From 2b9d3e6d682facfb808c0e6fc8edcddec3d985df Mon Sep 17 00:00:00 2001 From: Dave Boyd Date: Tue, 16 Jun 2026 11:03:14 -0400 Subject: [PATCH] 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 --- .gitignore | 4 + README.md | 194 ++++++ api_response_example.json | 1212 +++++++++++++++++++++++++++++++++++++ config.example.py | 13 + dc_md_va_unemployment.csv | 817 +++++++++++++++++++++++++ dc_md_va_unemployment.py | 214 +++++++ qcew_field_schema.md | 179 ++++++ series_id_formats.md | 296 +++++++++ surveys.json | 289 +++++++++ 9 files changed, 3218 insertions(+) create mode 100644 .gitignore create mode 100644 README.md create mode 100644 api_response_example.json create mode 100644 config.example.py create mode 100644 dc_md_va_unemployment.csv create mode 100644 dc_md_va_unemployment.py create mode 100644 qcew_field_schema.md create mode 100644 series_id_formats.md create mode 100644 surveys.json diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..0a054b9 --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +config.py +__pycache__/ +*.pyc +.env diff --git a/README.md b/README.md new file mode 100644 index 0000000..46b139b --- /dev/null +++ b/README.md @@ -0,0 +1,194 @@ +# BLS Data Reference + +Bureau of Labor Statistics — API access, dataset catalog, series ID formats, and field schemas. + +## Quick Start + +1. Register for an API key: https://data.bls.gov/registrationEngine/ +2. Base API endpoint: `https://api.bls.gov/publicAPI/v2/timeseries/data/` +3. Content-Type: `application/json` (POST) + +--- + +## API Versions + +| Feature | v1 (no key) | v2 (registered) | +|---------------------------|-------------|-----------------| +| Daily query limit | 25 | 500 | +| Series per query | 25 | 50 | +| Years of history | 10 | 20 | +| Net/percent changes | No | Yes | +| Series descriptions | No | Yes | +| Calculations | No | Yes | + +--- + +## Registration + +- URL: https://data.bls.gov/registrationEngine/ +- Free, no approval needed — instant key via email +- Key goes in the JSON payload as `"registrationkey": "YOUR_KEY"` + +--- + +## Endpoints + +### GET: Survey List +``` +GET https://api.bls.gov/publicAPI/v2/surveys +GET https://api.bls.gov/publicAPI/v2/surveys/{survey_abbreviation} +``` +Returns all survey codes and names. See `surveys.json` for full list. + +### GET: Popular Series +``` +GET https://api.bls.gov/publicAPI/v2/timeseries/popular +GET https://api.bls.gov/publicAPI/v2/timeseries/popular?survey={abbreviation} +``` +Returns the 25 most-requested series IDs for a survey. + +### POST: Time Series Data +``` +POST https://api.bls.gov/publicAPI/v2/timeseries/data/ +Content-Type: application/json +``` + +**Minimal request (unregistered):** +```json +{ + "seriesid": ["LAUST110000000000003", "CES0000000001"], + "startyear": "2023", + "endyear": "2025" +} +``` + +**Full request (registered, v2 features):** +```json +{ + "seriesid": ["LAUST110000000000003", "CES0000000001"], + "startyear": "2020", + "endyear": "2025", + "registrationkey": "YOUR_KEY", + "catalog": true, + "calculations": true, + "annualaverage": true, + "aspects": true +} +``` + +**Response schema:** +```json +{ + "status": "REQUEST_SUCCEEDED", + "responseTime": 114, + "message": [], + "Results": { + "series": [ + { + "seriesID": "LAUST110000000000003", + "catalog": { + "series_title": "...", + "survey_name": "...", + "measure_data_type": "..." + }, + "data": [ + { + "year": "2025", + "period": "M12", + "periodName": "December", + "value": "6.4", + "footnotes": [ + { "code": "R", "text": "Data were subject to revision on April 8, 2026." } + ], + "calculations": { + "net_changes": { "1": "0.1", "3": "-0.2", "6": "0.5", "12": "-0.3" }, + "pct_changes": { "1": "1.6", "3": "-3.0", "6": "8.3", "12": "-4.5" } + } + } + ] + } + ] + } +} +``` + +**Period codes:** +- Monthly: `M01`–`M12`, `M13` (annual average) +- Quarterly: `Q01`–`Q04`, `Q05` (annual average) +- Annual: `A01` + +**Status codes:** `REQUEST_SUCCEEDED`, `REQUEST_FAILED`, `REQUEST_NOT_PROCESSED` + +**Error footnote codes:** +- `R` — Revised +- `P` — Preliminary +- `X` — Data unavailable (e.g., government shutdown gap) +- `N` — Not available + +--- + +## Python Example + +```python +import requests + +API_KEY = "YOUR_KEY" +BASE_URL = "https://api.bls.gov/publicAPI/v2/timeseries/data/" + +def get_series(series_ids, start_year, end_year): + payload = { + "seriesid": series_ids, + "startyear": str(start_year), + "endyear": str(end_year), + "registrationkey": API_KEY, + "catalog": True, + "calculations": True, + "annualaverage": True, + } + r = requests.post(BASE_URL, json=payload) + r.raise_for_status() + data = r.json() + if data["status"] != "REQUEST_SUCCEEDED": + raise ValueError(f"BLS API error: {data['message']}") + return data["Results"]["series"] + +# DC unemployment rate (LAUS) +series = get_series(["LAUST110000000000003"], 2020, 2025) +for obs in series[0]["data"]: + print(obs["year"], obs["periodName"], obs["value"]) +``` + +--- + +## Bulk Download (flat files) + +Base URL: `https://download.bls.gov/pub/time.series/` + +Each survey folder contains: +- `{prefix}.series` — master list of all series IDs with metadata +- `{prefix}.data.