
Maryland,MD,rate,LAUST240000000000003,2024,M02,February,3.2,0.2,0.8,0.7
|
||||
Maryland,MD,rate,LAUST240000000000003,2024,M01,January,3.0,0.6,0.5,0.4
|
||||
Maryland,MD,rate,LAUST240000000000003,2023,M12,December,2.4,0.0,0.1,0.3
|
||||
Maryland,MD,rate,LAUST240000000000003,2023,M11,November,2.4,-0.1,0.0,-0.1
|
||||
Maryland,MD,rate,LAUST240000000000003,2023,M10,October,2.5,0.2,0.3,-0.3
|
||||
Maryland,MD,rate,LAUST240000000000003,2023,M09,September,2.3,-0.1,0.3,-0.4
|
||||
Maryland,MD,rate,LAUST240000000000003,2023,M08,August,2.4,0.2,0.6,-0.8
|
||||
Maryland,MD,rate,LAUST240000000000003,2023,M07,July,2.2,0.2,0.6,-1.0
|
||||
Maryland,MD,rate,LAUST240000000000003,2023,M06,June,2.0,0.2,-0.1,-1.4
|
||||
Maryland,MD,rate,LAUST240000000000003,2023,M05,May,1.8,0.2,-0.7,-1.0
|
||||
Maryland,MD,rate,LAUST240000000000003,2023,M04,April,1.6,-0.5,-1.0,-1.1
|
||||
Maryland,MD,rate,LAUST240000000000003,2023,M03,March,2.1,-0.4,0.0,-1.2
|
||||
Maryland,MD,rate,LAUST240000000000003,2023,M02,February,2.5,-0.1,0.0,-1.1
|
||||
Maryland,MD,rate,LAUST240000000000003,2023,M01,January,2.6,0.5,-0.2,-1.1
|
||||
Maryland,MD,rate,LAUST240000000000003,2022,M12,December,2.1,-0.4,-0.6,-1.0
|
||||
Maryland,MD,rate,LAUST240000000000003,2022,M11,November,2.5,-0.3,-0.7,-1.2
|
||||
Maryland,MD,rate,LAUST240000000000003,2022,M10,October,2.8,0.1,-0.4,-1.4
|
||||
Maryland,MD,rate,LAUST240000000000003,2022,M09,September,2.7,-0.5,-0.7,-1.7
|
||||
Maryland,MD,rate,LAUST240000000000003,2022,M08,August,3.2,0.0,0.4,-2.0
|
||||
Maryland,MD,rate,LAUST240000000000003,2022,M07,July,3.2,-0.2,0.5,-2.2
|
||||
Maryland,MD,rate,LAUST240000000000003,2022,M06,June,3.4,0.6,0.1,-2.6
|
||||
Maryland,MD,rate,LAUST240000000000003,2022,M05,May,2.8,0.1,-0.8,-2.7
|
||||
Maryland,MD,rate,LAUST240000000000003,2022,M04,April,2.7,-0.6,-1.0,-3.0
|
||||
Maryland,MD,rate,LAUST240000000000003,2022,M03,March,3.3,-0.3,0.2,-2.9
|
||||
Maryland,MD,rate,LAUST240000000000003,2022,M02,February,3.6,-0.1,-0.1,-2.9
|
||||
Maryland,MD,rate,LAUST240000000000003,2022,M01,January,3.7,0.6,-0.5,-3.0
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2026,M04,April,139946,-646,-6992,28851
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2026,M03,March,140592,-17317,22156,18513
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2026,M02,February,157909,10971,22786,32579
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2026,M01,January,146938,28502,—,30023
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2025,M12,December,118436,-16687,-21500,20789
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2025,M11,November,135123,—,-12657,26536
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2025,M09,September,139936,-7844,3650,40257
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2025,M08,August,147780,4922,27465,30349
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2025,M07,July,142858,6572,31763,24328
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2025,M06,June,136286,15971,14207,23346
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2025,M05,May,120315,9220,-5015,32018
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2025,M04,April,111095,-10984,-5820,26928
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2025,M03,March,122079,-3251,24432,23207
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2025,M02,February,125330,8415,16743,22057
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2025,M01,January,116915,19268,8868,19421
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2024,M12,December,97647,-10940,-2032,19779
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2024,M11,November,108587,540,-8844,32321
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2024,M10,October,108047,8368,-10483,26914
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2024,M09,September,99679,-17752,-13261,27027
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2024,M08,August,117431,-1099,29134,39586
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2024,M07,July,118530,5590,34363,47006
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2024,M06,June,112940,24643,14068,47812
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2024,M05,May,88297,4130,-14976,32156
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2024,M04,April,84167,-14705,-13327,34351
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2024,M03,March,98872,-4401,21004,31011
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2024,M02,February,103273,5779,27007,22494
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2024,M01,January,97494,19626,16361,16081
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2023,M12,December,77868,1602,5216,11306
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2023,M11,November,76266,-4867,-1579,-2295
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2023,M10,October,81133,8481,9609,-7267
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2023,M09,September,72652,-5193,7524,-11590
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2023,M08,August,77845,6321,21704,-25911
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2023,M07,July,71524,6396,21708,-31106
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2023,M06,June,65128,8987,-2733,-44059
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2023,M05,May,56141,6325,-24638,-33916
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2023,M04,April,49816,-18045,-31597,-35585
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2023,M03,March,67861,-12918,1299,-35787
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2023,M02,February,80779,-634,2218,-33121
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2023,M01,January,81413,14851,-6987,-35607
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2022,M12,December,66562,-11999,-17680,-29899
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2022,M11,November,78561,-9839,-25195,-39882
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2022,M10,October,88400,4158,-14230,-43944
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2022,M09,September,84242,-19514,-24945,-55735
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2022,M08,August,103756,1126,13699,-62033
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2022,M07,July,102630,-6557,17229,-72667
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2022,M06,June,109187,19130,5539,-84420
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2022,M05,May,90057,4656,-23843,-83292
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2022,M04,April,85401,-18247,-31619,-94207
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2022,M03,March,103648,-10252,7187,-92942
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2022,M02,February,113900,-3120,-4543,-91640
|
||||
Maryland,MD,unemployed,LAUST240000000000004,2022,M01,January,117020,20559,-15324,-93385
|
||||
Maryland,MD,employed,LAUST240000000000005,2026,M04,April,3066322,-4868,45073,-53005
|
||||
Maryland,MD,employed,LAUST240000000000005,2026,M03,March,3071190,26817,3417,-51222
|
||||
Maryland,MD,employed,LAUST240000000000005,2026,M02,February,3044373,23124,-19574,-50809
|
||||
Maryland,MD,employed,LAUST240000000000005,2026,M01,January,3021249,-46524,—,-81769
|
||||
Maryland,MD,employed,LAUST240000000000005,2025,M12,December,3067773,3826,-22086,-60212
|
||||
Maryland,MD,employed,LAUST240000000000005,2025,M11,November,3063947,—,-31844,-59364
|
||||
Maryland,MD,employed,LAUST240000000000005,2025,M09,September,3089859,-5932,-30886,-47878
|
||||
Maryland,MD,employed,LAUST240000000000005,2025,M08,August,3095791,-36517,-3992,-44925
|
||||
Maryland,MD,employed,LAUST240000000000005,2025,M07,July,3132308,11563,12981,-46426
|
||||
Maryland,MD,employed,LAUST240000000000005,2025,M06,June,3120745,20962,-1667,-34262
|
||||
Maryland,MD,employed,LAUST240000000000005,2025,M05,May,3099783,-19544,4601,-31736
|
||||
Maryland,MD,employed,LAUST240000000000005,2025,M04,April,3119327,-3085,16309,-23991
|
||||
Maryland,MD,employed,LAUST240000000000005,2025,M03,March,3122412,27230,-5573,-25164
|
||||
Maryland,MD,employed,LAUST240000000000005,2025,M02,February,3095182,-7836,-28129,-21677
|
||||
Maryland,MD,employed,LAUST240000000000005,2025,M01,January,3103018,-24967,-39397,-6970
|
||||
Maryland,MD,employed,LAUST240000000000005,2024,M12,December,3127985,4674,-9752,11279
|
||||
Maryland,MD,employed,LAUST240000000000005,2024,M11,November,3123311,-19104,-17405,-6062
|
||||
Maryland,MD,employed,LAUST240000000000005,2024,M10,October,3142415,4678,-36319,16493
|
||||
Maryland,MD,employed,LAUST240000000000005,2024,M09,September,3137737,-2979,-17270,13129
|
||||
Maryland,MD,employed,LAUST240000000000005,2024,M08,August,3140716,-38018,9197,-868
|
||||
Maryland,MD,employed,LAUST240000000000005,2024,M07,July,3178734,23727,35416,449
|
||||
Maryland,MD,employed,LAUST240000000000005,2024,M06,June,3155007,23488,7431,3104
|
||||
Maryland,MD,employed,LAUST240000000000005,2024,M05,May,3131519,-11799,14660,6853
|
||||
Maryland,MD,employed,LAUST240000000000005,2024,M04,April,3143318,-4258,33330,12932
|
||||
Maryland,MD,employed,LAUST240000000000005,2024,M03,March,3147576,30717,30870,19702
|
||||
Maryland,MD,employed,LAUST240000000000005,2024,M02,February,3116859,6871,-12514,27364
|
||||
Maryland,MD,employed,LAUST240000000000005,2024,M01,January,3109988,-6718,-15934,34308
|
||||
Maryland,MD,employed,LAUST240000000000005,2023,M12,December,3116706,-12667,-7902,19104
|
||||
Maryland,MD,employed,LAUST240000000000005,2023,M11,November,3129373,3451,-12211,47709
|
||||
Maryland,MD,employed,LAUST240000000000005,2023,M10,October,3125922,1314,-52363,32271
|
||||
Maryland,MD,employed,LAUST240000000000005,2023,M09,September,3124608,-16976,-27295,36989
|
||||
Maryland,MD,employed,LAUST240000000000005,2023,M08,August,3141584,-36701,16918,47379
|
||||
Maryland,MD,employed,LAUST240000000000005,2023,M07,July,3178285,26382,47899,59430
|
||||
Maryland,MD,employed,LAUST240000000000005,2023,M06,June,3151903,27237,24029,58026
|
||||
Maryland,MD,employed,LAUST240000000000005,2023,M05,May,3124666,-5720,35171,47109
|
||||
Maryland,MD,employed,LAUST240000000000005,2023,M04,April,3130386,2512,54706,57737
|
||||
Maryland,MD,employed,LAUST240000000000005,2023,M03,March,3127874,38379,30272,47169
|
||||
Maryland,MD,employed,LAUST240000000000005,2023,M02,February,3089495,13815,7831,38608
|
||||
Maryland,MD,employed,LAUST240000000000005,2023,M01,January,3075680,-21922,-17971,39921
|
||||
Maryland,MD,employed,LAUST240000000000005,2022,M12,December,3097602,15938,9983,38062
|
||||
Maryland,MD,employed,LAUST240000000000005,2022,M11,November,3081664,-11987,-12541,34548
|
||||
Maryland,MD,employed,LAUST240000000000005,2022,M10,October,3093651,6032,-25204,55969
|
||||
Maryland,MD,employed,LAUST240000000000005,2022,M09,September,3087619,-6586,-6258,67551
|
||||
Maryland,MD,employed,LAUST240000000000005,2022,M08,August,3094205,-24650,16648,73147
|
||||
Maryland,MD,employed,LAUST240000000000005,2022,M07,July,3118855,24978,46206,65679
|
||||
Maryland,MD,employed,LAUST240000000000005,2022,M06,June,3093877,16320,13172,84022
|
||||
Maryland,MD,employed,LAUST240000000000005,2022,M05,May,3077557,4908,26670,88251
|
||||
Maryland,MD,employed,LAUST240000000000005,2022,M04,April,3072649,-8056,36890,86415
|
||||
Maryland,MD,employed,LAUST240000000000005,2022,M03,March,3080705,29818,21165,99482
|
||||
Maryland,MD,employed,LAUST240000000000005,2022,M02,February,3050887,15128,3771,98867
|
||||
Maryland,MD,employed,LAUST240000000000005,2022,M01,January,3035759,-23781,-1923,92542
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2026,M04,April,3206268,-5514,38081,—
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2026,M03,March,3211782,9500,—,—
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2026,M02,February,3202282,34095,—,—
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2026,M01,January,3168187,—,—,—
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2025,M12,December,3186209,-12861,-43586,-39423
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2025,M11,November,3199070,—,-44501,-32828
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2025,M09,September,3229795,-13776,-27236,-7621
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2025,M08,August,3243571,-31595,23473,-14576
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2025,M07,July,3275166,18135,44744,-22098
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2025,M06,June,3257031,36933,12540,-10916
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2025,M05,May,3220098,-10324,-414,282
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2025,M04,April,3230422,-14069,10489,2937
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2025,M03,March,3244491,23979,18859,-1957
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2025,M02,February,3220512,579,-11386,380
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2025,M01,January,3219933,-5699,-30529,12451
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2024,M12,December,3225632,-6266,-11784,31058
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2024,M11,November,3231898,-18564,-26249,26259
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2024,M10,October,3250462,13046,-46802,43407
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2024,M09,September,3237416,-20731,-30531,40156
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2024,M08,August,3258147,-39117,38331,38718
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2024,M07,July,3297264,29317,69779,47455
