This paper introduces the first narrative-based dataset on fiscal consolidations for sub-Saharan Africa (SSA). Drawing on staff reports from the International Monetary Fund (IMF) during the period 1990-2024 and using an approach assisted by artificial intelligence (AI), the dataset systematically identifies fiscal consolidation actions motivated by long-term considerations (rather than cyclical conditions), such as reducing an inherited budget deficit, ensuring long-term public debt sustainability and improving economic efficiency. By focusing exclusively on measures exogenous to the business cycle, the dataset provides a more precise identification of fiscal consolidation actions for the empirical analysis of the macroeconomic effects of fiscal policy in SSA.
Abdel‐Latif et al. (Thu,) studied this question.