{N}.{name}` — actual observations, split by category +- `{prefix}.{dimension}` — lookup/decode tables (area, industry, measure, etc.) + +Key folder prefixes: +``` +la/ — LAUS (local area unemployment) +ce/ — CES national employment +sm/ — State & Metro employment (CES state) +en/ — QCEW +oe/ — OES (occupational employment) +jt/ — JOLTS +cu/ — CPI-U +wp/ — PPI commodities +``` + +Files are tab-delimited. Useful for bulk PostgreSQL ingest. + +--- + +## Files in this folder + +- `README.md` — this file +- `surveys.json` — complete survey list from the API +- `series_id_formats.md` — series ID decode tables for each key dataset +- `qcew_field_schema.md` — QCEW quarterly and annual CSV field layouts +- `api_response_example.json` — real API response sample diff --git a/api_response_example.json b/api_response_example.json new file mode 100644 index 0000000..82d5329 --- /dev/null +++ b/api_response_example.json @@ -0,0 +1,1212 @@ +{ + "status": "REQUEST_SUCCEEDED", + "responseTime": 103, + "message": [], + "Results": { + "series": [ + { + "seriesID": "LAUST110000000000003", + "data": [ + { + "year": "2025", + "period": "M12", + "periodName": "December", + "value": "6.4", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2025", + "period": "M11", + "periodName": "November", + "value": "6.8", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2025", + "period": "M10", + "periodName": "October", + "value": "-", + "footnotes": [ + { + "code": "X", + "text": "Data unavailable due to the 2025 lapse in appropriations." + } + ] + }, + { + "year": "2025", + "period": "M09", + "periodName": "September", + "value": "6.8", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2025", + "period": "M08", + "periodName": "August", + "value": "7.1", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2025", + "period": "M07", + "periodName": "July", + "value": "6.8", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2025", + "period": "M06", + "periodName": "June", + "value": "6.4", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2025", + "period": "M05", + "periodName": "May", + "value": "5.8", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2025", + "period": "M04", + "periodName": "April", + "value": "5.5", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2025", + "period": "M03", + "periodName": "March", + "value": "6.1", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2025", + "period": "M02", + "periodName": "February", + "value": "6.0", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2025", + "period": "M01", + "periodName": "January", + "value": "5.8", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M12", + "periodName": "December", + "value": "5.1", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M11", + "periodName": "November", + "value": "5.2", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M10", + "periodName": "October", + "value": "5.2", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M09", + "periodName": "September", + "value": "5.3", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M08", + "periodName": "August", + "value": "6.1", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } 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"footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2025", + "period": "M02", + "periodName": "February", + "value": "393994", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2025", + "period": "M01", + "periodName": "January", + "value": "390797", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M12", + "periodName": "December", + "value": "391619", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M11", + "periodName": "November", + "value": "391918", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M10", + "periodName": "October", + "value": "390915", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M09", + "periodName": "September", + "value": "389157", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M08", + "periodName": "August", + "value": "386519", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M07", + "periodName": "July", + "value": "395848", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M06", + "periodName": "June", + "value": "391286", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M05", + "periodName": "May", + "value": "388487", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M04", + "periodName": "April", + "value": "393443", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M03", + "periodName": "March", + "value": "396914", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M02", + "periodName": "February", + "value": "391919", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2024", + "period": "M01", + "periodName": "January", + "value": "389873", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2023", + "period": "M12", + "periodName": "December", + "value": "390621", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2023", + "period": "M11", + "periodName": "November", + "value": "391725", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2023", + "period": "M10", + "periodName": "October", + "value": "388857", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2023", + "period": "M09", + "periodName": "September", + "value": "387265", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2023", + "period": "M08", + "periodName": "August", + "value": "383911", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2023", + "period": "M07", + "periodName": "July", + "value": "390390", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2023", + "period": "M06", + "periodName": "June", + "value": "385195", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2023", + "period": "M05", + "periodName": "May", + "value": "383283", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2023", + "period": "M04", + "periodName": "April", + "value": "381772", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2023", + "period": "M03", + "periodName": "March", + "value": "382203", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2023", + "period": "M02", + "periodName": "February", + "value": "381305", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + }, + { + "year": "2023", + "period": "M01", + "periodName": "January", + "value": "379092", + "footnotes": [ + { + "code": "R", + "text": "Data were subject to revision on April 8, 2026." + } + ] + } + ] + } + ] + } +} diff --git a/config.example.py b/config.example.py new file mode 100644 index 0000000..a2d014a --- /dev/null +++ b/config.example.py @@ -0,0 +1,13 @@ +# BLS API Configuration +# ---------------------- +# 1. Register for a free API key at: https://data.bls.gov/registrationEngine/ +# Required fields: first name, last name, organization, email address. +# Your key will be emailed within a few minutes. +# +# 2. Copy this file to config.py: +# cp config.example.py config.py +# +# 3. Paste your key below and save. + +BLS_API_KEY = "YOUR_KEY_HERE" +BLS_API_BASE = "https://api.bls.gov/publicAPI/v2" diff --git a/dc_md_va_unemployment.csv b/dc_md_va_unemployment.csv new file mode 100644 index 0000000..e4c9971 --- /dev/null +++ b/dc_md_va_unemployment.csv @@ -0,0 +1,817 @@ +geography,geo_code,measure,series_id,year,period,period_name,value,mom_chg,qoq_chg,yoy_chg +District of Columbia,DC,rate,LAUST110000000000003,2026,M04,April,5.5,-0.2,-0.7,0.0 +District of Columbia,DC,rate,LAUST110000000000003,2026,M03,March,5.7,-0.4,-0.7,-0.4 +District of Columbia,DC,rate,LAUST110000000000003,2026,M02,February,6.1,-0.1,-0.7,0.1 +District of Columbia,DC,rate,LAUST110000000000003,2026,M01,January,6.2,-0.2,—,0.4 +District of Columbia,DC,rate,LAUST110000000000003,2025,M12,December,6.4,-0.4,-0.4,1.3 +District of Columbia,DC,rate,LAUST110000000000003,2025,M11,November,6.8,—,-0.3,1.6 +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 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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 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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 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(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 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(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 diff --git a/dc_md_va_unemployment.py b/dc_md_va_unemployment.py new file mode 100644 index 0000000..559fb6c --- /dev/null +++ b/dc_md_va_unemployment.py @@ -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() diff --git a/qcew_field_schema.md b/qcew_field_schema.md new file mode 100644 index 0000000..8e2fe43 --- /dev/null +++ b/qcew_field_schema.md @@ -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 +``` diff --git a/series_id_formats.md b/series_id_formats.md new file mode 100644 index 0000000..eb31ea4 --- /dev/null +++ b/series_id_formats.md @@ -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" +``` diff --git a/surveys.json b/surveys.json new file mode 100644 index 0000000..68a3dba --- /dev/null +++ b/surveys.json @@ -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" + } + ] + } +}