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2024,M06,June,3267947,48131,21499,50916
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2024,M05,May,3219816,-7669,-316,39009
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2024,M04,April,3227485,-18963,20003,47283
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2024,M03,March,3246448,26316,51874,50713
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2024,M02,February,3220132,12650,14493,49858
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2024,M01,January,3207482,12908,427,50389
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2023,M12,December,3194574,-11065,-2686,30410
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2023,M11,November,3205639,-1416,-13790,45414
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2023,M10,October,3207055,9795,-42754,25004
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2023,M09,September,3197260,-22169,-19771,25399
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2023,M08,August,3219429,-30380,38622,21468
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2023,M07,July,3249809,32778,69607,28324
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2023,M06,June,3217031,36224,21296,13967
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2023,M05,May,3180807,605,10533,13193
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2023,M04,April,3180202,-15533,23109,22152
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2023,M03,March,3195735,25461,31571,11382
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2023,M02,February,3170274,13181,10049,5487
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2023,M01,January,3157093,-7071,-24958,4314
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2022,M12,December,3164164,3939,-7697,8163
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2022,M11,November,3160225,-21826,-37736,-5334
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2022,M10,October,3182051,10190,-39434,12025
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2022,M09,September,3171861,-26100,-31203,11816
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2022,M08,August,3197961,-23524,30347,11114
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2022,M07,July,3221485,18421,63435,-6988
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2022,M06,June,3203064,35450,18711,-398
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2022,M05,May,3167614,9564,2827,4959
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2022,M04,April,3158050,-26303,5271,-7792
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2022,M03,March,3184353,19566,28352,6540
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2022,M02,February,3164787,12008,-772,7227
|
||||
Maryland,MD,laborforce,LAUST240000000000006,2022,M01,January,3152779,-3222,-17247,-843
|
||||
Virginia,VA,rate,LAUST510000000000003,2026,M04,April,3.4,-0.4,-0.5,0.4
|
||||
Virginia,VA,rate,LAUST510000000000003,2026,M03,March,3.8,-0.1,0.3,0.5
|
||||
Virginia,VA,rate,LAUST510000000000003,2026,M02,February,3.9,0.0,0.0,0.6
|
||||
Virginia,VA,rate,LAUST510000000000003,2026,M01,January,3.9,0.4,—,0.7
|
||||
Virginia,VA,rate,LAUST510000000000003,2025,M12,December,3.5,-0.4,0.1,0.8
|
||||
Virginia,VA,rate,LAUST510000000000003,2025,M11,November,3.9,—,0.3,0.8
|
||||
Virginia,VA,rate,LAUST510000000000003,2025,M09,September,3.4,-0.2,-0.1,0.5
|
||||
Virginia,VA,rate,LAUST510000000000003,2025,M08,August,3.6,0.1,0.3,0.2
|
||||
Virginia,VA,rate,LAUST510000000000003,2025,M07,July,3.5,0.0,0.5,0.3
|
||||
Virginia,VA,rate,LAUST510000000000003,2025,M06,June,3.5,0.2,0.2,0.4
|
||||
Virginia,VA,rate,LAUST510000000000003,2025,M05,May,3.3,0.3,0.0,0.5
|
||||
Virginia,VA,rate,LAUST510000000000003,2025,M04,April,3.0,-0.3,-0.2,0.5
|
||||
Virginia,VA,rate,LAUST510000000000003,2025,M03,March,3.3,0.0,0.6,0.6
|
||||
Virginia,VA,rate,LAUST510000000000003,2025,M02,February,3.3,0.1,0.2,0.4
|
||||
Virginia,VA,rate,LAUST510000000000003,2025,M01,January,3.2,0.5,0.3,0.4
|
||||
Virginia,VA,rate,LAUST510000000000003,2024,M12,December,2.7,-0.4,-0.2,0.3
|
||||
Virginia,VA,rate,LAUST510000000000003,2024,M11,November,3.1,0.2,-0.3,0.5
|
||||
Virginia,VA,rate,LAUST510000000000003,2024,M10,October,2.9,0.0,-0.3,0.2
|
||||
Virginia,VA,rate,LAUST510000000000003,2024,M09,September,2.9,-0.5,-0.2,0.1
|
||||
Virginia,VA,rate,LAUST510000000000003,2024,M08,August,3.4,0.2,0.6,0.4
|
||||
Virginia,VA,rate,LAUST510000000000003,2024,M07,July,3.2,0.1,0.7,0.5
|
||||
Virginia,VA,rate,LAUST510000000000003,2024,M06,June,3.1,0.3,0.4,0.3
|
||||
Virginia,VA,rate,LAUST510000000000003,2024,M05,May,2.8,0.3,-0.1,0.1
|
||||
Virginia,VA,rate,LAUST510000000000003,2024,M04,April,2.5,-0.2,-0.3,0.2
|
||||
Virginia,VA,rate,LAUST510000000000003,2024,M03,March,2.7,-0.2,0.3,0.0
|
||||
Virginia,VA,rate,LAUST510000000000003,2024,M02,February,2.9,0.1,0.3,0.1
|
||||
Virginia,VA,rate,LAUST510000000000003,2024,M01,January,2.8,0.4,0.1,-0.1
|
||||
Virginia,VA,rate,LAUST510000000000003,2023,M12,December,2.4,-0.2,-0.4,0.0
|
||||
Virginia,VA,rate,LAUST510000000000003,2023,M11,November,2.6,-0.1,-0.4,-0.2
|
||||
Virginia,VA,rate,LAUST510000000000003,2023,M10,October,2.7,-0.1,0.0,0.0
|
||||
Virginia,VA,rate,LAUST510000000000003,2023,M09,September,2.8,-0.2,0.0,0.0
|
||||
Virginia,VA,rate,LAUST510000000000003,2023,M08,August,3.0,0.3,0.3,-0.2
|
||||
Virginia,VA,rate,LAUST510000000000003,2023,M07,July,2.7,-0.1,0.4,-0.2
|
||||
Virginia,VA,rate,LAUST510000000000003,2023,M06,June,2.8,0.1,0.1,-0.1
|
||||
Virginia,VA,rate,LAUST510000000000003,2023,M05,May,2.7,0.4,-0.1,0.0
|
||||
Virginia,VA,rate,LAUST510000000000003,2023,M04,April,2.3,-0.4,-0.6,0.0
|
||||
Virginia,VA,rate,LAUST510000000000003,2023,M03,March,2.7,-0.1,0.3,0.2
|
||||
Virginia,VA,rate,LAUST510000000000003,2023,M02,February,2.8,-0.1,0.0,0.2
|
||||
Virginia,VA,rate,LAUST510000000000003,2023,M01,January,2.9,0.5,0.2,0.2
|
||||
Virginia,VA,rate,LAUST510000000000003,2022,M12,December,2.4,-0.4,-0.4,-0.2
|
||||
Virginia,VA,rate,LAUST510000000000003,2022,M11,November,2.8,0.1,-0.4,0.7
|
||||
Virginia,VA,rate,LAUST510000000000003,2022,M10,October,2.7,-0.1,-0.2,-0.3
|
||||
Virginia,VA,rate,LAUST510000000000003,2022,M09,September,2.8,-0.4,-0.1,-0.5
|
||||
Virginia,VA,rate,LAUST510000000000003,2022,M08,August,3.2,0.3,0.5,-0.8
|
||||
Virginia,VA,rate,LAUST510000000000003,2022,M07,July,2.9,0.0,0.6,-1.3
|
||||
Virginia,VA,rate,LAUST510000000000003,2022,M06,June,2.9,0.2,0.4,-1.8
|
||||
Virginia,VA,rate,LAUST510000000000003,2022,M05,May,2.7,0.4,0.1,-1.5
|
||||
Virginia,VA,rate,LAUST510000000000003,2022,M04,April,2.3,-0.2,-0.4,-1.8
|
||||
Virginia,VA,rate,LAUST510000000000003,2022,M03,March,2.5,-0.1,-0.1,-2.0
|
||||
Virginia,VA,rate,LAUST510000000000003,2022,M02,February,2.6,-0.1,0.5,-2.1
|
||||
Virginia,VA,rate,LAUST510000000000003,2022,M01,January,2.7,0.1,-0.3,-2.2
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2026,M04,April,151108,-19833,-23611,16516
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2026,M03,March,170941,-5707,15769,22744
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2026,M02,February,176648,1929,468,27269
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2026,M01,January,174719,19547,—,28217
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2025,M12,December,155172,-21008,1994,32539
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2025,M11,November,176180,—,12677,37362
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2025,M09,September,153178,-10325,-4511,19344
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2025,M08,August,163503,5138,15646,10513
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2025,M07,July,158365,676,23773,8989
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2025,M06,June,157689,9832,9492,15341
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2025,M05,May,147857,13265,-1522,19767
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2025,M04,April,134592,-13605,-11910,22672
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2025,M03,March,148197,-1182,25564,25079
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2025,M02,February,149379,2877,10561,19625
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2025,M01,January,146502,23869,12209,20444
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2024,M12,December,122633,-16185,-11201,14827
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2024,M11,November,138818,4525,-14172,18830
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2024,M10,October,134293,459,-15083,10255
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2024,M09,September,133834,-19156,-8514,7182
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2024,M08,August,152990,3614,24900,15745
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2024,M07,July,149376,7028,37456,25134
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2024,M06,June,142348,14258,19230,13273
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2024,M05,May,128090,16170,-1664,6582
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2024,M04,April,111920,-11198,-14138,8415
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2024,M03,March,123118,-6636,15312,1498
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2024,M02,February,129754,3696,9766,3161
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2024,M01,January,126058,18252,2020,-3567
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2023,M12,December,107806,-12182,-18846,1361
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2023,M11,November,119988,-4050,-17257,-2449
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2023,M10,October,124038,-2614,-204,4131
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2023,M09,September,126652,-10593,-2423,3818
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2023,M08,August,137245,13003,15737,-6206
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2023,M07,July,124242,-4833,20737,-4594
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2023,M06,June,129075,7567,7455,-403
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2023,M05,May,121508,18003,-5085,2107
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2023,M04,April,103505,-18115,-26120,2373
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2023,M03,March,121620,-4973,15175,11612
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2023,M02,February,126593,-3032,4156,15031
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2023,M01,January,129625,23180,9718,13002
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2022,M12,December,106445,-15992,-16389,-7033
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2022,M11,November,122437,2530,-21014,33828
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2022,M10,October,119907,-2927,-8929,-10963
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2022,M09,September,122834,-20617,-6644,-20572
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2022,M08,August,143451,14615,24050,-28853
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2022,M07,July,128836,-642,27704,-54425
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2022,M06,June,129478,10077,19470,-74219
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2022,M05,May,119401,18269,7839,-62801
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2022,M04,April,101132,-8876,-15491,-74994
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2022,M03,March,110008,-1554,-3470,-83811
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2022,M02,February,111562,-5061,22953,-89267
|
||||
Virginia,VA,unemployed,LAUST510000000000004,2022,M01,January,116623,3145,-14247,-91191
|
||||
Virginia,VA,employed,LAUST510000000000005,2026,M04,April,4333772,15266,29525,-83211
|
||||
Virginia,VA,employed,LAUST510000000000005,2026,M03,March,4318506,19132,-2045,-84326
|
||||
Virginia,VA,employed,LAUST510000000000005,2026,M02,February,4299374,-4873,-19736,-76463
|
||||
Virginia,VA,employed,LAUST510000000000005,2026,M01,January,4304247,-16304,—,-73801
|
||||
Virginia,VA,employed,LAUST510000000000005,2025,M12,December,4320551,1441,-37266,-69440
|
||||
Virginia,VA,employed,LAUST510000000000005,2025,M11,November,4319110,—,-40357,-63280
|
||||
Virginia,VA,employed,LAUST510000000000005,2025,M09,September,4357817,-1650,-46065,-52976
|
||||
Virginia,VA,employed,LAUST510000000000005,2025,M08,August,4359467,-44571,-30213,-46037
|
||||
Virginia,VA,employed,LAUST510000000000005,2025,M07,July,4404038,156,-12945,-53092
|
||||
Virginia,VA,employed,LAUST510000000000005,2025,M06,June,4403882,14202,1050,-44136
|
||||
Virginia,VA,employed,LAUST510000000000005,2025,M05,May,4389680,-27303,13843,-38008
|
||||
Virginia,VA,employed,LAUST510000000000005,2025,M04,April,4416983,14151,38935,-28812
|
||||
Virginia,VA,employed,LAUST510000000000005,2025,M03,March,4402832,26995,12841,-47226
|
||||
Virginia,VA,employed,LAUST510000000000005,2025,M02,February,4375837,-2211,-6553,-32538
|
||||
Virginia,VA,employed,LAUST510000000000005,2025,M01,January,4378048,-11943,-40512,-11822
|
||||
Virginia,VA,employed,LAUST510000000000005,2024,M12,December,4389991,7601,-20802,-7826
|
||||
Virginia,VA,employed,LAUST510000000000005,2024,M11,November,4382390,-36170,-23114,-38905
|
||||
Virginia,VA,employed,LAUST510000000000005,2024,M10,October,4418560,7767,-38570,-5497
|
||||
Virginia,VA,employed,LAUST510000000000005,2024,M09,September,4410793,5289,-37225,-11119
|
||||
