AI Meets Fiscal Policy: Mapping Government Spending Actions Across 64 Countries

Shuvam Das, Davide Furceri, Nikhil Patel and Adrian Peralta-Alva

No 2026/043, IMF Working Papers from International Monetary Fund

Abstract: We build the first global quarterly narrative database of discretionary government spending actions by applying a fixed GPT–4.1 prompt to Economist Intelligence Unit (EIU) Country Reports. The resulting series identifies exogenous spending shocks—expansions and contractions—for an unbalanced panel of 64 countries from 1952:Q1 to 2023:Q4. We validate the database by (i) replicating expert narrative coding in Romer and Romer (2019), (ii) showing that the identified shocks predict subsequent movements in measured government spending, and (iii) establishing alignment with action-based consolidation measures in Adler et al. (2024). Using country-by-country VARs that treat the narrative indicator as an internal instrument, we derive the first set of comparable cumulative government spending multipliers. The median multiplier is 0.7 at horizons up to two years, with substantial heterogeneity across countries and over time. Pooled estimates imply larger multipliers in less open economies, under fixed exchange-rate regimes, and in downturns. Multipliers are smaller when uncertainty is high and larger when political support is stronger.

Keywords: fiscal multipliers; government spending shocks; narrative identification; large language models; text-as-data; proxy SVAR; cross-country database.; government spending multiplier; IMF working papers; government spending action; mapping government spending; country report; Vector autoregression; Fiscal stance; Caribbean; Global; South America; Central America (search for similar items in EconPapers)
Pages: 89
Date: 2026-03-06
New Economics Papers: this item is included in nep-mac and nep-opm
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