Global supply-chain pressure intelligence (monthly)

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Monthly global supply-chain pressure intelligence, 1998-01 onward: the NY Fed Global Supply Chain Pressure Index (GSCPI, standard deviations from its historical mean; keyless FRBNY workbook download) with documented pressure tiers (deep_slack/slack/normal/elevated/high/extreme), a 0-100 normal-CDF pressure score, 1- and 12-month changes, trailing-36-month percentile rank and record flags, signed above/below-average streaks, and surge/easing flags. Method caveats: the workbook is an OLE2 .xls despite its .xlsx URL (magic-sniffed); any in-window gap or null fails loudly, never imputed. One row per month x World (WLD); join on year_month. Who joins this: online shops use pressure_tier/surge_flag as inventory-cost and stock-out risk features; subscription businesses use the 0-100 score as a margin-pressure covariate; sales teams time pipeline on streak_months/easing_flag.

Source: NY Fed Global Supply Chain Pressure Index — supply-chain pressure intelligence
Rows: 344
Columns: 16
Source cadence: Monthly
Last refreshed: Sep 30, 2026
Theme: economy

Column	Type	Description
month_start	date	First day of the calendar month (panel grain).
year_month	string	Calendar month as YYYY-MM (panel join key).
country	string	Canonical aggregate name (World) for every row.
country_code	string	WLD aggregate code — the GSCPI is global by construction; join on year_month (date-only join).
gscpi_std	float	Global Supply Chain Pressure Index, standard deviations from its historical mean (verbatim FRBNY print; positive = above-average pressure).
pressure_tier	string	Documented band: deep_slack (<= -1.0), slack (-1.0, -0.25], normal (-0.25, 0.25), elevated [0.25, 1.0), high [1.0, 2.0), extreme (>= 2.0).
pressure_score_0_100	float	100 * Phi(gscpi_std) with Phi the standard normal CDF: monotone 0-100 rescaling (50 = historical mean).
mom_change	float	1-month change in the index, native std-dev units (null for the first panel month).
yoy_change	float	12-month change in the index, native std-dev units (null for the first panel year).
pct_rank_36m	float	Trailing-36-month percentile rank of the reading, 0-100 (min 24 observations; null until the window fills).
streak_months	integer	Signed run length of consecutive months on the same side of zero: positive = above-average pressure streak, negative = below-average pressure streak.
surge_flag	integer	1 when gscpi_std >= 1.0 (one sigma above the mean — the historical disruption zone), else 0.
easing_flag	integer	1 when mom_change <= -0.25 (a quarter-sigma one-month drop: pressure unwinding fast), else 0.
record_high_36m_flag	integer	1 when the reading is the trailing-36-month maximum (min 24 observations).
record_low_36m_flag	integer	1 when the reading is the trailing-36-month minimum (min 24 observations).
row_hash	string	Deterministic 16-hex sha256 of source + year_month + WLD + the index value (idempotency key).

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

month_start	year_month	country	country_code	gscpi_std	pressure_tier	pressure_score_0_100	mom_change	yoy_change	pct_rank_36m	streak_months	easing_flag	row_hash
1998-01-01	1998-01	World	WLD	-1.16	deep_slack	12.301	—	—	—	-1	0	8db28703c7844184
1998-02-01	1998-02	World	WLD	-0.439	slack	33.027	0.721	—	—	-2	0	50b12332b0077957
1998-03-01	1998-03	World	WLD	-0.033	normal	48.69	0.406	—	—	-3	0	0e2ea4b043be45ce
1998-04-01	1998-04	World	WLD	-0.09	normal	46.42	-0.057	—	—	-4	0	98a29929d4fb016e
1998-05-01	1998-05	World	WLD	-0.423	slack	33.625	-0.333	—	—	-5	1	8284b11121336367
1998-06-01	1998-06	World	WLD	-0.764	slack	22.251	-0.341	—	—	-6	1	6b230e6a507130ef
1998-07-01	1998-07	World	WLD	-0.88	slack	18.956	-0.116	—	—	-7	0	2087974951c4e213
1998-08-01	1998-08	World	WLD	-0.89	slack	18.669	-0.011	—	—	-8	0	238678bc0f018b8e
1998-09-01	1998-09	World	WLD	-0.917	slack	17.958	-0.027	—	—	-9	0	fa14df457db805f7
1998-10-01	1998-10	World	WLD	-0.639	slack	26.132	0.278	—	—	-10	0	a6f79a042d10b0d6

Profiled Oct 1, 2026 from snapshot 20260930T232925Z-fa511be5d185

Measured

Completeness: 99.3%
Rows: 344
Columns: 16
Columns with gaps: 3

Rows per yearmonth_start · 1998–2026 · 29 years

19982026

Column	Missing	Distinct	Range	Distribution
month_startdate	0%	332	Jan 1, 1998 → Aug 1, 2026	—
year_monthvarchar	0%	341	—	1998-011 1998-021 1998-031
countryvarchar	0%	1	—	World344
country_codevarchar	0%	1	—	WLD344
gscpi_stddouble	0%	369	-1.59 → 4.43median -0.2281	8 outside 1st–99th percentile
pressure_tiervarchar	0%	6	—	slack149 normal88 elevated49
pressure_score_0_100double	0%	324	5.58 → 100median 40.98	8 outside 1st–99th percentile
mom_changedouble	0.29%	373	-1.48 → 1.39median 0.0365	8 outside 1st–99th percentile
yoy_changedouble	3.5%	295	-4.85 → 3.81median 0.0858	8 outside 1st–99th percentile
pct_rank_36mdouble	6.7%	48	2.78 → 100median 55.56	15 outside 1st–99th percentile
streak_monthsbigint	0%	83	-54 → 41median -3	8 outside 1st–99th percentile
surge_flagbigint	0%	2	0 → 1median 0
easing_flagbigint	0%	2	0 → 1median 0
record_high_36m_flagbigint	0%	2	0 → 1median 0
record_low_36m_flagbigint	0%	2	0 → 1median 0
row_hashvarchar	0%	389	—	0044cde82949e5fa1 017fb82535276d901 01c097552b988fcb1

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

cURL

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

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/nyfed_gscpi_supply_pressure_intel/global_supply_chain_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/nyfed_gscpi_supply_pressure_intel/global_supply_chain_pressure_monthly

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

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Pinned to snapshot 20260930T232925Z-fa511be5d185 and its content hash, so readers get exactly the data you used.

APA

NY Fed Global Supply Chain Pressure Index — supply-chain pressure intelligence. (2026). Global supply-chain pressure intelligence (monthly) [Data set, snapshot 20260930T232925Z-fa511be5d185, sha256 fa511be5d185]. Datazimuts. Retrieved 2026-10-01, from https://datazimuts.com/en/datasets/nyfed_gscpi_supply_pressure_intel/global_supply_chain_pressure_monthly?snapshot=20260930T232925Z-fa511be5d185

BibTeX

@misc{dz_nyfed_gscpi_supply_pressure_intel_global_fa511be5,
  title        = {{Global supply-chain pressure intelligence (monthly)}},
  author       = {{NY Fed Global Supply Chain Pressure Index — supply-chain pressure intelligence}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/nyfed_gscpi_supply_pressure_intel/global_supply_chain_pressure_monthly?snapshot=20260930T232925Z-fa511be5d185}},
  note         = {Snapshot 20260930T232925Z-fa511be5d185, sha256 fa511be5d185f0cb7f42087d974992b324905a018680d5c6e2d274b52f6e0ea4; accessed 2026-10-01}
}

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Global supply-chain pressure intelligence (monthly)

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