Virginia,VA,employed,LAUST510000000000005,2024,M08,August,4405504,-51626,-22184,-23739
|
||||
Virginia,VA,employed,LAUST510000000000005,2024,M07,July,4457130,9112,11335,-14069
|
||||
Virginia,VA,employed,LAUST510000000000005,2024,M06,June,4448018,20330,-2040,-4586
|
||||
Virginia,VA,employed,LAUST510000000000005,2024,M05,May,4427688,-18107,19313,-3852
|
||||
Virginia,VA,employed,LAUST510000000000005,2024,M04,April,4445795,-4263,55925,7942
|
||||
Virginia,VA,employed,LAUST510000000000005,2024,M03,March,4450058,41683,52241,16828
|
||||
Virginia,VA,employed,LAUST510000000000005,2024,M02,February,4408375,18505,-12920,13906
|
||||
Virginia,VA,employed,LAUST510000000000005,2024,M01,January,4389870,-7947,-34187,37156
|
||||
Virginia,VA,employed,LAUST510000000000005,2023,M12,December,4397817,-23478,-24095,45379
|
||||
Virginia,VA,employed,LAUST510000000000005,2023,M11,November,4421295,-2762,-7948,92711
|
||||
Virginia,VA,employed,LAUST510000000000005,2023,M10,October,4424057,2145,-47142,82294
|
||||
Virginia,VA,employed,LAUST510000000000005,2023,M09,September,4421912,-7331,-30692,101130
|
||||
Virginia,VA,employed,LAUST510000000000005,2023,M08,August,4429243,-41956,-2297,109573
|
||||
Virginia,VA,employed,LAUST510000000000005,2023,M07,July,4471199,18595,33346,123675
|
||||
Virginia,VA,employed,LAUST510000000000005,2023,M06,June,4452604,21064,19374,130680
|
||||
Virginia,VA,employed,LAUST510000000000005,2023,M05,May,4431540,-6313,37071,119666
|
||||
Virginia,VA,employed,LAUST510000000000005,2023,M04,April,4437853,4623,85139,132779
|
||||
Virginia,VA,employed,LAUST510000000000005,2023,M03,March,4433230,38761,80792,136540
|
||||
Virginia,VA,employed,LAUST510000000000005,2023,M02,February,4394469,41755,65885,138072
|
||||
Virginia,VA,employed,LAUST510000000000005,2023,M01,January,4352714,276,10951,143720
|
||||
Virginia,VA,employed,LAUST510000000000005,2022,M12,December,4352438,23854,31656,126314
|
||||
Virginia,VA,employed,LAUST510000000000005,2022,M11,November,4328584,-13179,8914,118105
|
||||
Virginia,VA,employed,LAUST510000000000005,2022,M10,October,4341763,20981,-5761,138858
|
||||
Virginia,VA,employed,LAUST510000000000005,2022,M09,September,4320782,1112,-1142,159568
|
||||
Virginia,VA,employed,LAUST510000000000005,2022,M08,August,4319670,-27854,7796,150583
|
||||
Virginia,VA,employed,LAUST510000000000005,2022,M07,July,4347524,25600,42450,138275
|
||||
Virginia,VA,employed,LAUST510000000000005,2022,M06,June,4321924,10050,25234,166496
|
||||
Virginia,VA,employed,LAUST510000000000005,2022,M05,May,4311874,6800,55477,175727
|
||||
Virginia,VA,employed,LAUST510000000000005,2022,M04,April,4305074,8384,96080,179637
|
||||
Virginia,VA,employed,LAUST510000000000005,2022,M03,March,4296690,40293,70566,184973
|
||||
Virginia,VA,employed,LAUST510000000000005,2022,M02,February,4256397,47403,45918,180933
|
||||
Virginia,VA,employed,LAUST510000000000005,2022,M01,January,4208994,-17130,6089,159161
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2026,M04,April,4484880,-4567,5914,—
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2026,M03,March,4489447,13425,—,—
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2026,M02,February,4476022,-2944,—,—
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2026,M01,January,4478966,—,—,—
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2025,M12,December,4475723,-19567,-35272,-36901
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2025,M11,November,4495290,—,-27680,-25918
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2025,M09,September,4510995,-11975,-50576,-33632
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2025,M08,August,4522970,-39433,-14567,-35524
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2025,M07,July,4562403,832,10828,-44103
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2025,M06,June,4561571,24034,10542,-28795
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2025,M05,May,4537537,-14038,12321,-18241
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2025,M04,April,4551575,546,27025,-6140
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2025,M03,March,4551029,25813,38405,-22147
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2025,M02,February,4525216,666,4008,-12913
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2025,M01,January,4524550,11926,-28303,8622
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2024,M12,December,4512624,-8584,-32003,7001
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2024,M11,November,4521208,-31645,-37286,-20075
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2024,M10,October,4552853,8226,-53653,4758
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2024,M09,September,4544627,-13867,-45739,-3937
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2024,M08,August,4558494,-48012,2716,-7994
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2024,M07,July,4606506,16140,48791,11065
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2024,M06,June,4590366,34588,17190,8687
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2024,M05,May,4555778,-1937,17649,2730
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2024,M04,April,4557715,-15461,41787,16357
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2024,M03,March,4573176,35047,67553,18326
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2024,M02,February,4538129,22201,-3154,17067
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2024,M01,January,4515928,10305,-32167,33589
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2023,M12,December,4505623,-35660,-42941,46740
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2023,M11,November,4541283,-6812,-25205,90262
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2023,M10,October,4548095,-469,-47346,86425
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2023,M09,September,4548564,-17924,-33115,104948
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2023,M08,August,4566488,-28953,13440,103367
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2023,M07,July,4595441,13762,54083,119081
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2023,M06,June,4581679,28631,26829,130277
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2023,M05,May,4553048,11690,31986,121773
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2023,M04,April,4541358,-13492,59019,135152
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2023,M03,March,4554850,33788,95967,148152
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2023,M02,February,4521062,38723,70041,153103
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2023,M01,January,4482339,23456,20669,156722
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2022,M12,December,4458883,7862,15267,119281
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2022,M11,November,4451021,-10649,-12100,151933
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2022,M10,October,4461670,18054,-14690,127895
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2022,M09,September,4443616,-19505,-7786,138996
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2022,M08,August,4463121,-13239,31846,121730
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2022,M07,July,4476360,24958,70154,83850
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2022,M06,June,4451402,20127,44704,92277
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2022,M05,May,4431275,25069,63316,112926
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2022,M04,April,4406206,-492,80589,104643
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2022,M03,March,4406698,38739,67096,101162
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2022,M02,February,4367959,42342,68871,91666
|
||||
Virginia,VA,laborforce,LAUST510000000000006,2022,M01,January,4325617,-13985,-8158,67970
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2026,M04,April,3.9,-0.2,-0.4,0.6
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2026,M03,March,4.1,-0.4,0.3,0.5
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2026,M02,February,4.5,0.2,0.2,0.9
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2026,M01,January,4.3,0.5,—,0.9
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2025,M12,December,3.8,-0.5,-0.3,0.9
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2025,M11,November,4.3,—,0.1,1.1
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2025,M09,September,4.1,-0.1,0.2,1.0
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2025,M08,August,4.2,0.1,0.6,0.7
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2025,M07,July,4.1,0.2,0.8,0.7
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2025,M06,June,3.9,0.3,0.3,0.6
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2025,M05,May,3.6,0.3,0.0,0.8
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2025,M04,April,3.3,-0.3,-0.1,0.7
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2025,M03,March,3.6,0.0,0.7,0.7
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2025,M02,February,3.6,0.2,0.4,0.6
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2025,M01,January,3.4,0.5,0.2,0.5
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2024,M12,December,2.9,-0.3,-0.2,0.4
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2024,M11,November,3.2,0.0,-0.3,0.6
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2024,M10,October,3.2,0.1,-0.2,0.5
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2024,M09,September,3.1,-0.4,-0.2,0.4
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2024,M08,August,3.5,0.1,0.7,0.7
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2024,M07,July,3.4,0.1,0.8,0.8
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2024,M06,June,3.3,0.5,0.4,0.7
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2024,M05,May,2.8,0.2,-0.2,0.4
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2024,M04,April,2.6,-0.3,-0.3,0.6
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2024,M03,March,2.9,-0.1,0.4,0.4
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2024,M02,February,3.0,0.1,0.4,0.3
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2024,M01,January,2.9,0.4,0.2,0.2
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2023,M12,December,2.5,-0.1,-0.2,0.3
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2023,M11,November,2.6,-0.1,-0.2,0.0
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2023,M10,October,2.7,0.0,0.1,0.0
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2023,M09,September,2.7,-0.1,0.1,0.0
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2023,M08,August,2.8,0.2,0.4,-0.3
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2023,M07,July,2.6,0.0,0.6,-0.3
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2023,M06,June,2.6,0.2,0.1,-0.5
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2023,M05,May,2.4,0.4,-0.3,-0.3
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2023,M04,April,2.0,-0.5,-0.7,-0.5
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2023,M03,March,2.5,-0.2,0.3,-0.4
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2023,M02,February,2.7,0.0,0.1,-0.3
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2023,M01,January,2.7,0.5,0.0,-0.6
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2022,M12,December,2.2,-0.4,-0.5,-0.7
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2022,M11,November,2.6,-0.1,-0.5,-0.5
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2022,M10,October,2.7,0.0,-0.2,-0.9
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2022,M09,September,2.7,-0.4,-0.4,-1.2
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2022,M08,August,3.1,0.2,0.4,-1.7
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2022,M07,July,2.9,-0.2,0.4,-2.1
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2022,M06,June,3.1,0.4,0.2,-2.3
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2022,M05,May,2.7,0.2,-0.3,-2.2
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2022,M04,April,2.5,-0.4,-0.8,-2.4
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2022,M03,March,2.9,-0.1,0.0,-2.4
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2022,M02,February,3.0,-0.3,-0.1,-2.4
|
||||
DC Metro (MSA),DCMSA,rate,LAUMT114790000000003,2022,M01,January,3.3,0.4,-0.3,-2.2
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2026,M04,April,134887,-7625,-14103,17530
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2026,M03,March,142512,-11369,9750,13942
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2026,M02,February,153881,4891,4035,26463
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2026,M01,January,148990,16228,—,29463
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2025,M12,December,132762,-17084,-9222,30874
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2025,M11,November,149846,—,1080,36464
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2025,M09,September,141984,-6782,1978,33333
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2025,M08,August,148766,4173,21059,24412
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2025,M07,July,144593,4587,27236,21004
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2025,M06,June,140006,12299,11436,21521
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2025,M05,May,127707,10350,289,28055
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2025,M04,April,117357,-11213,-2170,27233
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2025,M03,March,128570,1152,26682,26472
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2025,M02,February,127418,7891,14036,20248
