US metro unemployment-rate intelligence (monthly)
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Monthly US metro unemployment-rate intelligence from the BLS Local Area Unemployment Statistics metropolitan-area program (keyless BLS Public Data API v2): not-seasonally-adjusted unemployment rates for the 15 US metros shared with metro_degree_days / metro_thermal_demand_intel / metro_daylight_intel, with YoY percentage-point change (the seasonality-robust momentum lens for NSA data), a 12-month z-score, a documented 0-100 labor-health score with per-month ranks and health tiers, rising / falling / rapid-rise / 12-month-record-low/high flags, and month-level context: the national NSA headline rate, each metro's gap to it, the national spread, and YoY rising/falling breadth. Joins 1:1 with the sibling metro panels on (month, metro); the 5 Canadian metros are covered by cma_labour_divergence_monthly. Methodology window 2017-01 onward; October 2025 is an upstream shutdown gap kept as explicit nulls. BLS material is public domain (commercial reuse allowed); source: U.S. Bureau of Labor Statistics.
- Rows
- 1,725
- Columns
- 28
- Source cadence
- Monthly
- Last refreshed
- Sep 29, 2026
- Theme
- economy
| Column | Type | Description |
|---|---|---|
| month | string | Reference month: first day of the month, ISO date. The panel covers the consistent methodology window 2017-01 onward (the fetch starts 2017-01, limited by the v2 10-year query window, so YoY features start 2018-01) and ends at the latest month published for every metro (metros lag the national headline by ~1 month). October 2025 is an upstream shutdown gap (nulls). (unit: ISO date) |
| country_code | string | ISO alpha-3 country code (USA for every row). (unit: ISO 3166-1 alpha-3) |
| metro | string | Metro short name (e.g. New York, Los Angeles). Primary join key with month: same 15 US metros and spelling as the sibling metro_degree_days / metro_thermal_demand / metro_daylight panels. |
| geo_code | string | Stable geography code: snake_case metro slug (new_york, los_angeles, ...). Secondary join key. |
| geo_name | string | Official BLS catalog Area field for the metro (e.g. Los Angeles-Long Beach-Anaheim, CA Metropolitan Statistical Area). |
| cbsa_code | string | 5-digit Core-Based Statistical Area code (e.g. 31080 = Los Angeles-Long Beach-Anaheim). (unit: CBSA) |
| state_fips | string | Census FIPS state code of the metro's first-listed state (e.g. 06 = California). |
| ur_nsa_pct | float | Published not-seasonally-adjusted unemployment rate, kept verbatim as published. Metro LAUS rates are NSA, so MoM changes embed seasonal movement. (unit: percent) |
| ur_mom_pp | float | Month-over-month change of the NSA unemployment rate. Embeds seasonal movement — prefer ur_yoy_pp. (unit: percentage points) |
| ur_yoy_pp | float | Year-over-year change of the NSA unemployment rate: the primary, seasonality-robust momentum lens for metro data (the comparison BLS itself publishes). (unit: percentage points) |
| ur_6m_pp | float | 6-month change of the NSA unemployment rate. (unit: percentage points) |
| ur_z_12m | float | Z-score of the rate versus its own trailing-12-month mean/std (inclusive of the current month, minimum 8 observations so the October 2025 gap does not erase nearby z-scores). Positive = unusually hot versus its own recent year. (unit: z-score) |
