US producer-price industry intelligence (monthly)
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Monthly US producer-price intelligence from the BLS Producer Price Index via the keyless BLS Public Data API v2: the all-commodities headline plus net-output price indexes for 14 NAICS industries (11 manufacturing, truck transportation, utilities, hospitals), 2017-01 onward. Every series carries YoY and NSA MoM change, the 12-month YoY trend, heating / cooling / steady flags, an above-3% gauge, riser/faller and 12-month record-high flags, and a documented 0-100 price-pressure score with per-month industry ranks and p1-p4 tiers, plus month-level context (headline YoY, industry-vs-headline gap, above-3% breadth, cross-industry spread). Who joins this: an online shop joins industry YoY / pressure_score on (naics_code, month) to monthly COGS to time price changes and model margin pressure; a B2B sales team joins on (industry, month) to prioritize accounts in expanding-margin industries. Caveats: all series are not seasonally adjusted (YoY is the primary gauge); the latest month is preliminary and routinely revised. BLS material is public domain (commercial reuse allowed); source: U.S. Bureau of Labor Statistics.
- Rows
- 1,740
- Columns
- 25
- Source cadence
- Monthly
- Last refreshed
- Sep 30, 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. (unit: ISO date) |
| country_code | string | ISO alpha-3 country code (USA for every row). (unit: ISO 3166-1 alpha-3) |
| naics_code | string | NAICS industry code ('ALL' on the all-commodities headline row). Primary join key with month. (unit: code) |
| industry_code | string | Stable Datazimuts code: all_commodities, food_mfg, paper_mfg, petroleum_coal_mfg, chemical_mfg, plastics_rubber_mfg, nonmetallic_mineral_mfg, primary_metal_mfg, fabricated_metal_mfg, machinery_mfg, computer_electronic_mfg, transportation_equipment_mfg, utilities, truck_transportation, hospitals. (unit: code) |
| industry_name | string | Human-readable industry name. |
| seasonally_adjusted | boolean | Always false: every series in this dataset is published by BLS only unadjusted, so YoY is the primary gauge. (unit: boolean) |
| index_value | float | Published PPI index level, kept verbatim (rounded to 3 decimals). Index bases differ across industries: levels are not comparable across rows, percent changes are. (unit: index points) |
| yoy_pct | float | Year-over-year percent change of the index — the primary factory-gate inflation gauge. (unit: percent) |
| yoy_trend_12m | float | Trailing 12-month mean of yoy_pct (minimum 6 observations): the slow-moving trend of producer-price pressure. Gaps are never interpolated. (unit: percent) |
| yoy_vs_trend_pp | float | yoy_pct minus its 12-month trend, in percentage points. Positive = running hotter than trend. (unit: pp) |
| mom_pct | float | Month-over-month percent change. NOT seasonally adjusted — seasonal swings show up here; prefer yoy_pct. (unit: percent) |
| heating_flag | string | 'heating' when YoY runs 0.5pp or more above its 12-month trend, 'cooling' when 0.5pp or more below, 'steady' otherwise; null when YoY or trend is null. (unit: category) |
| above3_flag | boolean | True when yoy_pct exceeds 3% (broad pressure gauge). (unit: boolean) |
| riser_flag | boolean | True when NSA mom_pct >= 0.5. (unit: boolean) |
| faller_flag | boolean | True when NSA mom_pct <= -0.5. (unit: boolean) |
| record_high_12m_flag | boolean | True when index_value is the maximum over the trailing 12 months. (unit: boolean) |
| pressure_score | float | Documented 0-100 composite: yoy_pct min-maxed within each month across the 14 industries (headline excluded from the peer group). Higher = hottest factory-gate pressure that month. Null on the headline row. (unit: 0-100 score) |
| pressure_rank | integer | Per-month rank of pressure_score across the 14 industries (1 = hottest); min-ranking ties. (unit: rank) |
| pressure_tier | string | Fixed pressure tier: p1 (score >= 75, hot), p2 (>= 50), p3 (>= 25), p4 (< 25, cool). (unit: category) |
| industry_vs_headline_pp | float | Industry yoy_pct minus the all-commodities headline yoy_pct, percentage points (null on the headline row). (unit: pp) |
| breadth_above3_pct | float | Month-level context: share of the 14 industries with YoY above 3%, percent (diffusion-style breadth index). (unit: percent) |
| industry_spread_pp | float | Month-level context: max minus min YoY across the 14 industries — the cross-industry divergence gauge. (unit: pp) |
| headline_yoy_pct | float | Month-level context: YoY percent change of the all-commodities headline (WPU00000000). (unit: percent) |
| bls_series_id | string | BLS series id behind the row (e.g. PCU325---325---). |
| row_hash | string | Deterministic 16-hex row hash of series_id + month (idempotency). |
First 10 sample rows — a preview, not the complete dataset.