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2025,M01,January,119527,17639,7667,18380
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2024,M12,December,101888,-11494,-6763,15579
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2024,M11,November,113382,1522,-10972,23460
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2024,M10,October,111860,3209,-11729,16458
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2024,M09,September,108651,-15703,-9834,15756
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2024,M08,August,124354,765,24702,24658
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2024,M07,July,123589,5104,33465,32077
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2024,M06,June,118485,18833,16387,27149
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2024,M05,May,99652,9528,-7518,16979
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2024,M04,April,90124,-11974,-11023,18496
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2024,M03,March,102098,-5072,15789,14595
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2024,M02,February,107170,6023,17248,13523
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2024,M01,January,101147,14838,5745,7515
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2023,M12,December,86309,-3613,-6586,8950
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2023,M11,November,89922,-5480,-9774,1255
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2023,M10,October,95402,2507,3890,2373
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2023,M09,September,92895,-6801,1559,1567
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2023,M08,August,99696,8184,17023,-7650
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2023,M07,July,91512,176,19884,-11244
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2023,M06,June,91336,8663,3833,-15834
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2023,M05,May,82673,11045,-10974,-11621
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2023,M04,April,71628,-15875,-22004,-14027
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2023,M03,March,87503,-6144,10144,-12077
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2023,M02,February,93647,15,4980,-10517
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2023,M01,January,93632,16273,603,-16624
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2022,M12,December,77359,-11308,-13969,-19572
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2022,M11,November,88667,-4362,-18679,-16003
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2022,M10,October,93029,1701,-9727,-28827
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2022,M09,September,91328,-16018,-15842,-41618
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2022,M08,August,107346,4590,13052,-56788
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2022,M07,July,102756,-4414,17101,-70685
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2022,M06,June,107170,12876,7590,-77800
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2022,M05,May,94294,8639,-9870,-70421
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2022,M04,April,85655,-13925,-24601,-80689
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2022,M03,March,99580,-4584,2649,-78517
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2022,M02,February,104164,-6092,-506,-78482
|
||||
DC Metro (MSA),DCMSA,unemployed,LAUMT114790000000004,2022,M01,January,110256,13325,-11600,-74280
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2026,M04,April,3314544,5437,27940,-100507
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2026,M03,March,3309107,14881,-12943,-100900
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2026,M02,February,3294226,7622,-25994,-104407
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2026,M01,January,3286604,-35446,—,-115943
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2025,M12,December,3322050,1830,-32435,-90303
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2025,M11,November,3320220,—,-44717,-87209
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2025,M09,September,3354485,-10452,-57664,-64333
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2025,M08,August,3364937,-59474,-25897,-55689
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2025,M07,July,3424411,12262,9360,-50303
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2025,M06,June,3412149,21315,2142,-36664
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2025,M05,May,3390834,-24217,-7799,-33640
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2025,M04,April,3415051,5044,12504,-21895
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2025,M03,March,3410007,11374,-2346,-30593
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2025,M02,February,3398633,-3914,-8796,-9199
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2025,M01,January,3402547,-9806,-23055,1575
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2024,M12,December,3412353,4924,-6465,14911
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2024,M11,November,3407429,-18173,-13197,-7799
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2024,M10,October,3425602,6784,-49112,14188
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2024,M09,September,3418818,-1808,-29995,11252
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2024,M08,August,3420626,-54088,-3848,-3998
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2024,M07,July,3474714,25901,37768,6768
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2024,M06,June,3448813,24339,8213,105
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2024,M05,May,3424474,-12472,16642,285
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2024,M04,April,3436946,-3654,35974,12450
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2024,M03,March,3440600,32768,43158,18724
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2024,M02,February,3407832,6860,-7396,15406
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2024,M01,January,3400972,3530,-10442,27786
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2023,M12,December,3397442,-17786,-10124,32810
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2023,M11,November,3415228,3814,-9396,59989
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2023,M10,October,3411414,3848,-56532,45834
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2023,M09,September,3407566,-17058,-41142,53897
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2023,M08,August,3424624,-43322,435,63188
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2023,M07,July,3467946,19238,43450,74616
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2023,M06,June,3448708,24519,26832,84972
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2023,M05,May,3424189,-307,31763,74212
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2023,M04,April,3424496,2620,51310,82921
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2023,M03,March,3421876,29450,57244,79849
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2023,M02,February,3392426,19240,37187,80592
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2023,M01,January,3373186,8554,7606,93800
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2022,M12,December,3364632,9393,10963,74645
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2022,M11,November,3355239,-10341,-6197,72388
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2022,M10,October,3365580,11911,-27750,94572
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2022,M09,September,3353669,-7767,-10067,109307
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2022,M08,August,3361436,-31894,11459,114400
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2022,M07,July,3393330,29594,51755,106161
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2022,M06,June,3363736,13759,21709,119815
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2022,M05,May,3349977,8402,38143,131470
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2022,M04,April,3341575,-452,62189,132571
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2022,M03,March,3342027,30193,52040,143329
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2022,M02,February,3311834,32448,28983,137427
|
||||
DC Metro (MSA),DCMSA,employed,LAUMT114790000000005,2022,M01,January,3279386,-10601,8378,122411
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2026,M04,April,3449431,-2188,13837,—
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2026,M03,March,3451619,3512,—,—
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2026,M02,February,3448107,12513,—,—
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2026,M01,January,3435594,—,—,—
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2025,M12,December,3454812,-15254,-41657,-59429
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2025,M11,November,3470066,—,-43637,-50745
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2025,M09,September,3496469,-17234,-55686,-31000
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2025,M08,August,3513703,-55301,-4838,-31277
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2025,M07,July,3569004,16849,36596,-29299
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2025,M06,June,3552155,33614,13578,-15143
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2025,M05,May,3518541,-13867,-7510,-5585
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2025,M04,April,3532408,-6169,10334,5338
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2025,M03,March,3538577,12526,24336,-4121
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2025,M02,February,3526051,3977,5240,11049
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2025,M01,January,3522074,7833,-15388,19955
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2024,M12,December,3514241,-6570,-13228,30490
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2024,M11,November,3520811,-16651,-24169,15661
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2024,M10,October,3537462,9993,-60841,30646
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2024,M09,September,3527469,-17511,-39829,27008
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2024,M08,August,3544980,-53323,20854,20660
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2024,M07,July,3598303,31005,71233,38845
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2024,M06,June,3567298,43172,24600,27254
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2024,M05,May,3524126,-2944,9124,17264
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2024,M04,April,3527070,-15628,24951,30946
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2024,M03,March,3542698,27696,58947,33319
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2024,M02,February,3515002,12883,9852,28929
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2024,M01,January,3502119,18368,-4697,35301
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2023,M12,December,3483751,-21399,-16710,41760
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2023,M11,November,3505150,-1666,-19170,61244
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2023,M10,October,3506816,6355,-52642,48207
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2023,M09,September,3500461,-23859,-39583,55464
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2023,M08,August,3524320,-35138,17458,55538
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2023,M07,July,3559458,19414,63334,63372
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2023,M06,June,3540044,33182,30665,69138
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2023,M05,May,3506862,10738,20789,62591
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2023,M04,April,3496124,-13255,29306,68894
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2023,M03,March,3509379,23306,67388,67772
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2023,M02,February,3486073,19255,42167,70075
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2023,M01,January,3466818,24827,8209,77176
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2022,M12,December,3441991,-1915,-3006,55073
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2022,M11,November,3443906,-14703,-24876,56385
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2022,M10,October,3458609,13612,-37477,65745
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2022,M09,September,3444997,-23785,-25909,67689
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2022,M08,August,3468782,-27304,24511,57612
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2022,M07,July,3496086,25180,68856,35476
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2022,M06,June,3470906,26635,29299,42015
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2022,M05,May,3444271,17041,28273,61049
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2022,M04,April,3427230,-14377,37588,51882
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2022,M03,March,3441607,25609,54689,64812
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2022,M02,February,3415998,26356,28477,58945
|
||||
DC Metro (MSA),DCMSA,laborforce,LAUMT114790000000006,2022,M01,January,3389642,2724,-3222,48131
|
||||
|
214
dc_md_va_unemployment.py
Normal file
214
dc_md_va_unemployment.py
Normal file
@ -0,0 +1,214 @@
|
||||
"""
|
||||
DC/MD/VA Unemployment Dashboard
|
||||
Pulls LAUS data for DC, Maryland, Virginia (state) plus the DC-Arlington-Alexandria metro.
