US regional manufacturing pulse: NY Empire State + Texas surveys

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Monthly US regional manufacturing pulse, 2001-07 onward: harmonized diffusion indexes from the NY Fed Empire State Manufacturing Survey and the Dallas Fed Texas Manufacturing Outlook Survey (keyless), one row per region x month (US-NY, US-TX). Shared indicator vocabulary — headline, new orders, shipments, unfilled orders, delivery time, inventories, prices paid/received, employment, workweek, 6-month outlook, 6-month capex plans — plus deterministic derived features: headline MoM change, trailing-12m z-score, 0-100 mfg_pulse_score (mean z of headline/orders/shipments/employment), 0-100 price_pressure_score on prices paid, expansion/contraction regime and tiers, signed streak_months, zero-crossing event flags, and 12-month new high/low flags. Raw gates (exact native layouts, gapless monthly grids, diffusion bounds, Texas index == increase minus decrease identity, known survey start months) fail loudly, never imputed. Who joins this: online shops join revenue by state and month to test whether the regional pulse leads local demand; subscription businesses use mfg_pulse_score and regime_tier as demand-regime features for churn models; sales teams weight territory outreach by streak_months and crossing event flags.

Source: US Regional Manufacturing Pulse (NY + Dallas Fed surveys, keyless)
Rows: 571
Columns: 32
Source cadence: Monthly
Last refreshed: Oct 1, 2026
Theme: economy

Column	Type	Description
date	string	Reference month, first calendar day (ISO YYYY-MM-DD); monthly join key. (unit: date)
country	string	Country name (shared normalization layer). (unit: string)
country_code	string	ISO 3166-1 alpha-3 country code (USA). (unit: string)
region_code	string	ISO 3166-2 region code: US-NY (New York) or US-TX (Texas); region join key. (unit: string)
region_name	string	Region name: New York or Texas. (unit: string)
source_survey	string	Native survey: 'Empire State Manufacturing Survey' (NY) or 'Texas Manufacturing Outlook Survey' (TX). (unit: string)
headline_idx	float	Headline diffusion index: NY general business conditions (GACDISA) / TX general business activity (bact). > 0 = expansion. Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
new_orders_idx	float	New orders diffusion index (NY NOCDISA / TX vnwo). Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
shipments_idx	float	Shipments diffusion index (NY SHCDISA / TX vshp). Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
unfilled_orders_idx	float	Unfilled orders diffusion index (NY UOCDISA / TX ufil). Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
delivery_time_idx	float	Delivery time diffusion index (NY DTCDISA / TX dtm); positive = slower deliveries. Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
inventories_idx	float	Inventories diffusion index (NY IVCDISA / TX finished-goods fgi). Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
prices_paid_idx	float	Prices paid diffusion index (NY PPCDISA / TX raw-materials prm); input-cost pressure. Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
prices_received_idx	float	Prices received diffusion index (NY PRCDISA / TX finished-goods pfg); selling-price pressure. Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
employment_idx	float	Employment diffusion index (NY NECDISA / TX nemp). Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
workweek_idx	float	Average workweek diffusion index (NY AWCDISA / TX avgwk). Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
supply_availability_idx	float	Supply availability diffusion index (NY ASCDISA only; null for Texas). Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
company_outlook_idx	float	Company outlook diffusion index (TX colk only; null for New York). Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
outlook_6m_idx	float	Expected business conditions six months ahead (NY GAFDISA / TX fbact). Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
capex_6m_idx	float	Expected capital expenditures six months ahead (NY CEFDISA / TX fcexp). Seasonally adjusted; diffusion points in [-100, 100]. (unit: diffusion points)
headline_mom_pp	float	Month-over-month change of headline_idx, in diffusion points. Null for each region's first month. (unit: diffusion points)
headline_z12m	float	Headline standardized against its own trailing 12 months (causal window incl. this month). Null for the first 11 months per region. (unit: z-score)
mfg_pulse_score	float	0-100 = clip(50 + 25 * mean(z12m of headline_idx, new_orders_idx, shipments_idx, employment_idx)). 50 = in line with the last year, ~75 = one sd above. Null for the first 11 months per region. (unit: score 0-100)
price_pressure_score	float	0-100 = clip(50 + 25 * z12m(prices_paid_idx)). Input-cost heat for margin planning. Null for the first 11 months per region. (unit: score 0-100)
regime	string	'expansion' when headline_idx > 0, else 'contraction' (diffusion > 0 = expansion by construction). (unit: string)
regime_tier	string	Magnitude tier of headline_idx: strong_expansion (>= 10), expansion ((0, 10)), contraction ((-10, 0]), strong_contraction (< -10). (unit: string)
streak_months	integer	Signed consecutive-month run of sign(headline_idx): +k = k expansion months in a row, -k = k contraction months; a zero headline breaks the run. (unit: months (signed))
crossing_up	boolean	True when headline_idx crossed zero upward this month versus last month (contraction -> expansion event). Null for each region's first month. (unit: boolean)
crossing_down	boolean	True when headline_idx crossed zero downward this month versus last month (expansion -> contraction event). Null for each region's first month. (unit: boolean)
new_high_12m	boolean	True when headline_idx is at or above its trailing-12-month maximum. Null for the first 11 months per region. (unit: boolean)
new_low_12m	boolean	True when headline_idx is at or below its trailing-12-month minimum. Null for the first 11 months per region. (unit: boolean)
row_hash	string	Deterministic 16-hex sha256 of region_code + date + rounded headline/new-orders/shipments/employment (idempotency key). (unit: string)

