US industry labor tightness: JOLTS by industry (monthly)
Monthly US industry labor-market tightness from the BLS Job Openings and Labor Turnover Survey (keyless BLS Public Data API v2): seasonally adjusted job openings, quits, and layoffs & discharges rates for 12 industries (Construction, Manufacturing, Wholesale, Retail, Information, Financial activities, Educational services, Health care, Arts/entertainment, Accommodation/food, Other services, Government), 2017-01 onward. Includes MoM/YoY momentum, trailing-12-month z-scores, a documented 0-100 tightness score with per-industry quartile tiers, and worker-confidence / layoff-stress / record-openings flags. BLS material is public domain (commercial reuse allowed); source: U.S. Bureau of Labor Statistics.
- Rows
- 1,392
- Columns
- 24
- Source cadence
- Monthly
- Last refreshed
- Oct 1, 2026
- Theme
- economy
| Column | Type | Description |
|---|---|---|
| month | date | Reference month (first day). |
| year_month | string | Reference month as YYYY-MM. |
| industry | string | BLS JOLTS published industry label. |
| industry_code | string | 6-char JOLTS industry_code. |
| country | string | United States. |
| country_code | string | ISO alpha-3 USA. |
| openings_rate_pct | float | Job openings rate, SA, pct (BLS JOLTS). |
| quits_rate_pct | float | Quits rate, SA, pct (BLS JOLTS). |
| layoffs_rate_pct | float | Layoffs & discharges rate, SA, pct (BLS JOLTS). |
| openings_rate_mom_pp | float | MoM change of openings rate, pp. |
| openings_rate_yoy_pp | float | YoY change of openings rate, pp. |
| quits_rate_mom_pp | float | MoM change of quits rate, pp. |
| quits_rate_yoy_pp | float | YoY change of quits rate, pp. |
| layoffs_rate_mom_pp | float | MoM change of layoffs rate, pp. |
| layoffs_rate_yoy_pp | float | YoY change of layoffs rate, pp. |
| z12_openings_rate | float | Trailing-12m z-score, openings rate. |
| z12_quits_rate | float | Trailing-12m z-score, quits rate. |
| z12_layoffs_rate | float | Trailing-12m z-score, layoffs rate. |
| tightness_score | float | 0-100 industry tightness composite. |
| tightness_tier | string | Per-industry quartile: t1 (loosest)..t4 (tightest). |
| quits_confidence_flag | boolean | 1 when quits rate >= trailing-12m 75th pct. |
| layoff_stress_flag | boolean | 1 when layoffs rate >= trailing-12m 90th pct. |
| openings_record_12m_flag | boolean | 1 when openings rate == trailing-12m max. |
| row_hash | string | Deterministic 16-hex row identity hash. |
First 10 sample rows — a preview, not the complete dataset.
| month | year_month | industry | industry_code | country | country_code | openings_rate_pct | quits_rate_pct | layoffs_rate_pct | openings_rate_mom_pp | openings_rate_yoy_pp | quits_rate_mom_pp | quits_rate_yoy_pp | layoffs_rate_mom_pp | layoffs_rate_yoy_pp | z12_openings_rate | z12_quits_rate | z12_layoffs_rate | tightness_score | tightness_tier | quits_confidence_flag | layoff_stress_flag | openings_record_12m_flag | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2017-01-01 | 2017-01 | Construction | 230000 | United States | USA | 2.3 | 2.2 | 2.7 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | b5fd1d85e4886688 |
| 2017-01-01 | 2017-01 | Manufacturing | 300000 | United States | USA | 2.9 | 1.4 | 0.9 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 5f2a90e54413d9dc |
| 2017-01-01 | 2017-01 | Wholesale trade | 420000 | United States | USA | 2.9 | 1.6 | 0.7 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 2c31af7bbf6f4e61 |
| 2017-01-01 | 2017-01 | Retail trade | 440000 | United States | USA | 3.8 | 3 | 1 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | aa1b7593932f808e |
| 2017-01-01 | 2017-01 | Information | 510000 | United States | USA | 3 | 1.4 | 0.9 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | d2013334d6991ab5 |
| 2017-01-01 | 2017-01 | Financial activities | 520000 | United States | USA | 3.9 | 1.4 | 0.4 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 04a037b815e4f06e |
| 2017-01-01 | 2017-01 | Educational services | 610000 | United States | USA | 2 | 1.3 | 0.8 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | f95e0bd3eecb7efa |