| labor_health_score | float | Documented 0-100 composite: 100 * (0.50 * min-max(winsorized -ur_nsa_pct, -12..-1) + 0.50 * min-max(winsorized -ur_yoy_pp, -2..+2)), min-maxed within each month across the 15 metros. Higher = healthiest labor market versus peers this month. (unit: 0-100 score) |
| health_rank | integer | Per-month rank of labor_health_score across the 15 metros (1 = healthiest). (unit: rank) |
| health_tier | string | Quartile bucket of labor_health_score within the month: h1 (healthiest) .. h4 (weakest). (unit: tier) |
| unemployment_rank | integer | Per-month rank of the raw ur_nsa_pct across the 15 metros (1 = lowest rate). (unit: rank) |
| rising_flag | integer | 1 when ur_yoy_pp is positive (labor market looser than a year ago). (unit: binary) |
| falling_flag | integer | 1 when ur_yoy_pp is negative (labor market tighter than a year ago). (unit: binary) |
| rapid_rise_flag | integer | 1 when ur_yoy_pp >= 1.0 pp (sharp yearly deterioration). (unit: binary) |
| record_low_12m_flag | integer | 1 when the rate equals the trailing-12-month minimum (inclusive, minimum 12 observations). (unit: binary) |
| record_high_12m_flag | integer | 1 when the rate equals the trailing-12-month maximum (inclusive, minimum 12 observations). (unit: binary) |
| us_unemployment_rate | float | Month-level context: national not-seasonally-adjusted unemployment rate (series LNU04000000, CPS household survey headline, NSA basis matching the metros). (unit: percent) |
| metro_minus_us_pp | float | Month-level context: metro rate minus national rate, in percentage points. Positive = the metro runs hotter than the nation. (unit: pp) |
| national_spread_pp | float | Month-level context: max minus min metro ur_nsa_pct that month — the national divergence gauge. (unit: pp) |
| n_rising_yoy | integer | Month-level context: how many of the 15 metros are looser than a year ago (breadth gauge). (unit: count) |
| n_falling_yoy | integer | Month-level context: how many of the 15 metros are tighter than a year ago (breadth gauge). (unit: count) |
| bls_series_id | string | BLS series id behind the row (LAUMT{ff}{cbsa}00000003). |
| row_hash | string | Deterministic 16-hex row hash of geo_code + month (idempotency). |
First 10 sample rows — a preview, not the complete dataset.
| month | country_code | metro | geo_code | geo_name | cbsa_code | state_fips | ur_nsa_pct | ur_mom_pp | ur_yoy_pp | ur_6m_pp | ur_z_12m | labor_health_score | health_rank | health_tier | unemployment_rank | rising_flag | falling_flag | rapid_rise_flag | record_low_12m_flag | record_high_12m_flag | us_unemployment_rate | metro_minus_us_pp | national_spread_pp | n_rising_yoy | n_falling_yoy | bls_series_id | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2017-01-01 | USA | New York | new_york | New York-Newark-Jersey City, NY-NJ Metropolitan Statistical Area | 35620 | 36 | 4.8 | — | — | — | — | 42.742 | 8 | h2 | 8 | 0 | 0 | 0 | 0 | 0 | 5.1 | -0.3 | 3.1 | 0 | 0 | LAUMT363562000000003 | dbd1928296f91a63 |
| 2017-01-01 | USA | Los Angeles | los_angeles | Los Angeles-Long Beach-Anaheim, CA Metropolitan Statistical Area | 31080 | 06 | 5 | — | — | — | — | 39.516 | 10 | h3 | 10 | 0 | 0 | 0 | 0 | 0 | 5.1 | -0.1 | 3.1 | 0 | 0 | LAUMT063108000000003 | e5b6f94bed70c636 |
| 2017-01-01 | USA | Chicago | chicago | Chicago-Naperville-Elgin, IL-IN Metropolitan Statistical Area | 16980 | 17 | 5.7 | — | — | — | — | 28.226 | 13 | h4 | 13 | 0 | 0 | 0 | 0 | 0 | 5.1 | 0.6 | 3.1 | 0 | 0 | LAUMT171698000000003 | 540d79de4d894a50 |
