A Narrative Fiscal Consolidation Dataset for Sub-Saharan Africa

Hany Abdel-Latif, Khalil Bechchani, Antonio David and Thibault Lemaire

No 2026/011, IMF Working Papers from International Monetary Fund

Abstract: 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.

Keywords: Fiscal consolidation; Fiscal policy; Narrative identification; Sub-Saharan Africa; Artificial intelligence methods; Imf working papers; narrative fiscal consolidation dataset; fiscal consolidation action; views of the International Monetary Fund; Hany Abdel-Latif; Fiscal stance; Africa; Middle East; Southeast Asia; North Africa (search for similar items in EconPapers)
Pages: 111
Date: 2026-01-23
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