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

date	country	country_code	region_code	region_name	source_survey	headline_idx	new_orders_idx	shipments_idx	unfilled_orders_idx	delivery_time_idx	inventories_idx	prices_paid_idx	prices_received_idx	employment_idx	workweek_idx	supply_availability_idx	company_outlook_idx	outlook_6m_idx	capex_6m_idx	headline_mom_pp	headline_z12m	mfg_pulse_score	price_pressure_score	regime	regime_tier	streak_months	crossing_up	crossing_down	new_high_12m	new_low_12m	row_hash
2001-07-01	United States	USA	US-NY	New York	Empire State Manufacturing Survey	-13.3	7	-9.1	-16.6	-15.9	-28.8	-10.6	-20.1	-15.1	-14.1	—	—	46.5	4.2	—	—	—	—	contraction	strong_contraction	-1	—	—	—	—	89ff9b545e7867f1
2001-08-01	United States	USA	US-NY	New York	Empire State Manufacturing Survey	-8	-0.8	-7.6	-9.8	-19.6	-28.3	-1.1	-19	-26.5	-5.8	—	—	49.1	3.8	5.3	—	—	—	contraction	contraction	-2	false	false	—	—	55da55a59e6f85b0
2001-09-01	United States	USA	US-NY	New York	Empire State Manufacturing Survey	-12.5	25	-10.9	36.5	-1.8	40	-7.8	-0.6	-16.6	-15.1	—	—	-11.9	10.3	-4.5	—	—	—	contraction	strong_contraction	-3	false	false	—	—	42533b4770ead1c1
2001-10-01	United States	USA	US-NY	New York	Empire State Manufacturing Survey	-14.7	-18.2	-8.6	-25.2	-14.8	-23.9	-6.1	-25.4	-24	-12.9	—	—	37	14.6	-2.2	—	—	—	contraction	strong_contraction	-4	false	false	—	—	3eb6ef137c977df0
2001-11-01	United States	USA	US-NY	New York	Empire State Manufacturing Survey	-21.5	-7	-3.8	-26.9	-26.9	-22.6	-6.8	-26.9	-32.5	-12	—	—	34.2	11.2	-6.8	—	—	—	contraction	strong_contraction	-5	false	false	—	—	bf4bee2acf6e6a42
2001-12-01	United States	USA	US-NY	New York	Empire State Manufacturing Survey	-9.8	-4.3	-2	-23.7	-19.1	-19.8	-6.2	-25.7	-26.5	-10.5	—	—	49.1	11.5	11.7	—	—	—	contraction	contraction	-6	false	false	—	—	8fa8a635adf54932
2002-01-01	United States	USA	US-NY	New York	Empire State Manufacturing Survey	1.4	6.4	5.2	-15.5	-15.5	-20.6	-1.4	-16.2	-21.6	-9.5	—	—	42.3	10.3	11.2	—	—	—	expansion	expansion	1	true	false	—	—	bf50b79eb5f0ff8a
2002-02-01	United States	USA	US-NY	New York	Empire State Manufacturing Survey	13.3	9.3	13.2	1	-12	-11	-3.9	-10.2	-13.3	-9.2	—	—	60.6	22	11.9	—	—	—	expansion	strong_expansion	2	false	false	—	—	3af9b89e44510f51
2002-03-01	United States	USA	US-NY	New York	Empire State Manufacturing Survey	13.8	19.5	13.7	2.8	-6.4	0	-1.8	-8.3	-1.7	3.6	—	—	66.7	30.3	0.5	—	—	—	expansion	strong_expansion	3	false	false	—	—	3075c145f93b64a1
2002-04-01	United States	USA	US-NY	New York	Empire State Manufacturing Survey	20.2	21.8	24.1	-3	-6	-1	4.2	-10.3	1.1	22	—	—	69.5	22	6.4	—	—	—	expansion	strong_expansion	4	false	false	—	—	665becbda7ad34d3

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

cURL

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

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/regional_fed_manufacturing_intel/us_regional_manufacturing_pulse_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/regional_fed_manufacturing_intel/us_regional_manufacturing_pulse_monthly

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

Cite this snapshot

Pinned to snapshot 20261001T000656Z-0c644c883b22 and its content hash, so readers get exactly the data you used.

APA

US Regional Manufacturing Pulse (NY + Dallas Fed surveys, keyless). (2026). US regional manufacturing pulse: NY Empire State + Texas surveys [Data set, snapshot 20261001T000656Z-0c644c883b22, sha256 0c644c883b22]. Datazimuts. Retrieved 2026-10-01, from https://datazimuts.com/en/datasets/regional_fed_manufacturing_intel/us_regional_manufacturing_pulse_monthly?snapshot=20261001T000656Z-0c644c883b22

BibTeX

@misc{dz_regional_fed_manufacturing_intel_us_regi_0c644c88,
  title        = {{US regional manufacturing pulse: NY Empire State + Texas surveys}},
  author       = {{US Regional Manufacturing Pulse (NY + Dallas Fed surveys, keyless)}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/regional_fed_manufacturing_intel/us_regional_manufacturing_pulse_monthly?snapshot=20261001T000656Z-0c644c883b22}},
  note         = {Snapshot 20261001T000656Z-0c644c883b22, sha256 0c644c883b22496372e7412ffc79fd737b521f7a997ac447e56763d55b98fadf; accessed 2026-10-01}
}

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US regional manufacturing pulse: NY Empire State + Texas surveys

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