|
||||
Outputs a formatted console table and saves results to CSV.
|
||||
"""
|
||||
|
||||
import requests
|
||||
import csv
|
||||
import sys
|
||||
from datetime import datetime
|
||||
from config import BLS_API_KEY, BLS_API_BASE
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Series definitions
|
||||
# Format: LAUS[S=SA / U=unadj][area code 15 chars][measure 2 chars]
|
||||
# Measures: 03=rate 04=unemployed 05=employed 06=labor force
|
||||
# ---------------------------------------------------------------------------
|
||||
SERIES = {
|
||||
# DC
|
||||
"DC_rate": "LAUST110000000000003",
|
||||
"DC_unemployed": "LAUST110000000000004",
|
||||
"DC_employed": "LAUST110000000000005",
|
||||
"DC_laborforce": "LAUST110000000000006",
|
||||
# Maryland
|
||||
"MD_rate": "LAUST240000000000003",
|
||||
"MD_unemployed": "LAUST240000000000004",
|
||||
"MD_employed": "LAUST240000000000005",
|
||||
"MD_laborforce": "LAUST240000000000006",
|
||||
# Virginia
|
||||
"VA_rate": "LAUST510000000000003",
|
||||
"VA_unemployed": "LAUST510000000000004",
|
||||
"VA_employed": "LAUST510000000000005",
|
||||
"VA_laborforce": "LAUST510000000000006",
|
||||
# DC-Arlington-Alexandria MSA (state FIPS 11 + CBSA 47900)
|
||||
"DCMSA_rate": "LAUMT114790000000003",
|
||||
"DCMSA_unemployed":"LAUMT114790000000004",
|
||||
"DCMSA_employed": "LAUMT114790000000005",
|
||||
"DCMSA_laborforce":"LAUMT114790000000006",
|
||||
}
|
||||
|
||||
LABELS = {
|
||||
"DC": "District of Columbia",
|
||||
"MD": "Maryland",
|
||||
"VA": "Virginia",
|
||||
"DCMSA": "DC Metro (MSA)",
|
||||
}
|
||||
|
||||
MEASURES = ["rate", "unemployed", "employed", "laborforce"]
|
||||
MEASURE_LABELS = {
|
||||
"rate": "Unemp Rate %",
|
||||
"unemployed": "Unemployed",
|
||||
"employed": "Employed",
|
||||
"laborforce": "Labor Force",
|
||||
}
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def fetch(series_ids: list[str], start_year: int, end_year: int) -> dict:
|
||||
"""POST to BLS API v2, return dict keyed by seriesID."""
|
||||
payload = {
|
||||
"seriesid": series_ids,
|
||||
"startyear": str(start_year),
|
||||
"endyear": str(end_year),
|
||||
"registrationkey": BLS_API_KEY,
|
||||
"catalog": True,
|
||||
"calculations": True,
|
||||
"annualaverage": False,
|
||||
}
|
||||
r = requests.post(f"{BLS_API_BASE}/timeseries/data/", json=payload, timeout=30)
|
||||
r.raise_for_status()
|
||||
body = r.json()
|
||||
if body["status"] != "REQUEST_SUCCEEDED":
|
||||
raise RuntimeError(f"BLS API error: {body['message']}")
|
||||
return {s["seriesID"]: s["data"] for s in body["Results"]["series"]}
|
||||
|
||||
|
||||
def latest(data: list[dict]) -> dict | None:
|
||||
"""Return the most recent non-null observation."""
|
||||
for obs in data:
|
||||
if obs["value"] != "-":
|
||||
return obs
|
||||
return None
|
||||
|
||||
|
||||
def get_calc(obs: dict, window: str) -> str:
|
||||
"""Pull a net_change or pct_change value safely."""
|
||||
try:
|
||||
return obs["calculations"]["net_changes"].get(window, "—")
|
||||
except (KeyError, TypeError):
|
||||
return "—"
|
||||
|
||||
|
||||
def fmt_num(val: str, is_rate: bool = False) -> str:
|
||||
if val in (None, "—", "-"):
|
||||
return "—"
|
||||
try:
|
||||
n = float(val)
|
||||
return f"{n:.1f}%" if is_rate else f"{n:,.0f}"
|
||||
except ValueError:
|
||||
return val
|
||||
|
||||
|
||||
def fmt_chg(val: str, is_rate: bool = False) -> str:
|
||||
if val in (None, "—", ""):
|
||||
return "—"
|
||||
try:
|
||||
n = float(val)
|
||||
sign = "+" if n > 0 else ""
|
||||
return f"{sign}{n:.1f}pp" if is_rate else f"{sign}{n:,.0f}"
|
||||
except ValueError:
|
||||
return val
|
||||
|
||||
|
||||
def print_dashboard(results: dict):
|
||||
geo_keys = ["DC", "MD", "VA", "DCMSA"]
|
||||
now = datetime.now().strftime("%Y-%m-%d %H:%M")
|
||||
|
||||
print()
|
||||
print("=" * 72)
|
||||
print(f" DC / MD / VA UNEMPLOYMENT DASHBOARD pulled {now}")
|
||||
print("=" * 72)
|
||||
|
||||
for geo in geo_keys:
|
||||
rate_series = SERIES[f"{geo}_rate"]
|
||||
rate_data = results.get(rate_series, [])
|
||||
obs = latest(rate_data)
|
||||
if not obs:
|
||||
print(f"\n {LABELS[geo]}: no data\n")
|
||||
continue
|
||||
|
||||
period = f"{obs['periodName']} {obs['year']}"
|
||||
rate = fmt_num(obs["value"], is_rate=True)
|
||||
mom = fmt_chg(get_calc(obs, "1"), is_rate=True)
|
||||
yoy = fmt_chg(get_calc(obs, "12"), is_rate=True)
|
||||
|
||||
print()
|
||||
print(f" {LABELS[geo]} ({period})")
|
||||
print(f" {'─' * 60}")
|
||||
print(f" {'Unemployment Rate:':<28} {rate:>8} MoM: {mom:>8} YoY: {yoy:>8}")
|
||||
|
||||
for measure in ["unemployed", "employed", "laborforce"]:
|
||||
sid = SERIES[f"{geo}_{measure}"]
|
||||
data = results.get(sid, [])
|
||||
o = latest(data)
|
||||
if not o:
|
||||
continue
|
||||
label = MEASURE_LABELS[measure]
|
||||
val = fmt_num(o["value"])
|
||||
m = fmt_chg(get_calc(o, "1"))
|
||||
y = fmt_chg(get_calc(o, "12"))
|
||||
print(f" {label + ':':<28} {val:>10} MoM: {m:>8} YoY: {y:>8}")
|
||||
|
||||
print()
|
||||
print("=" * 72)
|
||||
print(" Note: Not seasonally adjusted. MoM/YoY = net change.")
|
||||
print("=" * 72)
|
||||
print()
|
||||
|
||||
|
||||
def save_csv(results: dict, path: str):
|
||||
rows = []
|
||||
geo_keys = ["DC", "MD", "VA", "DCMSA"]
|
||||
|
||||
for geo in geo_keys:
|
||||
for measure in MEASURES:
|
||||
key = f"{geo}_{measure}"
|
||||
sid = SERIES[key]
|
||||
data = results.get(sid, [])
|
||||
for obs in data:
|
||||
if obs["value"] == "-":
|
||||
continue
|
||||
rows.append({
|
||||
"geography": LABELS[geo],
|
||||
"geo_code": geo,
|
||||
"measure": measure,
|
||||
"series_id": sid,
|
||||
"year": obs["year"],
|
||||
"period": obs["period"],
|
||||
"period_name": obs["periodName"],
|
||||
"value": obs["value"],
|
||||
"mom_chg": get_calc(obs, "1"),
|
||||
"qoq_chg": get_calc(obs, "3"),
|
||||
"yoy_chg": get_calc(obs, "12"),
|
||||
})
|
||||
|
||||
if not rows:
|
||||
print("No data to save.")
|
||||
return
|
||||
|
||||
fieldnames = list(rows[0].keys())
|
||||
with open(path, "w", newline="") as f:
|
||||
w = csv.DictWriter(f, fieldnames=fieldnames)
|
||||
w.writeheader()
|
||||
w.writerows(rows)
|
||||
print(f" Saved {len(rows)} rows → {path}")
|
||||
|
||||
|
||||
def main():
|
||||
current_year = datetime.now().year
|
||||
start_year = current_year - 4 # 5 years of history (within 20-yr v2 limit)
|
||||
|
||||
print(f"Fetching {len(SERIES)} LAUS series ({start_year}–{current_year})...")
|
||||
|
||||
# API allows 50 series per call; we have 16, so one call is fine
|
||||
results = fetch(list(SERIES.values()), start_year, current_year)
|
||||
|
||||
print_dashboard(results)
|
||||
|
||||
out_csv = "dc_md_va_unemployment.csv"
|
||||
save_csv(results, out_csv)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
179
qcew_field_schema.md
Normal file
179
qcew_field_schema.md
Normal file
@ -0,0 +1,179 @@
|
||||
# QCEW File Field Schemas
|
||||
|
||||
Source: https://data.bls.gov/cew/doc/layouts/
|
||||
|
||||
QCEW bulk data is available as CSV files by area, by industry, or as singlefiles (no title columns).
