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

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 proxy SVARs that treat the narrative indicator as an internal instrument, we estimate cumulative government spending multipliers. The median multiplier is about 0.7 at horizons up to two years, with substantial heterogeneity across countries and states. Multipliers are larger in countries that are less open to trade, under fixed exchange rate regimes, during downturns, and at the zero lower bound. Political conditions also matter: multipliers are smaller when broad economic policy uncertainty and fiscal policy-specific uncertainty is high, but weak political support is associated with larger conditional multipliers.