AI Meets Fiscal Policy: Mapping Government Spending Actions Across 62 Countries

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

No 21272, CEPR Discussion Papers from Centre for Economic Policy Research

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 unbalanced panel covers 62 countries from 1952–2023 and identifies exogenous spending shocks. We validate the measure by replicating expert narrative coding in Romer and Romer (2019), showing that the actions predict subsequent movements in measured government spending, and documenting close alignment with action-based consolidation series in Adler et al. (2024). Using country-specific 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 (search for similar items in EconPapers)
JEL-codes: E32 (search for similar items in EconPapers)
Date: 2026-03
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