Global R&D expenditure signals (innovation intensity)

Name: Global R&D expenditure signals (innovation intensity)
Creator: Global R&D Expenditure Signals (derived)
License: https://www.worldbank.org/en/about/legal/terms-of-use
Keywords: rd, innovation, research, technology, competitiveness, productivity, unesco, world-bank, momentum, anomaly-detection, forecasting, rank, signals

Annual global R&D-intensity signals from the World Bank World Development Indicators (GB.XPD.RSDV.GD.ZS, ~193 economies, 1996 -> 2024): 5-year changes, OLS trend slopes, 3-sigma anomaly flags, 5-year extrapolation forecasts, 10-year intensity z-scores, per-year intensity ranks, and innovation-leader (>=3%), laggard (<0.5%), and catch-up gauges. The innovation-input lens — R&D spending as % of GDP is the standard proxy for an economy's investment in future productivity. Country codes validated via the shared normalization layer. Raw indicator: UNESCO Institute for Statistics via the World Bank (keyless API).

Source: Global R&D Expenditure Signals (derived)2,690 rowsUpdated: 9/22/2026License: CC-BY-4.0

rdinnovationresearchtechnologycompetitivenessproductivityunescoworld-bankmomentumanomaly-detectionforecastingranksignals

Quality

94.7

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	Economy name as published by the World Bank.
country_code	string	ISO 3166-1 alpha-3 code for the economy.
series_id	string	World Bank WDI indicator code: GB.XPD.RSDV.GD.ZS (research and development expenditure as a percent of GDP).
series_label	string	Indicator title as published in the World Bank World Development Indicators.
value	float	Expenditures for research and development are current and capital expenditures (both public and private) on creative work undertaken systematically to increase knowledge, including knowledge of humanity, culture, and society, and the use of knowledge for new applications. R&D covers basic research, applied research, and experimental development (UNESCO Institute for Statistics, via World Bank WDI). (unit: percent of GDP)
change_5y_pp	float
trend_slope_10y	float
anomaly_flag	integer
forecast_5y	float
intensity_z_10y	float
rank	integer
high_rd_flag	integer
laggard_flag	integer
catchup_flag	integer

Sample rows

date	country	country_code	series_id	series_label	value	change_5y_pp	trend_slope_10y	forecast_5y	intensity_z_10y	rank	laggard_flag
2016-01-01	Angola	AGO	GB.XPD.RSDV.GD.ZS	Research and development expenditure (% of GDP, UNESCO via World Bank WDI)	0.03229	—	—	—	—	96	1
2007-01-01	Albania	ALB	GB.XPD.RSDV.GD.ZS	Research and development expenditure (% of GDP, UNESCO via World Bank WDI)	0.08411	—	—	—	—	97	1
2008-01-01	Albania	ALB	GB.XPD.RSDV.GD.ZS	Research and development expenditure (% of GDP, UNESCO via World Bank WDI)	0.14973	—	—	—	—	88	1
2021-01-01	Albania	ALB	GB.XPD.RSDV.GD.ZS	Research and development expenditure (% of GDP, UNESCO via World Bank WDI)	0.18998	—	—	—	—	81	1
2022-01-01	Albania	ALB	GB.XPD.RSDV.GD.ZS	Research and development expenditure (% of GDP, UNESCO via World Bank WDI)	0.19997	—	—	—	—	73	1

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/rd_signals/global_rd_expenditure_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/rd_signals/global_rd_expenditure_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/rd_signals/global_rd_expenditure_signals

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