US industrial-production industry breadth intelligence (monthly)

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Monthly US industrial-production breadth intelligence from the Federal Reserve G.17 release (Table 4, seasonally adjusted, 2017=100): 19 NAICS manufacturing industries plus mining and electric & gas utilities, with month-on-month, 3-month-annualized and year-on-year momentum, capacity utilization and its gap versus the trailing 10-year mean (manufacturing only), expansion flags, and a documented 0-100 industrial-health score ranked per month (h1 leaders .. h4 laggards). Durable- and nondurable-manufacturing aggregates included for context. Full history from 1972 (mining 1919, utilities 1939). US federal public domain (commercial reuse allowed); source: Board of Governors of the Federal Reserve System.

Source: Federal Reserve G.17 US industrial-production industry breadth intelligence
Rows: 16,120
Columns: 21
Source cadence: Monthly
Last refreshed: Sep 27, 2026
Theme: economy

Column	Type	Description
industry_code	string	Stable industry identifier: NAICS code for leaf industries (e.g. 334, 3361-3, 21), 'durable-mfg'/'nondurable-mfg' for aggregates.
industry_name	string	English industry name (hand-resolved from the G.17 mnemonics).
naics	string	NAICS code or range covered (e.g. 3361-3363).
sector	string	durable manufacturing / nondurable manufacturing / mining / utilities.
is_aggregate	boolean	True for the durable- and nondurable-manufacturing aggregates, which are official Fed series (IP.GMFD.S / IP.GMFN.S), not computed; breadth diffusion and health scoring use leaf industries only.
country_code	string	ISO alpha-3 country code (USA for all rows).
month	string	Reference month, first day of month (ISO date).
as_of	string	Last complete month in the snapshot (pinned for idempotent re-runs).
ip_index	float	Industrial production index, seasonally adjusted, 2017=100. (unit: index)
ip_mom_pct	float	Month-on-month percent change of the IP index. (unit: percent)
ip_3m_ann_pct	float	3-month percent change of the IP index, annualized. (unit: percent)
ip_yoy_pct	float	Year-on-year percent change of the IP index. (unit: percent)
cu_pct	float	Capacity utilization rate, seasonally adjusted. Null for mining and utilities — the G.17 publishes no utilization rates there. (unit: percent)
cu_gap_lra_pp	float	Capacity utilization minus its trailing 120-month mean (min 60 observations); positive = tighter than usual. Null for mining/utilities. (unit: percentage points)
expanding_mom	boolean	True when ip_mom_pct > 0.
expanding_3m	boolean	True when ip_3m_ann_pct > 0.
health_score	float	0-100 industrial-health score: 100(0.35min-max winsorized ip_mom_pct + 0.35min-max winsorized ip_3m_ann_pct + 0.20min-max winsorized ip_yoy_pct + 0.10*min-max winsorized cu_gap_lra_pp), winsorized at +/-5, +/-15, +/-20, +/-10 and min-max normalized across the 21 leaf industries within the month (mining/utilities contribute 0.5 for the utilization leg); 50 when degenerate. Null for the two aggregate rows. (unit: score)
health_rank	string	Per-month health rank across the 21 leaf industries (1 = healthiest); deterministic tie-break on industry_code. Null for aggregates.
health_tier	string	h1 leaders (>=70) / h2 firm (>=45) / h3 soft (>=20) / h4 laggards (<20); null for aggregates.
row_hash	string	SHA-256 (truncated) of the row's content fields.
source_retrieved	string	Source data vintage: latest reference month present in the retrieved packages (content-pinned so identical re-fetches hash identically).

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

industry_code	industry_name	naics	sector	is_aggregate	country_code	month	as_of	ip_index	ip_mom_pct	ip_3m_ann_pct	ip_yoy_pct	cu_pct	cu_gap_lra_pp	expanding_mom	expanding_3m	health_score	health_rank	health_tier	row_hash	source_retrieved
21	Mining	21	mining	false	USA	1919-01-01	2026-08-01	23.571	—	—	—	—	—	false	false	50	1	h2	16abba611b6d0065	2026-08-01
21	Mining	21	mining	false	USA	1919-02-01	2026-08-01	20.822	-11.662	—	—	—	—	false	false	50	1	h2	32a90873cd0c8af8	2026-08-01
21	Mining	21	mining	false	USA	1919-03-01	2026-08-01	19.86	-4.621	—	—	—	—	false	false	50	1	h2	cba85d4eb7342777	2026-08-01
21	Mining	21	mining	false	USA	1919-04-01	2026-08-01	21.234	6.92	-34.135	—	—	—	true	false	50	1	h2	c7f42e7dd8ab70c8	2026-08-01
21	Mining	21	mining	false	USA	1919-05-01	2026-08-01	22.128	4.207	27.54	—	—	—	true	true	50	1	h2	118822ae6ff77a4c	2026-08-01
21	Mining	21	mining	false	USA	1919-06-01	2026-08-01	22.746	2.795	72.076	—	—	—	true	true	50	1	h2	194c4e12f46f449e	2026-08-01
21	Mining	21	mining	false	USA	1919-07-01	2026-08-01	24.121	6.042	66.492	—	—	—	true	true	50	1	h2	8fbe8029736f720e	2026-08-01
21	Mining	21	mining	false	USA	1919-08-01	2026-08-01	23.433	-2.849	25.776	—	—	—	false	true	50	1	h2	0d3a5a62f7a04056	2026-08-01
21	Mining	21	mining	false	USA	1919-09-01	2026-08-01	25.289	7.918	52.787	—	—	—	true	true	50	1	h2	1226e3f941442278	2026-08-01
21	Mining	21	mining	false	USA	1919-10-01	2026-08-01	26.045	2.989	35.935	—	—	—	true	true	50	1	h2	5b6e332fc428b8e2	2026-08-01

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

cURL

curl "https://datazimuts.com/v1/datasets/fed_g17_industry_breadth_intel/us_ip_industry_breadth_monthly" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/fed_g17_industry_breadth_intel/us_ip_industry_breadth_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/fed_g17_industry_breadth_intel/us_ip_industry_breadth_monthly

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

Cite this snapshot

Pinned to snapshot 20260927T142341Z-89fa0b7b2cf0 and its content hash, so readers get exactly the data you used.

APA

Federal Reserve G.17 US industrial-production industry breadth intelligence. (2026). US industrial-production industry breadth intelligence (monthly) [Data set, snapshot 20260927T142341Z-89fa0b7b2cf0, sha256 89fa0b7b2cf0]. Datazimuts. Retrieved 2026-09-27, from https://datazimuts.com/en/datasets/fed_g17_industry_breadth_intel/us_ip_industry_breadth_monthly?snapshot=20260927T142341Z-89fa0b7b2cf0

BibTeX

@misc{dz_fed_g17_industry_breadth_intel_us_ip_ind_89fa0b7b,
  title        = {{US industrial-production industry breadth intelligence (monthly)}},
  author       = {{Federal Reserve G.17 US industrial-production industry breadth intelligence}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/fed_g17_industry_breadth_intel/us_ip_industry_breadth_monthly?snapshot=20260927T142341Z-89fa0b7b2cf0}},
  note         = {Snapshot 20260927T142341Z-89fa0b7b2cf0, sha256 89fa0b7b2cf01b7c7f21c59fd58fa40401811dbe268cf40fa479ba057d0b568b; accessed 2026-09-27}
}

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US industrial-production industry breadth intelligence (monthly)

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