The Global Fiscal Actions (GFA) Dataset

Hites Ahir, Miyoko Asai, Matthieu Bellon, Maria Chiara Cavalleri, Shuvam Das, Davide Furceri, Faizaan Kisat, Aiko Mineshima, Irene Rausell Moreno, Vishal Parmar, Nikhil Patel and Adrian Peralta-Alva

No 2026/221, IMF Working Papers from International Monetary Fund

Abstract: We introduce the Global Fiscal Actions (GFA) Dataset, the first quarterly global narrative database of discretionary fiscal policy actions, covering 142 countries since 1990. GFA combines Economist Intelligence Unit country reports with large language models to systematically identify and classify tax and spending measures, together with their motivations, timing, implementation status, and supporting evidence. We validate the dataset against economists' assessments, existing narrative datasets, fiscal aggregates, and repeated AI extractions, demonstrating high accuracy and reproducibility. We illustrate its value through applications to fiscal cyclicality and country-specific fiscal multipliers, providing new evidence on the conduct and effectiveness of fiscal policy worldwide.

Keywords: Artificial intelligence; Large language models; Fiscal policy; Fiscal cyclicality; Fiscal multipliers; Cross-country analysis (search for similar items in EconPapers)
Pages: 134
Date: 2026-10-23
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