A daily measure of geopolitical risk constructed using large language models, covering 1960 to present.
Last updated: March 31, 2026 with data through March 31, 2026.
The AI-GPR Index measures geopolitical risk by reading newspaper articles with artificial intelligence. Building on the original GPR Index (Caldara and Iacoviello, 2022), it replaces keyword matching with semantic understanding: rather than searching for specific word combinations, the AI reads each article and assigns a risk score based on its geopolitical content.
The index is constructed at daily frequency using articles from the New York Times, Washington Post, and Chicago Tribune, spanning 1960 through 2026.
The main index. Aggregates LLM-assigned geopolitical risk scores across all newspaper articles on each date, normalized by total newspaper article count. Captures the overall level of geopolitical risk as perceived in the news.
A keyword-based GPR index constructed using the Caldara and Iacoviello (2022) proximity search methodology on the same three newspapers. Counts articles matching specific combinations of geopolitical and risk-related terms. Extended back to 1960.
A sub-index identifying articles that discuss oil or energy supply disruptions driven by geopolitical events. Constructed using a second LLM classification layer applied to high-GPR articles containing oil-related keywords.
Articles matching a broad keyword filter are scored by GPT-4o-mini on a 0–1 scale reflecting the intensity of geopolitical risk content. All indices are normalized to a mean of 100 over 1985–2019. See the paper for full details.
Geopolitical risk decomposed by country — track the risk footprint of individual nations as initiator, respondent, or spillover across the full 1960–2026 sample.
Explore Country Index →Breaks down geopolitical risk by category — military conflict, terrorism, sanctions, coups, nuclear threats, and more — revealing which types of events drive aggregate risk at each moment in history.
Explore Event Types →Directed country-pair indices capturing which nation initiates and which responds — select any initiator–respondent pair from an interactive matrix spanning 1960 to 2026.
Explore Bilateral Pairs →Use the range slider below the chart to zoom into specific periods. Hover over the chart for exact values. Double-click to reset zoom.
Note: The AI-GPR Country Index uses machine learning to identify specific countries and roles (Initiator, Respondent, Spillover) within news text associated with geopolitical risk. Smoothing reflects a trailing moving average. Country-level data are available through March 2026. Scaling uses the same monthly factors as the overall AI-GPR index (mean=100, 1985-2019), so that values are comparable to the aggregate index.
Note: Each series sums the AI-GPR sentiment score for articles classified under that Monthly GPR by event type.
Note: The bilateral index measures geopolitical risk in articles where both countries appear together as initiator and respondent (in either direction). Top pairs are ranked by total cumulative sentiment across the full sample. Scaled using the same monthly factors of the overall AI-GPR index (mean = 100, 1985–2019), so that values are comparable across pairs and to the aggregate index. Bilateral data are available through March 2026.
| File | Description | Download |
|---|---|---|
| ai_gpr_data_daily.csv | Daily AI-GPR index, 1960–present | ↓ CSV |
| ai_gpr_data_monthly.csv | Monthly AI-GPR index and components, 1960–present | ↓ CSV |
| AI_GPR_PAPER.pdf | Working paper describing the methodology |
| File | Description | Download |
|---|---|---|
| ai_gpr_country_monthly.csv | Monthly GPR by country (initiator / respondent / all roles) | ↓ CSV |
| ai_gpr_eventtype_monthly.csv | Monthly GPR by event type | ↓ CSV |
| ai_gpr_bilateral_monthly.csv | Monthly bilateral GPR for top country pairs (directed: initiator → respondent) | ↓ CSV |
If you use the AI-GPR Index, please let us know, and cite:
Iacoviello, Matteo and Jonathan Tong (2026). “The AI-GPR Index: Measuring Geopolitical Risk using Artificial Intelligence.” Working Paper, Federal Reserve Board of Governors.
Contact: matteo.iacoviello@frb.gov · jtong45@wisc.edu · www.matteoiacoviello.com