| 2017-01-01 | USA | Houston | houston | Houston-Pasadena-The Woodlands, TX Metropolitan Statistical Area | 26420 | 48 | 5.9 | — | — | — | — | 25 | 15 | h4 | 15 | 0 | 0 | 0 | 0 | 0 | 5.1 | 0.8 | 3.1 | 0 | 0 | LAUMT482642000000003 | 5194a1b3bd60cd33 |
| 2017-01-01 | USA | Phoenix | phoenix | Phoenix-Mesa-Chandler, AZ Metropolitan Statistical Area | 38060 | 04 | 4.7 | — | — | — | — | 44.355 | 7 | h2 | 7 | 0 | 0 | 0 | 0 | 0 | 5.1 | -0.4 | 3.1 | 0 | 0 | LAUMT043806000000003 | e922251cdead581e |
| 2017-01-01 | USA | Atlanta | atlanta | Atlanta-Sandy Springs-Roswell, GA Metropolitan Statistical Area | 12060 | 13 | 5.2 | — | — | — | — | 36.29 | 12 | h3 | 12 | 0 | 0 | 0 | 0 | 0 | 5.1 | 0.1 | 3.1 | 0 | 0 | LAUMT131206000000003 | 7b3733523288d3df |
| 2017-01-01 | USA | Boston | boston | Boston-Cambridge-Newton, MA-NH Metropolitan Statistical Area | 14460 | 25 | 3.8 | — | — | — | — | 58.871 | 2 | h1 | 2 | 0 | 0 | 0 | 0 | 0 | 5.1 | -1.3 | 3.1 | 0 | 0 | LAUMT251446000000003 | 05f8123c3abb4bd0 |
| 2017-01-01 | USA | Seattle | seattle | Seattle-Tacoma-Bellevue, WA Metropolitan Statistical Area | 42660 | 53 | 4.2 | — | — | — | — | 52.419 | 5 | h2 | 5 | 0 | 0 | 0 | 0 | 0 | 5.1 | -0.9 | 3.1 | 0 | 0 | LAUMT534266000000003 | fc37284e38ec3587 |
| 2017-01-01 | USA | Miami | miami | Miami-Fort Lauderdale-West Palm Beach, FL Metropolitan Statistical Area | 33100 | 12 | 4.8 | — | — | — | — | 42.742 | 8 | h3 | 8 | 0 | 0 | 0 | 0 | 0 | 5.1 | -0.3 | 3.1 | 0 | 0 | LAUMT123310000000003 | 03a06b362256ff00 |
| 2017-01-01 | USA | San Francisco | san_francisco | San Francisco-Oakland-Fremont, CA Metropolitan Statistical Area | 41860 | 06 | 4 | — | — | — | — | 55.645 | 4 | h1 | 4 | 0 | 0 | 0 | 0 | 0 | 5.1 | -1.1 | 3.1 | 0 | 0 | LAUMT064186000000003 | 9eb29a158b5f1c0d |
- Current
20260929T131451Z-d9a6b6f11bff · sha256 d9a6b6f11bff…
1,725 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
cURL
curl "https://datazimuts.com/v1/datasets/metro_unemployment_intel/us_metro_unemployment_rate_monthly" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/metro_unemployment_intel/us_metro_unemployment_rate_monthly").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)API endpoint: https://datazimuts.com/v1/datasets/metro_unemployment_intel/us_metro_unemployment_rate_monthly
Tip: fetch /llms.txt for the full machine-readable catalog.
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Cite this snapshot
Pinned to snapshot 20260929T131451Z-d9a6b6f11bff and its content hash, so readers get exactly the data you used.
APA
U.S. Bureau of Labor Statistics. (2026). US metro unemployment-rate intelligence (monthly) [Data set, snapshot 20260929T131451Z-d9a6b6f11bff, sha256 d9a6b6f11bff]. Datazimuts. Retrieved 2026-09-30, from https://datazimuts.com/en/datasets/metro_unemployment_intel/us_metro_unemployment_rate_monthly?snapshot=20260929T131451Z-d9a6b6f11bff
BibTeX
@misc{dz_metro_unemployment_intel_us_metro_unempl_d9a6b6f1,
title = {{US metro unemployment-rate intelligence (monthly)}},
author = {{U.S. Bureau of Labor Statistics}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/en/datasets/metro_unemployment_intel/us_metro_unemployment_rate_monthly?snapshot=20260929T131451Z-d9a6b6f11bff}},
note = {Snapshot 20260929T131451Z-d9a6b6f11bff, sha256 d9a6b6f11bff9eadfa69d4f162180eb090d0aeab2806df886e1299608796df98; accessed 2026-09-30}
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