|
||||
Download: https://www.bls.gov/cew/downloadable-data-files.htm
|
||||
|
||||
---
|
||||
|
||||
## Quarterly CSV Field Layout
|
||||
|
||||
| # | Field Name | Type | Max Len | Description |
|
||||
|---|-----------|------|---------|-------------|
|
||||
| 1 | area_fips | Text | 5 | 5-character FIPS code |
|
||||
| 2 | own_code | Text | 1 | Ownership code |
|
||||
| 3 | industry_code | Text | 6 | NAICS or SuperSector code |
|
||||
| 4 | agglvl_code | Text | 2 | Aggregation level code |
|
||||
| 5 | size_code | Text | 1 | Size code |
|
||||
| 6 | year | Text | 4 | Year |
|
||||
| 7 | qtr | Text | 1 | Quarter (1–4; A for annual) |
|
||||
| 8 | disclosure_code | Text | 1 | `' '`=disclosed; `N`=not disclosed |
|
||||
| 9 | area_title | Text | 80 | Area name (excluded from singlefile) |
|
||||
| 10 | own_title | Text | 80 | Ownership description (excluded from singlefile) |
|
||||
| 11 | industry_title | Text | 80 | Industry description (excluded from singlefile) |
|
||||
| 12 | agglvl_title | Text | 80 | Aggregation level title (excluded from singlefile) |
|
||||
| 13 | size_title | Text | 80 | Size class title (excluded from singlefile) |
|
||||
| 14 | qtrly_estabs | Numeric | 8 | Count of establishments for the quarter |
|
||||
| 15 | month1_emplvl | Numeric | 9 | Employment, month 1 of quarter |
|
||||
| 16 | month2_emplvl | Numeric | 9 | Employment, month 2 of quarter |
|
||||
| 17 | month3_emplvl | Numeric | 9 | Employment, month 3 of quarter |
|
||||
| 18 | total_qtrly_wages | Numeric | 15 | Total wages for the quarter |
|
||||
| 19 | taxable_qtrly_wages | Numeric | 15 | Taxable wages for the quarter |
|
||||
| 20 | qtrly_contributions | Numeric | 13 | UI contributions for the quarter |
|
||||
| 21 | avg_wkly_wage | Numeric | 8 | Average weekly wage for the quarter |
|
||||
| 22 | lq_disclosure_code | Text | 1 | Location quotient disclosure code |
|
||||
| 23 | lq_qtrly_estabs | Numeric | 8 | LQ of establishment count vs. U.S. |
|
||||
| 24 | lq_month1_emplvl | Numeric | 8 | LQ of month 1 employment vs. U.S. |
|
||||
| 25 | lq_month2_emplvl | Numeric | 8 | LQ of month 2 employment vs. U.S. |
|
||||
| 26 | lq_month3_emplvl | Numeric | 8 | LQ of month 3 employment vs. U.S. |
|
||||
| 27 | lq_total_qtrly_wages | Numeric | 8 | LQ of total quarterly wages vs. U.S. |
|
||||
| 28 | lq_taxable_qtrly_wages | Numeric | 8 | LQ of taxable quarterly wages vs. U.S. |
|
||||
| 29 | lq_qtrly_contributions | Numeric | 8 | LQ of quarterly contributions vs. U.S. |
|
||||
| 30 | lq_avg_wkly_wage | Numeric | 8 | LQ of average weekly wage vs. U.S. |
|
||||
| 31 | oty_disclosure_code | Text | 1 | Over-the-year disclosure code |
|
||||
| 32 | oty_qtrly_estabs_chg | Numeric | 8 | OTY change in establishment count |
|
||||
| 33 | oty_qtrly_estabs_pct_chg | Numeric | 8 | OTY % change in establishment count |
|
||||
| 34 | oty_month1_emplvl_chg | Numeric | 9 | OTY change in month 1 employment |
|
||||
| 35 | oty_month1_emplvl_pct_chg | Numeric | 8 | OTY % change in month 1 employment |
|
||||
| 36 | oty_month2_emplvl_chg | Numeric | 9 | OTY change in month 2 employment |
|
||||
| 37 | oty_month2_emplvl_pct_chg | Numeric | 8 | OTY % change in month 2 employment |
|
||||
| 38 | oty_month3_emplvl_chg | Numeric | 9 | OTY change in month 3 employment |
|
||||
| 39 | oty_month3_emplvl_pct_chg | Numeric | 8 | OTY % change in month 3 employment |
|
||||
| 40 | oty_total_qtrly_wages_chg | Numeric | 15 | OTY change in total wages |
|
||||
| 41 | oty_total_qtrly_wages_pct_chg | Numeric | 8 | OTY % change in total wages |
|
||||
| 42 | oty_taxable_qtrly_wages_chg | Numeric | 15 | OTY change in taxable wages |
|
||||
| 43 | oty_taxable_qtrly_wages_pct_chg | Numeric | 8 | OTY % change in taxable wages |
|
||||
| 44 | oty_qtrly_contributions_chg | Numeric | 13 | OTY change in contributions |
|
||||
| 45 | oty_qtrly_contributions_pct_chg | Numeric | 8 | OTY % change in contributions |
|
||||
| 46 | oty_avg_wkly_wage_chg | Numeric | 8 | OTY change in avg weekly wage |
|
||||
| 47 | oty_avg_wkly_wage_pct_chg | Numeric | 8 | OTY % change in avg weekly wage |
|
||||
|
||||
---
|
||||
|
||||
## Annual CSV Field Layout
|
||||
|
||||
| # | Field Name | Type | Max Len | Description |
|
||||
|---|-----------|------|---------|-------------|
|
||||
| 1 | area_fips | Text | 5 | 5-character FIPS code |
|
||||
| 2 | own_code | Text | 1 | Ownership code |
|
||||
| 3 | industry_code | Text | 6 | NAICS or SuperSector code |
|
||||
| 4 | agglvl_code | Text | 2 | Aggregation level code |
|
||||
| 5 | size_code | Text | 1 | Size code |
|
||||
| 6 | year | Text | 4 | Year |
|
||||
| 7 | qtr | Text | 1 | Always `A` for annual |
|
||||
| 8 | disclosure_code | Text | 1 | `' '`=disclosed; `N`=not disclosed |
|
||||
| 9 | area_title | Text | 80 | Area name (excluded from singlefile) |
|
||||
| 10 | own_title | Text | 80 | Ownership description (excluded from singlefile) |
|
||||
| 11 | industry_title | Text | 80 | Industry description (excluded from singlefile) |
|
||||
| 12 | agglvl_title | Text | 80 | Aggregation level title (excluded from singlefile) |
|
||||
| 13 | size_title | Text | 80 | Size class title (excluded from singlefile) |
|
||||
| 14 | annual_avg_estabs | Numeric | 8 | Annual avg of quarterly establishment counts |
|
||||
| 15 | annual_avg_emplvl | Numeric | 9 | Annual avg of monthly employment levels |
|
||||
| 16 | total_annual_wages | Numeric | 15 | Sum of 4 quarterly total wages |
|
||||
| 17 | taxable_annual_wages | Numeric | 15 | Sum of 4 quarterly taxable wages |
|
||||
| 18 | annual_contributions | Numeric | 13 | Sum of 4 quarterly UI contributions |
|
||||
| 19 | annual_avg_wkly_wage | Numeric | 8 | Avg weekly wage based on 12-month employment and total annual wages |
|
||||
| 20 | avg_annual_pay | Numeric | 8 | Avg annual pay (wages ÷ employment) |
|
||||
| 21 | lq_disclosure_code | Text | 1 | LQ disclosure code |
|
||||
| 22 | lq_annual_avg_estabs | Numeric | 8 | LQ of annual avg establishment count vs. U.S. |
|
||||
| 23 | lq_annual_avg_emplvl | Numeric | 8 | LQ of annual avg employment vs. U.S. |
|
||||
| 24 | lq_total_annual_wages | Numeric | 8 | LQ of total annual wages vs. U.S. |
|
||||
| 25 | lq_taxable_annual_wages | Numeric | 8 | LQ of taxable annual wages vs. U.S. |
|
||||
| 26 | lq_annual_contributions | Numeric | 8 | LQ of annual contributions vs. U.S. |
|
||||
| 27 | lq_annual_avg_wkly_wage | Numeric | 8 | LQ of annual avg weekly wage vs. U.S. |
|
||||
| 28 | lq_avg_annual_pay | Numeric | 8 | LQ of avg annual pay vs. U.S. |
|
||||
| 29 | oty_disclosure_code | Text | 1 | OTY disclosure code |
|
||||
| 30 | oty_annual_avg_estabs_chg | Numeric | 8 | OTY change in annual avg estabs |
|
||||
| 31 | oty_annual_avg_estabs_pct_chg | Numeric | 8 | OTY % change in annual avg estabs |
|
||||
| 32 | oty_annual_avg_emplvl_chg | Numeric | 9 | OTY change in annual avg employment |
|
||||
| 33 | oty_annual_avg_emplvl_pct_chg | Numeric | 8 | OTY % change in annual avg employment |
|
||||
| 34 | oty_total_annual_wages_chg | Numeric | 15 | OTY change in total annual wages |
|
||||
| 35 | oty_total_annual_wages_pct_chg | Numeric | 8 | OTY % change in total annual wages |
|
||||
| 36 | oty_taxable_annual_wages_chg | Numeric | 15 | OTY change in taxable annual wages |
|
||||
| 37 | oty_taxable_annual_wages_pct_chg | Numeric | 8 | OTY % change in taxable annual wages |
|
||||
| 38 | oty_annual_contributions_chg | Numeric | 13 | OTY change in annual contributions |
|
||||
| 39 | oty_annual_contributions_pct_chg | Numeric | 8 | OTY % change in annual contributions |
|
||||
| 40 | oty_annual_avg_wkly_wage_chg | Numeric | 8 | OTY change in annual avg weekly wage |
|
||||
| 41 | oty_annual_avg_wkly_wage_pct_chg | Numeric | 8 | OTY % change in annual avg weekly wage |
|
||||
| 42 | oty_avg_annual_pay_chg | Numeric | 8 | OTY change in avg annual pay |
|
||||
| 43 | oty_avg_annual_pay_pct_chg | Numeric | 8 | OTY % change in avg annual pay |
|
||||
|
||||
---
|
||||
|
||||
## Decode Tables
|
||||
|
||||
**own_code values:**
|
||||
| Code | Ownership |
|
||||
|------|-----------|
|
||||
| 0 | Total, all ownerships |
|
||||
| 1 | Federal government |
|
||||
| 2 | State government |
|
||||
| 3 | Local government |
|
||||
| 4 | International government |
|
||||
| 5 | Private |
|
||||
| 8 | Total, all government |
|
||||
| 9 | Total, all ownerships (same as 0) |
|
||||
|
||||
**agglvl_code examples:**
|
||||
| Code | Level |
|
||||
|------|-------|
|
||||
| 10 | U.S., NAICS supersectors |
|
||||
| 11 | U.S., NAICS sector |
|
||||
| 12 | U.S., NAICS 3-digit |
|
||||
| 13 | U.S., NAICS 4-digit |
|
||||
| 14 | U.S., NAICS 5-digit |
|
||||
| 15 | U.S., NAICS 6-digit |
|
||||
| 50 | State, NAICS supersectors |
|
||||
| 51 | State, NAICS sector |
|
||||
| 74 | County, NAICS 5-digit |
|
||||
| 75 | County, NAICS 6-digit |
|
||||
| 76 | MSA, NAICS supersectors |
|
||||
|
||||
**size_code values:**
|
||||
| Code | Employment size class |
|
||||
|------|----------------------|
|
||||
| 0 | All establishment sizes |
|
||||
| 1 | < 5 employees |
|
||||
| 2 | 5–9 |
|
||||
| 3 | 10–19 |
|
||||
| 4 | 20–49 |
|
||||
| 5 | 50–99 |
|
||||
| 6 | 100–249 |
|
||||
| 7 | 250–499 |
|
||||
| 8 | 500–999 |
|
||||
| 9 | 1,000+ |
|
||||
|
||||
---
|
||||
|
||||
## Download URLs
|
||||
|
||||
**By-area quarterly (single year):**
|
||||
```
|
||||
https://data.bls.gov/cew/data/files/{YEAR}/csv/{YEAR}_qtrly_by_area.zip
|
||||
```
|
||||
|
||||
**By-industry quarterly:**
|
||||
```
|
||||
https://data.bls.gov/cew/data/files/{YEAR}/csv/{YEAR}_qtrly_by_industry.zip
|
||||
```
|
||||
|
||||
**Annual singlefile (all areas, all industries, one row per combo):**
|
||||
```
|
||||
https://data.bls.gov/cew/data/files/{YEAR}/csv/{YEAR}_annual_singlefile.zip
|
||||
```
|
||||
|
||||
**QCEW API (individual series):**
|
||||
```
|
||||
https://data.bls.gov/cew/apps/data_views/data_views.htm
|
||||
```
|
||||
296
series_id_formats.md
Normal file
296
series_id_formats.md
Normal file
@ -0,0 +1,296 @@
|
||||
# BLS Series ID Formats
|
||||
|
||||
Series IDs are the primary key for every BLS API call and bulk data file.
