Global defense-spending signals

Name: Global defense-spending signals
Creator: Defense Spending Signals (derived)
License: https://www.worldbank.org/en/about/legal/terms-of-use
Keywords: defense, military-expenditure, sipri, world-bank, wdi, fiscal, geopolitics, rearmament, rankings, momentum, anomaly-detection, forecasting, signals

Annual military-expenditure burden signals from the World Bank World Development Indicators (SIPRI via WDI, 1960 ->, % of GDP, keyless): per-economy 5-year changes, 10-year OLS trend slopes, 3-sigma anomaly flags, linear-extrapolation forecasts, 10-year z-scores, per-year cross-country ranks, and high/low spender and rearmament-surge flags. The defense-burden lens — rearmament waves and drawdowns normalized across economies — not covered elsewhere in the catalog. Country labels normalized to ISO alpha-3. Raw data: World Bank WDI indicator MS.MIL.XPND.GD.ZS.

Source: Defense Spending Signals (derived)8,608 rowsUpdated: 9/22/2026License: CC-BY-4.0

defensemilitary-expendituresipriworld-bankwdifiscalgeopoliticsrearmamentrankingsmomentumanomaly-detectionforecastingsignals

Quality

96.1

Attribution

World Bank, World Development Indicators (derived signals by Frontier Data Hub)

Schema

Column	Type	Description
date	string	Reference year (World Bank WDI, annual).
country	string
country_code	string
series_id	string	World Bank WDI indicator code: MS.MIL.XPND.GD.ZS (military expenditure as a percent of GDP).
series_label	string	Indicator title as published in the World Bank World Development Indicators.
value	float	Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Expressed as a percent of GDP.
change_5y_pp	float
trend_slope_10y	float
anomaly_flag	integer
forecast_5y	float
spend_z_10y	float
rank	integer
high_spender_flag	integer
low_spender_flag	integer
surge_flag	integer

Sample rows

date	country	country_code	series_id	series_label	value	change_5y_pp	trend_slope_10y	forecast_5y	spend_z_10y	rank
1970-01-01	Afghanistan	AFG	MS.MIL.XPND.GD.ZS	Military expenditure (% of GDP)	1.62960609911055	—	—	—	—	71
1973-01-01	Afghanistan	AFG	MS.MIL.XPND.GD.ZS	Military expenditure (% of GDP)	1.86891025641026	—	—	—	—	65
1974-01-01	Afghanistan	AFG	MS.MIL.XPND.GD.ZS	Military expenditure (% of GDP)	1.61082474226804	—	—	—	—	75
1975-01-01	Afghanistan	AFG	MS.MIL.XPND.GD.ZS	Military expenditure (% of GDP)	1.72206572769953	—	—	—	—	78
1976-01-01	Afghanistan	AFG	MS.MIL.XPND.GD.ZS	Military expenditure (% of GDP)	2.04608695652174	—	0.06861171861116483	2.389145549577564	—	68

Download sample data

Download the full sample snapshot for this dataset (sample rows, not the complete dataset).

Download sample CSV Download sample JSON

Use with an LLM

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

cURL

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

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/defense_spending_signals/global_defense_spending_signals").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/defense_spending_signals/global_defense_spending_signals

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