Monthly US industrial-cycle breadth gauges from the Federal Reserve G.17 release: computed diffusion indexes over 21 industries (share expanding, 1-month/3-month/12-month), the Fed's official Table 6 diffusion indexes for cross-check, median industry momentum, headline IP and capacity-utilization context, and a documented breadth regime (broad/narrow expansion, mixed, narrow/broad contraction). From 1972-01, the first month all industries report. 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: 656
Columns: 20
Source cadence: Monthly
Last refreshed: Sep 27, 2026
Theme: economy

Column	Type	Description
month	string	Reference month, first day of month (ISO date).
country_code	string	ISO alpha-3 country code (USA for all rows).
as_of	string	Last complete month in the snapshot (pinned for idempotent re-runs).
n_industries	integer	Leaf industries in the breadth set (21).
diffusion_1m_pct	float	Share of the 21 leaf industries with ip_mom_pct > 0 (own computation). (unit: percent)
diffusion_3m_pct	float	Share of the 21 leaf industries with ip_3m_ann_pct > 0 (own computation); drives breadth_regime. (unit: percent)
diffusion_yoy_pct	float	Share of the 21 leaf industries with ip_yoy_pct > 0 (own computation). (unit: percent)
official_diffusion_1m	float	Fed's official Table 6 diffusion index, 1-month (50 = neutral). Null for the latest month: Table 6 runs one month behind Table 4 (2026-07 vs 2026-08 at collection). (unit: index)
official_diffusion_3m	float	Fed's official Table 6 diffusion index, 3-month (50 = neutral). (unit: index)
official_diffusion_6m	float	Fed's official Table 6 diffusion index, 6-month (50 = neutral). (unit: index)
median_mom_pct	float	Median ip_mom_pct across the 21 leaf industries. (unit: percent)
median_3m_ann_pct	float	Median ip_3m_ann_pct across the 21 leaf industries. (unit: percent)
median_yoy_pct	float	Median ip_yoy_pct across the 21 leaf industries. (unit: percent)
headline_ip_mom_pct	float	Month-on-month percent change of the headline total IP index. (unit: percent)
headline_ip_3m_ann_pct	float	3-month annualized percent change of the headline total IP index. (unit: percent)
headline_cu_pct	float	Headline total-industry capacity utilization rate. (unit: percent)
mfg_cu_pct	float	Manufacturing capacity utilization rate. (unit: percent)
breadth_regime	string	Industrial-breadth regime from diffusion_3m_pct: broad expansion (>=65) / narrow expansion (>=50) / mixed (>=35) / narrow contraction (>=20) / broad contraction (<20).
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.

month	country_code	as_of	n_industries	diffusion_1m_pct	diffusion_3m_pct	diffusion_yoy_pct	official_diffusion_1m	official_diffusion_3m	official_diffusion_6m	median_mom_pct	median_3m_ann_pct	median_yoy_pct	headline_ip_mom_pct	headline_ip_3m_ann_pct	headline_cu_pct	mfg_cu_pct	breadth_regime	row_hash	source_retrieved
1972-01-01	USA	2026-08-01	21	100	50	50	70.763	71.61	72.881	1.102	26.437	0.665	2.391	17.041	82.694	81.326	narrow expansion	2dcbd7924d7c0366	2026-08-01
1972-02-01	USA	2026-08-01	21	66.7	100	50	60.638	—	—	0.653	25.984	3.733	1.009	19.792	83.33	81.733	broad expansion	663e8868e1a71283	2026-08-01
1972-03-01	USA	2026-08-01	21	81	100	50	64.184	—	—	0.376	15.817	3.707	0.692	17.614	83.703	82.064	broad expansion	c97bbdd26f545527	2026-08-01
1972-04-01	USA	2026-08-01	21	76.2	90.5	100	66.667	70.213	—	1.034	6.343	3.755	1.001	11.355	84.332	82.792	broad expansion	0ee022b54eee5c49	2026-08-01
1972-05-01	USA	2026-08-01	21	57.1	76.2	50	52.482	68.085	—	0.136	6.774	2.325	0.014	7.031	84.132	82.728	broad expansion	43dde520709bd470	2026-08-01
1972-06-01	USA	2026-08-01	21	57.1	71.4	100	56.028	62.766	—	0.389	2.948	1.754	0.265	5.228	84.14	82.759	broad expansion	c934995d4b52549b	2026-08-01
1972-07-01	USA	2026-08-01	21	47.6	57.1	100	49.291	54.255	63.83	-0.024	2.399	3.05	-0.112	0.668	83.827	82.514	narrow expansion	f81e75ae9cc607fa	2026-08-01
1972-08-01	USA	2026-08-01	21	90.5	85.7	100	69.858	65.957	70.922	0.857	6.297	4.073	1.393	6.338	84.766	83.401	broad expansion	40e9d16ec6441f16	2026-08-01
1972-09-01	USA	2026-08-01	21	81	90.5	100	58.156	66.667	71.277	0.712	6.796	4.808	0.798	8.618	85.204	83.784	broad expansion	6cd289d98c326c15	2026-08-01
1972-10-01	USA	2026-08-01	21	90.5	100	100	67.731	75.177	71.986	1.52	12.464	9.098	1.27	14.755	86.036	84.787	broad expansion	15f276949e08cde9	2026-08-01

Sep 27, 2026Current
20260927T142450Z-b9bdace6e852 · sha256 b9bdace6e852…
656 rows · +0 rows vs previous
Sep 27, 2026
20260927T142208Z-b9bdace6e852 · sha256 b9bdace6e852…
656 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/fed_g17_industry_breadth_intel/us_ip_breadth_gauges_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_breadth_gauges_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_breadth_gauges_monthly

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

Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.

Cite this snapshot

Pinned to snapshot 20260927T142450Z-b9bdace6e852 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 breadth gauges (monthly) [Data set, snapshot 20260927T142450Z-b9bdace6e852, sha256 b9bdace6e852]. Datazimuts. Retrieved 2026-09-27, from https://datazimuts.com/en/datasets/fed_g17_industry_breadth_intel/us_ip_breadth_gauges_monthly?snapshot=20260927T142450Z-b9bdace6e852

BibTeX

@misc{dz_fed_g17_industry_breadth_intel_us_ip_bre_b9bdace6,
  title        = {{US industrial-production breadth gauges (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_breadth_gauges_monthly?snapshot=20260927T142450Z-b9bdace6e852}},
  note         = {Snapshot 20260927T142450Z-b9bdace6e852, sha256 b9bdace6e852656ae5ce20d6c19246dad2e546c4c0fefc8515889b7f2b02cf55; accessed 2026-09-27}
}

Embed a table or a chart

Paste this into any page. The embed is pinned to the same snapshot, follows the reader's light or dark setting, and always shows the source, license and a link back.

Type

X axis

Value

Aggregate

Theme

HTML

<iframe src="https://datazimuts.com/embed/chart?dataset=fed_g17_industry_breadth_intel%2Fus_ip_breadth_gauges_monthly&lang=en&theme=auto&snapshot=20260927T142450Z-b9bdace6e852&x=month&y=n_industries&agg=avg" title="US industrial-production breadth gauges (monthly)" width="100%" height="380" style="border:0" loading="lazy"></iframe>

Sign in to comment

Ask about this dataset. Answers come only from its catalog record, measured profile and change history, and list the facts they used.

US industrial-production breadth gauges (monthly)

Follow this dataset

Cite this snapshot

Embed a table or a chart

Use your own AI key