| 2017-01-01 | 2017-01 | Health care and social assistance | 620000 | United States | USA | 5.3 | 2 | 0.5 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | d6f0c79348040841 |
| 2017-01-01 | 2017-01 | Arts, entertainment, and recreation | 710000 | United States | USA | 3.4 | 3.1 | 3.1 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 258ae72c16b91943 |
| 2017-01-01 | 2017-01 | Accommodation and food services | 720000 | United States | USA | 4.5 | 4.3 | 2.2 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 24708d0b43682c1c |
Profiled Oct 1, 2026 from snapshot 20261001T003331Z-6e7842ff50ef
Measured- Completeness
- 95.4%
- Rows
- 1,392
- Columns
- 24
- Columns with gaps
- 14
| Column | Missing | Distinct | Range | Distribution |
|---|---|---|---|---|
| monthdate | 0% | 107 | Jan 1, 2017 → Aug 1, 2026 | — |
| year_monthvarchar | 0% | 125 | — |
|
| industryvarchar | 0% | 12 | — |
|
| industry_codevarchar | 0% | 12 | — |
|
| countryvarchar | 0% | 1 | — |
|
| country_codevarchar | 0% | 1 | — |
|
| openings_rate_pctdouble | 0% | 68 | 1.6 → 11.1median 4.3 | 26 outside 1st–99th percentile |
| quits_rate_pctdouble | 0% | 55 | 0.1 → 6.3median 1.8 | 28 outside 1st–99th percentile |
| layoffs_rate_pctdouble | 0% | 72 | 0.2 → 33.1median 0.9 | 19 outside 1st–99th percentile |
| openings_rate_mom_ppdouble | 0.86% | 143 | -4.2 → 3.9median 0 | 27 outside 1st–99th percentile |
| openings_rate_yoy_ppdouble | 10.3% | 214 | -5.2 → 5.4median -0.1 | 25 outside 1st–99th percentile |
| quits_rate_mom_ppdouble | 0.86% | 91 | -2.8 → 1.5median 0 | 30 outside 1st–99th percentile |
| quits_rate_yoy_ppdouble | 10.3% | 131 | -3.2 → 3.5median 0 | 26 outside 1st–99th percentile |
| layoffs_rate_mom_ppdouble | 0.86% | 102 | -25.9 → 31.4median 0 | 28 outside 1st–99th percentile |
| layoffs_rate_yoy_ppdouble | 10.3% | 143 | -31.8 → 31.5median 0 | 25 outside 1st–99th percentile |
| z12_openings_ratedouble | 9.5% | 1,241 | -2.98 → 2.9median -0.1401 | 26 outside 1st–99th percentile |
| z12_quits_ratedouble | 9.5% | 1,579 | -3.18 → 2.94median -0.0368 | 26 outside 1st–99th percentile |
| z12_layoffs_ratedouble | 9.5% | 1,051 | -2.37 → 3.17median -0.1618 | 26 outside 1st–99th percentile |
| tightness_scoredouble | 9.5% | 1,267 | 3.42 → 84.13median 49.17 | 26 outside 1st–99th percentile |
| tightness_tiervarchar | 9.5% | 4 | — |
|
| quits_confidence_flagboolean | 9.5% | 2 | — |
|
| layoff_stress_flagboolean | 9.5% | 2 | — |
|
| openings_record_12m_flagboolean | 9.5% | 2 | — |
|
| row_hashvarchar | 0% | 1,561 | — |
|
- Current
20261001T003331Z-6e7842ff50ef · sha256 6e7842ff50ef…
1,392 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/bls_jolts_industry_intel/us_industry_labor_tightness_monthly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/bls_jolts_industry_intel/us_industry_labor_tightness_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/bls_jolts_industry_intel/us_industry_labor_tightness_monthly
Tip: fetch /llms.txt for the full machine-readable catalog.
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Cite this snapshot
Pinned to snapshot 20261001T003331Z-6e7842ff50ef and its content hash, so readers get exactly the data you used.
U.S. Bureau of Labor Statistics. (2026). US industry labor tightness: JOLTS by industry (monthly) [Data set, snapshot 20261001T003331Z-6e7842ff50ef, sha256 6e7842ff50ef]. Datazimuts. Retrieved 2026-10-01, from https://datazimuts.com/en/datasets/bls_jolts_industry_intel/us_industry_labor_tightness_monthly?snapshot=20261001T003331Z-6e7842ff50ef
@misc{dz_bls_jolts_industry_intel_us_industry_lab_6e7842ff,
title = {{US industry labor tightness: JOLTS by industry (monthly)}},
author = {{U.S. Bureau of Labor Statistics}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/en/datasets/bls_jolts_industry_intel/us_industry_labor_tightness_monthly?snapshot=20261001T003331Z-6e7842ff50ef}},
note = {Snapshot 20261001T003331Z-6e7842ff50ef, sha256 6e7842ff50ef93b321818f8697b7367394cc648300b490f01ab167d6faeda71c; accessed 2026-10-01}
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