|
||||
All IDs use a two-letter survey prefix followed by survey-specific coded segments.
|
||||
|
||||
---
|
||||
|
||||
## LAUS — Local Area Unemployment Statistics (prefix: LA)
|
||||
|
||||
**Series ID:** `LA[S][AAAAAAAAAAAAAAA][MM]`
|
||||
|
||||
| Pos | Length | Field | Notes |
|
||||
|-----|--------|-------|-------|
|
||||
| 1-2 | 2 | Prefix | Always `LA` |
|
||||
| 3 | 1 | Seasonal adj | `S`=seasonally adjusted, `U`=unadjusted |
|
||||
| 4-18 | 15 | Area code | See area type prefixes below |
|
||||
| 19-20 | 2 | Measure code | See table below |
|
||||
|
||||
**Area code prefixes:**
|
||||
| Prefix | Geography |
|
||||
|--------|-----------|
|
||||
| `ST` | State (2-digit FIPS + 12 zeros) |
|
||||
| `MT` | Metropolitan statistical area |
|
||||
| `MD` | Metro division |
|
||||
| `CN` | County (5-digit FIPS + 10 zeros) |
|
||||
| `CS` | City/sub-county |
|
||||
| `RD` | Region/division |
|
||||
|
||||
**Measure codes:**
|
||||
| Code | Measure |
|
||||
|------|---------|
|
||||
| 03 | Unemployment rate |
|
||||
| 04 | Unemployment (level) |
|
||||
| 05 | Employment |
|
||||
| 06 | Labor force |
|
||||
| 07 | Employment-population ratio |
|
||||
| 08 | Labor force participation rate |
|
||||
| 09 | Civilian noninstitutional population |
|
||||
|
||||
**Examples:**
|
||||
```
|
||||
LAUST110000000000003 → DC, state, seasonally adj, unemployment rate
|
||||
LAUST110000000000005 → DC, state, seasonally adj, employment
|
||||
LAUCN110010000000003 → DC County, unadjusted, unemployment rate
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## CES — Current Employment Statistics, National (prefix: CE)
|
||||
|
||||
**Series ID:** `CE[S/U][IIIIIIII][DD]`
|
||||
|
||||
| Pos | Length | Field | Notes |
|
||||
|-----|--------|-------|-------|
|
||||
| 1-2 | 2 | Prefix | `CE` (national) or `SM` (state/metro) |
|
||||
| 3 | 1 | Seasonal adj | `S`=seasonally adjusted, `U`=unadjusted |
|
||||
| 4-11 | 8 | Industry code | Supersector (2) + NAICS-derived (6) |
|
||||
| 12-13 | 2 | Data type | See table below |
|
||||
|
||||
**Industry code — supersector first 2 digits:**
|
||||
| Code | Supersector |
|
||||
|------|-------------|
|
||||
| 00 | Total nonfarm |
|
||||
| 05 | Total private |
|
||||
| 06 | Goods-producing |
|
||||
| 07 | Service-providing |
|
||||
| 08 | Private service-providing |
|
||||
| 09 | Government |
|
||||
| 10 | Mining & logging |
|
||||
| 20 | Construction |
|
||||
| 30 | Manufacturing |
|
||||
| 31 | Durable goods |
|
||||
| 32 | Nondurable goods |
|
||||
| 40 | Trade, transportation, utilities |
|
||||
| 41 | Wholesale trade |
|
||||
| 42 | Retail trade |
|
||||
| 43 | Trans., warehousing, utilities |
|
||||
| 50 | Information |
|
||||
| 55 | Financial activities |
|
||||
| 60 | Professional & business services |
|
||||
| 65 | Education & health services |
|
||||
| 70 | Leisure & hospitality |
|
||||
| 80 | Other services |
|
||||
| 90 | Government |
|
||||
|
||||
**Data type codes (most common):**
|
||||
| Code | Measure |
|
||||
|------|---------|
|
||||
| 01 | All employees (thousands) |
|
||||
| 02 | Average weekly hours, all employees |
|
||||
| 03 | Average hourly earnings, all employees |
|
||||
| 06 | Production/nonsupervisory employees |
|
||||
| 07 | Avg weekly hours, prod/nonsupervisory |
|
||||
| 08 | Avg hourly earnings, prod/nonsupervisory |
|
||||
| 10 | Women employees |
|
||||
| 11 | Avg weekly earnings, all employees |
|
||||
|
||||
**Examples:**
|
||||
```
|
||||
CES0000000001 → Total nonfarm, SA, all employees
|
||||
CES0500000001 → Total private, SA, all employees
|
||||
CES3000000001 → Manufacturing, SA, all employees
|
||||
CES9000000001 → Government, SA, all employees
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## SM — State & Metro Employment (CES State, prefix: SM)
|
||||
|
||||
**Series ID:** `SM[S/U][SS][MMMMM][IIIIIIII][DD]`
|
||||
|
||||
| Pos | Length | Field | Notes |
|
||||
|-----|--------|-------|-------|
|
||||
| 1-2 | 2 | Prefix | `SM` |
|
||||
| 3 | 1 | Seasonal adj | `S` or `U` |
|
||||
| 4-5 | 2 | State FIPS | e.g., `11`=DC, `24`=MD, `51`=VA |
|
||||
| 6-10 | 5 | Area code | `00000`=statewide; MSA codes otherwise |
|
||||
| 11-18 | 8 | Industry code | Same as CES above |
|
||||
| 19-20 | 2 | Data type | Same as CES above |
|
||||
|
||||
**Examples:**
|
||||
```
|
||||
SMS110000000000001 → DC statewide, SA, all employees
|
||||
SMS240000000000001 → Maryland statewide, SA, all employees
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## QCEW — Quarterly Census of Employment & Wages (prefix: EN)
|
||||
|
||||
**Series ID:** `EN[U][AAAAA][D][Z][O][IIIIII]`
|
||||
|
||||
| Pos | Length | Field | Notes |
|
||||
|-----|--------|-------|-------|
|
||||
| 1-2 | 2 | Prefix | `EN` |
|
||||
| 3 | 1 | Seasonal adj | Always `U` (unadjusted) |
|
||||
| 4-8 | 5 | Area code | US=`00000`; state FIPS; county FIPS |
|
||||
| 9 | 1 | Data type | `1`=qtrly; `2`=annual |
|
||||
| 10 | 1 | Size code | `0`=all sizes; `1`–`9`=specific size classes |
|
||||
| 11 | 1 | Ownership code | See table below |
|
||||
| 12-17 | 6 | Industry code | NAICS or supersector |
|
||||
|
||||
**Ownership codes:**
|
||||
| Code | Ownership |
|
||||
|------|-----------|
|
||||
| 0 | All ownerships |
|
||||
| 1 | Federal government |
|
||||
| 2 | State government |
|
||||
| 3 | Local government |
|
||||
| 4 | International government |
|
||||
| 5 | Private |
|
||||
|
||||
**Examples:**
|
||||
```
|
||||
ENU1100010510000 → DC, quarterly, all sizes, private, total all industries
|
||||
ENU0000010510000 → National, quarterly, private, all industries
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## OES/OEWS — Occupational Employment & Wage Statistics (prefix: OE)
|
||||
|
||||
**Series ID:** `OE[U][T][AAAAAAA][IIIIII][OOOOOO][DD]`
|
||||
|
||||
| Pos | Length | Field | Notes |
|
||||
|-----|--------|-------|-------|
|
||||
| 1-2 | 2 | Prefix | `OE` |
|
||||
| 3 | 1 | Seasonal adj | Always `U` |
|
||||
| 4 | 1 | Area type | `N`=national; `S`=state; `M`=metro; `B`=metro div; `W`=nonmetro |
|
||||
| 5-11 | 7 | Area code | BLS area code (not FIPS) |
|
||||
| 12-17 | 6 | Industry code | NAICS or `000000` for cross-industry |
|
||||
| 18-23 | 6 | Occupation code | SOC 6-digit code or `000000` for all |
|
||||
| 24-25 | 2 | Data type | See table below |
|
||||
|
||||
**Data type codes:**
|
||||
| Code | Measure |
|
||||
|------|---------|
|
||||
| 01 | Employment (thousands) |
|
||||
| 02 | Employment % relative standard error |
|
||||
| 03 | Hourly mean wage |
|
||||
| 04 | Annual mean wage |
|
||||
| 05 | Wage % relative standard error |
|
||||
| 06 | Hourly 10th percentile wage |
|
||||
| 07 | Hourly 25th percentile wage |
|
||||
| 08 | Hourly median wage |
|
||||
| 09 | Hourly 75th percentile wage |
|
||||
| 10 | Hourly 90th percentile wage |
|
||||
| 11 | Annual 10th percentile wage |
|
||||
| 12 | Annual 25th percentile wage |
|
||||
| 13 | Annual median wage |
|
||||
| 14 | Annual 75th percentile wage |
|
||||
| 15 | Annual 90th percentile wage |
|
||||
|
||||
**Examples:**
|
||||
```
|
||||
OEUM0000400000000000001 → National, all industries, all occupations, employment
|
||||
OEUM0000400000015113201 → National, all industries, software devs, employment
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## JOLTS — Job Openings & Labor Turnover (prefix: JT)
|
||||
|
||||
**Series ID:** `JT[S/U][IIIIII][E/J][RR][LL]`
|
||||
|
||||
| Pos | Length | Field | Notes |
|
||||
|-----|--------|-------|-------|
|
||||
| 1-2 | 2 | Prefix | `JT` |
|
||||
| 3 | 1 | Seasonal adj | `S` or `U` |
|
||||
| 4-9 | 6 | Industry code | NAICS supersector |
|
||||
| 10 | 1 | Job status | `E`=total; `J`=job openings; `H`=hires; `L`=layoffs/discharges; `Q`=quits; `T`=total separations |
|
||||
| 11-12 | 2 | Rate/level | `R`=rate; `L`=level (thousands) |
|
||||
| 13-14 | 2 | Ownership | `00`=total; `10`=private; `20`=government |
|
||||
|
||||
**Examples:**
|
||||
```
|
||||
JTS000000000000JOL → Total nonfarm, job openings, level
|
||||
JTS000000000000JOR → Total nonfarm, job openings, rate
|
||||
JTS000000000000HIL → Total nonfarm, hires, level
|
||||
JTS000000000000TSL → Total nonfarm, total separations, level
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## CPI — Consumer Price Index (prefix: CU / CW / SU)
|
||||
|
||||
**Prefixes:**
|
||||
- `CU` — CPI-U (All Urban Consumers)
|
||||
- `CW` — CPI-W (Urban Wage Earners)
|
||||
- `SU` — Chained CPI-U (C-CPI-U)
|
||||
|
||||
**Series ID:** `CU[S/U][R/S][AAAA][IIIIII]`
|
||||
|
||||
| Pos | Length | Field | Notes |
|
||||
|-----|--------|-------|-------|
|
||||
| 1-2 | 2 | Prefix | `CU`, `CW`, or `SU` |
|
||||
| 3 | 1 | Seasonal adj | `S`=SA; `U`=unadjusted |
|
||||
| 4 | 1 | Periodicity | `R`=monthly; `S`=semiannual |
|
||||
| 5-8 | 4 | Area code | `0000`=US city average; others=specific metros |
|
||||
| 9-14 | 6 | Item code | Expenditure category code |
|
||||
|
||||
**Common item codes:**
|
||||
| Code | Category |
|
||||
|------|----------|
|
||||
| SA0 | All items |
|
||||
| SA0E | Energy |
|
||||
| SA0L1E | All items less food and energy (core) |
|
||||
| SAF | Food |
|
||||
| SAF1 | Food at home |
|
||||
| SAF11 | Cereals and bakery products |
|
||||
| SAH | Housing |
|
||||
| SAH1 | Shelter |
|
||||
| SAT | Transportation |
|
||||
| SAM | Medical care |
|
||||
|
||||
**Examples:**
|
||||
```
|
||||
CUUR0000SA0 → US city avg, unadjusted, all items
|
||||
CUSR0000SA0L1E → US city avg, SA, core (ex food & energy)
|
||||
CUUR0000SAF → US city avg, unadjusted, food
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## PPI — Producer Price Index (prefix: WP / PC)
|
||||
|
||||
- `WP` — PPI commodities (older series)
|
||||
- `PC` — PPI industry data (NAICS-based, current)
|
||||
|
||||
**Series ID (PC):** `PC[U][IIIIII][IIIIII]`
|
||||
|
||||
| Pos | Length | Field | Notes |
|
||||
|-----|--------|-------|-------|
|
||||
| 1-2 | 2 | Prefix | `PC` |
|
||||
| 3 | 1 | Seasonal adj | `U`=unadjusted (most PPI); `S`=SA |
|
||||
| 4-9 | 6 | Industry code | NAICS 6-digit |
|
||||
