US industry wage-pressure intelligence (monthly)

Commercial use OKFresh

The full snapshot isn't published yet — check back after the next refresh.Download sample CSV Download sample JSONSample rows only (up to 20) — not the complete dataset.

Monthly average hourly earnings (BLS Current Employment Statistics, seasonally adjusted, dollars per hour) for total private plus 10 supersectors - mining & logging, construction, manufacturing, trade/transportation/utilities, information, financial activities, professional & business services, education & health services, leisure & hospitality, other services - enriched into a join-ready wage-pressure panel: MoM and YoY growth, YoY acceleration in percentage points, percentile-of-own-trailing-36-month-history growth and level scores, a documented 0-100 wage-pressure index (0.6 growth / 0.4 level) with booming/strong/steady/soft/stalled bands, surge/cooling/record-high/preliminary flags, and the total-private benchmark broadcast on every row. Source: U.S. Bureau of Labor Statistics via the keyless Public Data API v2; federal public-domain data, safe for commercial use with attribution. Latest month is preliminary - BLS revises CES. Who joins this: a sales team (staffing, HR-tech, benefits, B2B services) joins (month, industry_code) to account prioritization - industries with accelerating wage growth are hiring and spending on talent; a B2B subscription business joins it to ICP expansion timing; an online shop joins it to labor-cost pass-through expectations by sector.

Source
Rows: 880
Columns: 20
Source cadence: Monthly
Last refreshed: Sep 30, 2026
Theme: economics

Column	Type	Description
month	string	ISO date of the month's first day. (unit: date)
industry_code	string	Machine code for the industry (total_private + 10 supersectors).
industry_name	string	Industry name.
country_code	string	ISO alpha-3 country code (USA).
country_name	string	Country name.
ahe_usd	float	Average hourly earnings, seasonally adjusted, dollars per hour (BLS CES). (unit: USD per hour)
ahe_mom_pct	float	Month-over-month growth of ahe_usd, percent. (unit: percent)
ahe_yoy_pct	float	Year-over-year growth of ahe_usd, percent. (unit: percent)
ahe_yoy_accel_pp	float	Change in ahe_yoy_pct vs the year-ago month, percentage points. (unit: percentage points)
growth_score	float	Percentile rank of ahe_yoy_pct in the industry's own trailing-36-month history, 0-100.
level_score	float	Percentile rank of ahe_usd in the industry's own trailing-36-month history, 0-100.
wage_pressure_index	float	Composite 0-100: 0.6 * growth_score + 0.4 * level_score.
intensity_band	string	booming (>=90) / strong (>=75) / steady (>=50) / soft (>=25) / stalled from the index; insufficient-history when history < 12 months.
surge	boolean	True when wage_pressure_index >= 70 and YoY acceleration is positive.
cooling	boolean	True when wage_pressure_index <= 30.
record_high_12m	boolean	True when ahe_usd is the trailing-12-month maximum.
preliminary	boolean	True for the latest month in the panel (BLS revises CES).
private_ahe_usd	float	Total-private average hourly earnings benchmark, dollars per hour, broadcast on every row. (unit: USD per hour)
private_ahe_yoy_pct	float	Year-over-year growth of the total-private benchmark, percent. (unit: percent)
row_hash	string	Deterministic sha256 row identity (first 16 hex chars).

First 10 sample rows — a preview, not the complete dataset.

month	industry_code	industry_name	country_code	country_name	ahe_usd	ahe_mom_pct	ahe_yoy_pct	ahe_yoy_accel_pp	growth_score	level_score	wage_pressure_index	intensity_band	surge	cooling	record_high_12m	preliminary	private_ahe_usd	private_ahe_yoy_pct	row_hash
2020-01-01	construction	Construction	USA	United States	31.22	—	—	—	—	—	—	insufficient-history	false	false	true	false	28.43	—	cf06ce0fb92778e3
2020-01-01	edu_health	Education and health services	USA	United States	27.83	—	—	—	—	—	—	insufficient-history	false	false	true	false	28.43	—	090dab3e483c12af
2020-01-01	financial_activities	Financial activities	USA	United States	36.69	—	—	—	—	—	—	insufficient-history	false	false	true	false	28.43	—	05f7ae6d94d9000a
2020-01-01	information	Information	USA	United States	42.86	—	—	—	—	—	—	insufficient-history	false	false	true	false	28.43	—	79536acb2b0583fd
2020-01-01	leisure_hospitality	Leisure and hospitality	USA	United States	16.84	—	—	—	—	—	—	insufficient-history	false	false	true	false	28.43	—	3353ebec5336d6d9
2020-01-01	manufacturing	Manufacturing	USA	United States	28.2	—	—	—	—	—	—	insufficient-history	false	false	true	false	28.43	—	a5f80ccf4c34cf11
2020-01-01	mining_logging	Mining and logging	USA	United States	34.41	—	—	—	—	—	—	insufficient-history	false	false	true	false	28.43	—	477b4989dfca95c1
2020-01-01	other_services	Other services	USA	United States	25.6	—	—	—	—	—	—	insufficient-history	false	false	true	false	28.43	—	c81b292d532a92bb
2020-01-01	prof_business	Professional and business services	USA	United States	34.3	—	—	—	—	—	—	insufficient-history	false	false	true	false	28.43	—	08391765cd2a6403
2020-01-01	total_private	Total private	USA	United States	28.43	—	—	—	—	—	—	insufficient-history	false	false	true	false	28.43	—	5fc76067122ee0fc