| month | country_code | naics_code | industry_code | industry_name | seasonally_adjusted | index_value | yoy_pct | yoy_trend_12m | yoy_vs_trend_pp | mom_pct | heating_flag | above3_flag | riser_flag | faller_flag | record_high_12m_flag | pressure_score | pressure_rank | pressure_tier | industry_vs_headline_pp | breadth_above3_pct | industry_spread_pp | headline_yoy_pct | bls_series_id | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2017-01-01 | USA | ALL | all_commodities | All commodities (headline) | false | 190.7 | — | — | — | — | — | — | — | — | false | — | — | — | — | 0 | — | — | WPU00000000 | 69a7b23d8ecb51d2 |
| 2017-01-01 | USA | 311 | food_mfg | Food manufacturing | false | 195.2 | — | — | — | — | — | — | — | — | false | — | — | — | — | 0 | — | — | PCU311---311--- | 899abd70eeeddab2 |
| 2017-01-01 | USA | 322 | paper_mfg | Paper manufacturing | false | 137.1 | — | — | — | — | — | — | — | — | false | — | — | — | — | 0 | — | — | PCU322---322--- | f47b3640c99dacbc |
| 2017-01-01 | USA | 324 | petroleum_coal_mfg | Petroleum and coal products manufacturing | false | 210.7 | — | — | — | — | — | — | — | — | false | — | — | — | — | 0 | — | — | PCU324---324--- | c11ad564ea476560 |
| 2017-01-01 | USA | 325 | chemical_mfg | Chemical manufacturing | false | 272.8 | — | — | — | — | — | — | — | — | false | — | — | — | — | 0 | — | — | PCU325---325--- | 2a0800698443dddf |
| 2017-01-01 | USA | 326 | plastics_rubber_mfg | Plastics and rubber products manufacturing | false | 183.2 | — | — | — | — | — | — | — | — | false | — | — | — | — | 0 | — | — | PCU326---326--- | d4bdeaaf7bf49e9c |
| 2017-01-01 | USA | 327 | nonmetallic_mineral_mfg | Nonmetallic mineral product manufacturing | false | 204.6 | — | — | — | — | — | — | — | — | false | — | — | — | — | 0 | — | — | PCU327---327--- | f116555412acae34 |
| 2017-01-01 | USA | 331 | primary_metal_mfg | Primary metal manufacturing | false | 183.4 | — | — | — | — | — | — | — | — | false | — | — | — | — | 0 | — | — | PCU331---331--- | b3ee88743c2e5ef7 |
| 2017-01-01 | USA | 332 | fabricated_metal_mfg | Fabricated metal product manufacturing | false | 190.2 | — | — | — | — | — | — | — | — | false | — | — | — | — | 0 | — | — | PCU332---332--- | bc5dd452c63545ae |
| 2017-01-01 | USA | 333 | machinery_mfg | Machinery manufacturing | false | 132.7 | — | — | — | — | — | — | — | — | false | — | — | — | — | 0 | — | — | PCU333---333--- | 91ce4a19b25dd419 |
Profiled Oct 1, 2026 from snapshot 20260930T152731Z-a884bfbb00b3
Measured- Completeness
- 93.9%
- Rows
- 1,740
- Columns
- 25
- Columns with gaps
- 14
20172026
| Column | Missing | Distinct | Range | Distribution |
|---|---|---|---|---|
| monthvarchar | 0% | 90 | — |
|
| country_codevarchar | 0% | 1 | — |
|
| naics_codevarchar | 0% | 13 | — |
|
| industry_codevarchar | 0% | 16 | — |