| 10-15 | 6 | Product code | Product within the industry |
|
||||
|
||||
---
|
||||
|
||||
## Helpful Discovery Commands
|
||||
|
||||
```bash
|
||||
# All surveys
|
||||
curl -s "https://api.bls.gov/publicAPI/v2/surveys" | python3 -m json.tool
|
||||
|
||||
# Popular series for a survey
|
||||
curl -s "https://api.bls.gov/publicAPI/v2/timeseries/popular?survey=LA"
|
||||
|
||||
# Bulk download decode tables (e.g. LAUS area codes)
|
||||
curl -s "https://download.bls.gov/pub/time.series/la/la.area" | head -50
|
||||
curl -s "https://download.bls.gov/pub/time.series/la/la.measure"
|
||||
curl -s "https://download.bls.gov/pub/time.series/ce/ce.datatype"
|
||||
curl -s "https://download.bls.gov/pub/time.series/ce/ce.supersector"
|
||||
curl -s "https://download.bls.gov/pub/time.series/sm/sm.state"
|
||||
```
|
||||
289
surveys.json
Normal file
289
surveys.json
Normal file
@ -0,0 +1,289 @@
|
||||
{
|
||||
"status": "REQUEST_SUCCEEDED",
|
||||
"responseTime": 12,
|
||||
"message": [],
|
||||
"Results": {
|
||||
"survey": [
|
||||
{
|
||||
"survey_abbreviation": "AP",
|
||||
"survey_name": "Consumer Price Index - Average Price Data"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "BD",
|
||||
"survey_name": "Business Employment Dynamics"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "BG",
|
||||
"survey_name": "Collective Bargaining Agreements-State and Local Government"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "BP",
|
||||
"survey_name": "Collective Bargaining Agreements-Private Sector"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "CA",
|
||||
"survey_name": "Biennial Nonfatal Case and Demographic numbers and rates: selected characteristics"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "CB",
|
||||
"survey_name": "Biennial Nonfatal Case and Demographic numbers and rates: selected characteristics"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "CC",
|
||||
"survey_name": "Employer Costs for Employee Compensation"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "CD",
|
||||
"survey_name": "Nonfatal cases involving days away from work: selected characteristics"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "CE",
|
||||
"survey_name": "Employment, Hours, and Earnings from the Current Employment Statistics survey (National)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "CF",
|
||||
"survey_name": "Census of Fatal Occupational Injuries"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "CH",
|
||||
"survey_name": "Nonfatal cases involving days away from work: selected characteristics (2003 - 2010)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "CI",
|
||||
"survey_name": "Employment Cost Index"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "CM",
|
||||
"survey_name": "Employer Costs for Employee Compensation"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "CS",
|
||||
"survey_name": "Nonfatal cases involving days away from work: selected characteristics (2011 forward)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "CU",
|
||||
"survey_name": "Consumer Price Index - All Urban Consumers"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "CW",
|
||||
"survey_name": "Consumer Price Index - Urban Wage Earners and Clerical Workers"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "CX",
|
||||
"survey_name": "Consumer Expenditure Survey"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "EB",
|
||||
"survey_name": "Employee Benefits Survey"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "EC",
|
||||
"survey_name": "Employment Cost Index"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "EE",
|
||||
"survey_name": "National Employment, Hours, and Earnings"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "EI",
|
||||
"survey_name": "Import/Export Price Indexes"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "EN",
|
||||
"survey_name": "Quarterly Census of Employment and Wages"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "EP",
|
||||
"survey_name": "Employment Projections by Industry"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "EW",
|
||||
"survey_name": "Quarterly Census of Employment and Wages (SIC)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "FA",
|
||||
"survey_name": "Census of Fatal Occupational Injuries (2023 forward)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "FI",
|
||||
"survey_name": "Census of Fatal Occupational Injuries (2003 - 2010)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "FM",
|
||||
"survey_name": "Marital and family labor force statistics from the Current Population Survey"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "FW",
|
||||
"survey_name": "Census of Fatal Occupational Injuries (2011-2022)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "GG",
|
||||
"survey_name": "Green Goods and Services"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "GP",
|
||||
"survey_name": "Geographic Profile"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "HC",
|
||||
"survey_name": "Nonfatal cases involving days away from work: Selected Characteristics (2002)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "HS",
|
||||
"survey_name": "Occupational injuries and illnesses: industry data (pre-1989)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "II",
|
||||
"survey_name": "Occupational injuries and illnesses: industry data"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "IN",
|
||||
"survey_name": "International Labor Comparison"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "IP",
|
||||
"survey_name": "Industry Productivity"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "IS",
|
||||
"survey_name": "Occupational injuries and illnesses industry data"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "JL",
|
||||
"survey_name": "Job Openings and Labor Turnover Survey"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "JT",
|
||||
"survey_name": "Job Openings and Labor Turnover Survey"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "KV",
|
||||
"survey_name": "Veterans Supplement data from the Current Population Survey"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "LA",
|
||||
"survey_name": "Local Area Unemployment Statistics"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "LE",
|
||||
"survey_name": "Weekly and hourly earnings data from the Current Population Survey"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "LF",
|
||||
"survey_name": "Labor Force Statistics from the Current Population Survey (SIC)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "LI",
|
||||
"survey_name": "Consumer Price Index - Department Store Inventory Price Index"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "LN",
|
||||
"survey_name": "Labor Force Statistics from the Current Population Survey"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "LU",
|
||||
"survey_name": "Union affiliation data from the Current Population Survey"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "ML",
|
||||
"survey_name": "Mass Layoff Statistics"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "MP",
|
||||
"survey_name": "Major Sector Total Factor Productivity"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "MU",
|
||||
"survey_name": "Consumer Price Index - All Urban Consumers (Old Series)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "MW",
|
||||
"survey_name": "Consumer Price Index - Urban Wage Earners and Clerical Workers (Old Series)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "NB",
|
||||
"survey_name": "National Compensation Survey-Benefits"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "NC",
|
||||
"survey_name": "National Compensation Survey"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "ND",
|
||||
"survey_name": "Producer Price Index Industry Data"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "NW",
|
||||
"survey_name": "National Compensation Survey"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "OE",
|
||||
"survey_name": "Occupational Employment and Wage Statistics"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "OR",
|
||||
"survey_name": "Occupational Requirements"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "PC",
|
||||
"survey_name": "Producer Price Index Industry Data"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "PD",
|
||||
"survey_name": "Producer Price Index - Discontinued (SIC)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "PF",
|
||||
"survey_name": "Federal Government Productivity Index"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "PI",
|
||||
"survey_name": "Industry Productivity Index"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "PR",
|
||||
"survey_name": "Major Sector Productivity and Costs"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "SA",
|
||||
"survey_name": "State and Area Employment, Hours, and Earnings (SIC)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "SH",
|
||||
"survey_name": "Occupational injuries and illnesses: industry data (1989-2001)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "SI",
|
||||
"survey_name": "Occupational injuries and illnesses: industry data (2002)"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "SM",
|
||||
"survey_name": "State and Area Employment, Hours, and Earnings"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "SU",
|
||||
"survey_name": "Consumer Price Index - Chained Consumer Price Index"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "TU",
|
||||
"survey_name": "American Time Use"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "WD",
|
||||
"survey_name": "Producer Price Index Commodity-Discontinued Series"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "WM",
|
||||
"survey_name": "Wage Modeling"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "WP",
|
||||
"survey_name": "Producer Price Index-Commodities"
|
||||
},
|
||||
{
|
||||
"survey_abbreviation": "WS",
|
||||
"survey_name": "Work Stoppage Data"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user