Profiled Oct 1, 2026 from snapshot 20260930T171616Z-c16d2a3b910b

Measured

Completeness: 93.4%
Rows: 880
Columns: 20
Columns with gaps: 7

Rows per yearmonth · 2020–2026 · 7 years

20202026

Column	Missing	Distinct	Range	Distribution
monthvarchar	0%	73	—	2020-01-0111 2020-02-0111 2020-03-0111
industry_codevarchar	0%	11	—	construction80 edu_health80 financial_activities80
industry_namevarchar	0%	12	—	Construction80 Education and health services80 Financial activities80
country_codevarchar	0%	1	—	USA880
country_namevarchar	0%	1	—	United States880
ahe_usddouble	0%	757	16.84 → 55.7median 34.52	18 outside 1st–99th percentile
ahe_mom_pctdouble	1.3%	211	-3.27 → 7.76median 0.35	18 outside 1st–99th percentile
ahe_yoy_pctdouble	15%	320	-1.33 → 13.83median 4.27	16 outside 1st–99th percentile
ahe_yoy_accel_ppdouble	30%	384	-7.21 → 12.57median -0.31	14 outside 1st–99th percentile
growth_scoredouble	28.8%	215	1.4 → 98.6median 38	7 outside 1st–99th percentile
level_scoredouble	13.8%	58	34.6 → 98.6median 98.6	8 outside 1st–99th percentile
wage_pressure_indexdouble	28.8%	262	35.8 → 98.6median 61.9	10 outside 1st–99th percentile
intensity_bandvarchar	0%	5	—	steady271 insufficient-history253 soft167
surgeboolean	0%	2	—	false693 true187
coolingboolean	0%	1	—	false880
record_high_12mboolean	0%	2	—	true733 false147
preliminaryboolean	0%	2	—	false869 true11
private_ahe_usddouble	0%	74	28.43 → 37.75median 33.47
private_ahe_yoy_pctdouble	15%	73	0.6 → 5.89median 4.17
row_hashvarchar	0%	1,057	—	006952034f74913b1 009053d9d02d6b651 00dd58245d7b97381

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/us_industry_wage_pressure_intel/us_industry_wage_pressure_monthly" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/us_industry_wage_pressure_intel/us_industry_wage_pressure_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/us_industry_wage_pressure_intel/us_industry_wage_pressure_monthly

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20260930T171616Z-c16d2a3b910b and its content hash, so readers get exactly the data you used.

APA

. (2026). US industry wage-pressure intelligence (monthly) [Data set, snapshot 20260930T171616Z-c16d2a3b910b, sha256 c16d2a3b910b]. Datazimuts. Retrieved 2026-10-01, from https://datazimuts.com/en/datasets/us_industry_wage_pressure_intel/us_industry_wage_pressure_monthly?snapshot=20260930T171616Z-c16d2a3b910b

BibTeX

@misc{dz_us_industry_wage_pressure_intel_us_indus_c16d2a3b,
  title        = {{US industry wage-pressure intelligence (monthly)}},
  author       = {{}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/us_industry_wage_pressure_intel/us_industry_wage_pressure_monthly?snapshot=20260930T171616Z-c16d2a3b910b}},
  note         = {Snapshot 20260930T171616Z-c16d2a3b910b, sha256 c16d2a3b910b0ff581c3c9f86d206c3b895730708cc01e18d9cc8f5cd8b96aa7; accessed 2026-10-01}
}

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US industry wage-pressure intelligence (monthly)

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