|
| industry_namevarchar | 0% | 16 | — |
|
| seasonally_adjustedboolean | 0% | 1 | — |
|
| index_valuedouble | 0% | 1,424 | 88.3 → 563.71median 201.15 | 35 outside 1st–99th percentile |
| yoy_pctdouble | 10.3% | 1,789 | -52.98 → 126.09median 2.63 | 32 outside 1st–99th percentile |
| yoy_trend_12mdouble | 14.7% | 1,869 | -23.85 → 77.08median 2.79 | 30 outside 1st–99th percentile |
| yoy_vs_trend_ppdouble | 14.7% | 1,281 | -50.82 → 122.51median 0.1144 | 30 outside 1st–99th percentile |
| mom_pctdouble | 0.86% | 1,545 | -47.5 → 103.11median 0.2082 | 36 outside 1st–99th percentile |
| heating_flagvarchar | 14.7% | 3 | — |
|
| above3_flagboolean | 10.3% | 2 | — |
|
| riser_flagboolean | 0.86% | 2 | — |
|
| faller_flagboolean | 0.86% | 2 | — |
|
| record_high_12m_flagboolean | 0% | 2 | — |
|
| pressure_scoredouble | 16.3% | 1,330 | 0 → 100median 56.05 | |
| pressure_rankbigint | 16.3% | 16 | 1 → 14median 7.5 | |
| pressure_tiervarchar | 16.3% | 4 | — |
|
| industry_vs_headline_ppdouble | 16.3% | 1,438 | -59.82 → 106.84median 0.6399 | 30 outside 1st–99th percentile |
| breadth_above3_pctdouble | 0% | 15 | 0 → 100median 39.29 | |
| industry_spread_ppdouble | 10.3% | 120 | 3.29 → 125.76median 24.8 | 45 outside 1st–99th percentile |
| headline_yoy_pctdouble | 10.3% | 122 | -9.42 → 22.69median 2.17 | 30 outside 1st–99th percentile |
| bls_series_idvarchar | 0% | 14 | — |
|
| row_hashvarchar | 0% | 1,853 | — |
|
- Current
20260930T152731Z-a884bfbb00b3 · sha256 a884bfbb00b3…
1,740 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/us_ppi_industry_intel/us_ppi_industry_intel_monthly" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/us_ppi_industry_intel/us_ppi_industry_intel_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_ppi_industry_intel/us_ppi_industry_intel_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 20260930T152731Z-a884bfbb00b3 and its content hash, so readers get exactly the data you used.
APA
U.S. Bureau of Labor Statistics. (2026). US producer-price industry intelligence (monthly) [Data set, snapshot 20260930T152731Z-a884bfbb00b3, sha256 a884bfbb00b3]. Datazimuts. Retrieved 2026-10-01, from https://datazimuts.com/en/datasets/us_ppi_industry_intel/us_ppi_industry_intel_monthly?snapshot=20260930T152731Z-a884bfbb00b3
BibTeX
@misc{dz_us_ppi_industry_intel_us_ppi_industry_in_a884bfbb,
title = {{US producer-price industry intelligence (monthly)}},
author = {{U.S. Bureau of Labor Statistics}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/en/datasets/us_ppi_industry_intel/us_ppi_industry_intel_monthly?snapshot=20260930T152731Z-a884bfbb00b3}},
note = {Snapshot 20260930T152731Z-a884bfbb00b3, sha256 a884bfbb00b3cab2c00332a533ed99a25092115b3e8e92711befb02395bc55ba; accessed 2026-10-01}
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